diff --git a/presentation/Slide Decks/.gitignore b/presentation/Slide Decks/.gitignore new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/presentation/Slide Decks/.gitignore @@ -0,0 +1 @@ + diff --git a/presentation/Slide Decks/IDETC Supplement Slides (BO block + gap + video + accel).pptx b/presentation/Slide Decks/IDETC Supplement Slides (BO block + gap + video + accel).pptx new file mode 100644 index 00000000..ae90a8bd Binary files /dev/null and b/presentation/Slide Decks/IDETC Supplement Slides (BO block + gap + video + accel).pptx differ diff --git a/presentation/Slide Decks/IDETC Tensegrity Slides Draft 1.pptx b/presentation/Slide Decks/IDETC Tensegrity Slides Draft 1.pptx new file mode 100644 index 00000000..82271229 Binary files /dev/null and b/presentation/Slide Decks/IDETC Tensegrity Slides Draft 1.pptx differ diff --git a/presentation/Slide Decks/draft1-previews/slide-01.png b/presentation/Slide Decks/draft1-previews/slide-01.png new file mode 100644 index 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differ diff --git a/presentation/Slide Decks/draft1-previews/slide-13.png b/presentation/Slide Decks/draft1-previews/slide-13.png new file mode 100644 index 00000000..2955034f Binary files /dev/null and b/presentation/Slide Decks/draft1-previews/slide-13.png differ diff --git a/presentation/Slide Decks/draft1-previews/slide-14.png b/presentation/Slide Decks/draft1-previews/slide-14.png new file mode 100644 index 00000000..1a360b40 Binary files /dev/null and b/presentation/Slide Decks/draft1-previews/slide-14.png differ diff --git a/presentation/Slide Decks/marcus-workflow-deck-previews/README.md b/presentation/Slide Decks/marcus-workflow-deck-previews/README.md new file mode 100644 index 00000000..c6236188 --- /dev/null +++ b/presentation/Slide Decks/marcus-workflow-deck-previews/README.md @@ -0,0 +1,23 @@ +# Workflow Discussion Slides — @me-madsen + +Source: https://byu.box.com/s/5k9os5cijnm2dbn0mrtngzglph6ce1do +(posted by @me-madsen in +[#41, 2026-07-28](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/41), +"I've been using this slideshow with recent meetings to show our work and process") + +7 slides, 103 MB (embedded videos), default PowerPoint theme — a working +meeting deck, not the IDETC deck. The .pptx is not committed (well over +GitHub's 100 MB limit); these are LibreOffice-rendered previews only. + +Assets in here that the IDETC deck currently lacks: + +- slide 3 — drop-tower photo and instrumented specimen on the base plate +- slide 7 — the end-to-end workflow figure (design → CAD → slice → print → + post-process → test → back to BO) +- slide 2 — TPU print-parameter timelapse +- slide 4 — portrait phone video, `prc1kn` 2026-07-22 drop 2 + +Note: slide 5 lists four objectives (transmissibility, compaction efficiency, +SEA, peak transmitted force). `bo/tensegrity_campaign.py` and the IDETC +abstract use two objectives (SEA, compaction efficiency) plus a peak-force +constraint. 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configuration (every geometry parameter at its upper +bound) and then morphs through the nine actual seed designs of the +printed campaign (S0, then Sobol specimens 1 to 8) in specimen order, +all five dials moving together for each transition. Requested in PR #84 +(me-madsen, 2026-08-20). No caption text on the slide; the dials carry +their bound values at their ends. + +Geometry, bounds, colors, and the depth-sorted renderer come from +build_search_space_figure.py so the assets stay consistent. Seed values +are the nine rows of bo/t3-prism-bo-batch.csv (commit 18c41a6, the batch +that was actually printed and drop-tested). One camera and one mm scale +cover every frame. + +Output: presentation/media/gif-designs-tour.gif (16:9, 1920x1080). +""" + +from __future__ import annotations + +import math +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +from PIL import Image + +from build_search_space_figure import ( + BOUNDS, + GUIDE, + INK, + INK2, + draw_structure, + nodes, + project, + setup_axes, +) +from build_search_space_gifs import ACCENT + +HERE = Path(__file__).resolve().parent +OUT = HERE / "media" / "gif-designs-tour.gif" + +FIG_W_IN, FIG_H_IN, DPI = 8.0, 4.5, 240 # 1920 x 1080 px +STEPS = 18 # frames for one design-to-design transition +STEP_MS = 70 # per-frame duration mid-transition +DESIGN_HOLD_MS = 950 # pause on each seed design +END_HOLD_MS = 1600 # pause at the all-max start and the last design + +DIALS = [ + ("R_mm", "triangle radius R", "{:.0f} mm"), + ("H_mm", "height H", "{:.0f} mm"), + ("twist_deg", "twist angle", "{:.0f}\N{DEGREE SIGN}"), + ("strut_d_mm", "strut diameter", "{:.1f} mm"), + ("cable_d_mm", "cable diameter", "{:.1f} mm"), +] + +# All nine campaign seed designs, in specimen order: S0 (specimen 0), +# then the eight Sobol designs (specimens 1-8). bo/t3-prism-bo-batch.csv. +SEEDS = [ + dict(R_mm=32.1266, H_mm=89.6262, twist_deg=59.7792, + strut_d_mm=7.8831, cable_d_mm=5.3902), + dict(R_mm=33.7842, H_mm=80.0836, twist_deg=77.4080, + strut_d_mm=10.8717, cable_d_mm=3.0003), + dict(R_mm=38.9665, H_mm=99.9950, twist_deg=47.6915, + strut_d_mm=7.0737, cable_d_mm=3.9241), + dict(R_mm=25.1224, H_mm=72.0587, twist_deg=65.1213, + strut_d_mm=10.1789, cable_d_mm=4.6640), + dict(R_mm=27.6114, H_mm=104.1304, twist_deg=70.4432, + strut_d_mm=9.2816, cable_d_mm=4.4922), + dict(R_mm=36.3001, H_mm=63.2297, twist_deg=52.9940, + strut_d_mm=6.4571, cable_d_mm=4.0550), + dict(R_mm=35.9821, H_mm=96.4464, twist_deg=62.1055, + strut_d_mm=11.6620, cable_d_mm=3.4949), + dict(R_mm=30.1066, H_mm=74.8199, twist_deg=44.4573, + strut_d_mm=8.5803, cable_d_mm=4.9377), + dict(R_mm=29.0207, H_mm=100.8663, twist_deg=63.7624, + strut_d_mm=6.1990, cable_d_mm=3.1969), +] + +ALL_MAX = {k: hi for k, (lo, hi) in BOUNDS.items()} + + +def eased(a, b): + """Cosine-eased dict-of-params a -> b sequence, STEPS frames.""" + out = [] + for t in range(STEPS): + f = (1 - math.cos(math.pi * t / (STEPS - 1))) / 2 + out.append({k: a[k] + (b[k] - a[k]) * f for k in a}) + return out + + +def frame_sequence(): + """(params, hold_ms) for every frame: all-max, then each seed.""" + frames = [] + waypoints = [ALL_MAX] + SEEDS + for i in range(len(waypoints) - 1): + for t, params in enumerate(eased(waypoints[i], waypoints[i + 1])): + hold = STEP_MS + if i == 0 and t == 0: + hold = END_HOLD_MS + elif t == STEPS - 1: + hold = (END_HOLD_MS if i == len(waypoints) - 2 + else DESIGN_HOLD_MS) + frames.append((params, hold)) + return frames + + +def union_bbox(frames): + """(u, v) bounding box over every frame, one camera for the whole run.""" + us, vs = [], [] + for params, _ in frames: + bot, top = nodes(params) + for pt in bot + top: + u, v, _ = project(pt) + us.append(u) + vs.append(v) + return min(us), max(us), min(vs), max(vs) + + +def draw_dial_panel(fig, params): + """Five stacked dials, all live: every transition moves all of them.""" + sx0, sx1 = 0.685, 0.945 + for j, (key, label, fmt) in enumerate(DIALS): + lo, hi = BOUNDS[key] + val = params[key] + frac = (val - lo) / (hi - lo) + y = 0.78 - j * 0.14 + fig.text(sx0, y + 0.045, label, ha="left", va="bottom", + fontsize=10, color=INK) + fig.text(sx1, y + 0.045, fmt.format(val), ha="right", va="bottom", + fontsize=10, color=ACCENT, fontweight="bold") + fig.add_artist(plt.Line2D([sx0, sx1], [y, y], color=GUIDE, + lw=2.6, solid_capstyle="round", + transform=fig.transFigure)) + fig.add_artist(plt.Line2D([sx0 + frac * (sx1 - sx0)], [y], + marker="o", markersize=9, color=ACCENT, + transform=fig.transFigure)) + # Bound values at the dial ends (lo left, hi right). + fig.text(sx0, y - 0.032, fmt.format(lo), ha="center", va="top", + fontsize=8, color=INK2) + fig.text(sx1, y - 0.032, fmt.format(hi), ha="center", va="top", + fontsize=8, color=INK2) + + +def render_frame(params, bbox): + u0, u1, v0, v1 = bbox + fig = plt.figure(figsize=(FIG_W_IN, FIG_H_IN), dpi=DPI) + fig.patch.set_facecolor("white") + + ax = fig.add_axes([0.02, 0.05, 0.62, 0.91]) + center = ((u0 + u1) / 2, (v0 + v1) / 2) + ppmm = setup_axes(ax, fig, center, (u1 - u0) + 40, (v1 - v0) + 30) + draw_structure(ax, params, ppmm) + + draw_dial_panel(fig, params) + + fig.canvas.draw() + img = Image.fromarray(np.asarray(fig.canvas.buffer_rgba())[..., :3]) + plt.close(fig) + return img + + +def main(): + frames = frame_sequence() + bbox = union_bbox(frames) + imgs = [render_frame(params, bbox) for params, _ in frames] + palette = imgs[0].quantize(colors=256) + imgs_q = [f.quantize(colors=256, palette=palette, dither=0) + for f in imgs] + durations = [hold for _, hold in frames] + OUT.parent.mkdir(parents=True, exist_ok=True) + imgs_q[0].save(OUT, save_all=True, append_images=imgs_q[1:], + duration=durations, loop=0, optimize=True) + print(f"wrote {OUT} ({OUT.stat().st_size / 1e6:.2f} MB, " + f"{len(imgs_q)} frames)") + + +if __name__ == "__main__": + main() diff --git a/presentation/build_lattice_figure.py b/presentation/build_lattice_figure.py new file mode 100644 index 00000000..c14965d2 --- /dev/null +++ b/presentation/build_lattice_figure.py @@ -0,0 +1,199 @@ +"""Build a concept image of a TPU + PLA tensegrity-inspired lattice. + +Requested in PR #84 (me-madsen, 2026-08-24): a single unlabeled figure of +what a TPU + PLA lattice might look like, in the same clean rendering +style as the T3 search-space figures. This is a concept visual (the +lattice is future work, per the review discussion on the presentation +template), so the geometry is a plausible extension of the printed T3 +prism rather than a design that exists in the campaign: + +- A single level of T3 prisms (revised per me-madsen's follow-up: the + two-layer mast version read as a mess), each shorter than the printed + mid-range specimen so the prism proportions look roughly equilateral + (height close to the base triangle's side length). +- Modules tile a 2 x 2 grid (also down from 3 x 2 in that revision). + Neighbors are joined by one short TPU cable at the bottom and top + vertex planes (the single nearest vertex pair), which is what makes it + read as a lattice of connected modules rather than an array. +- Member proportions, colors, camera, and the occlusion-safe depth + sorting all come from build_search_space_figure.py, so this image + cannot drift from the T3 assets in style. + +Output: presentation/media/fig-lattice-concept.png (16:9, unlabeled). +""" + +from __future__ import annotations + +import itertools +import math +import sys +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) + +from build_search_space_figure import C_CABLE, C_STRUT, JOINT_D + +OUT = HERE / "media" / "fig-lattice-concept.png" + +# One module. Shorter than the printed mid-range specimen so the prism +# reads roughly equilateral: base triangle side = R * sqrt(3) ~ 52 mm, +# so H ~ 55 mm keeps height and width visually comparable. +R = 30.0 # mm, triangle radius +H = 55.0 # mm, height per layer +TWIST = 60.0 # deg +STRUT_D = 8.0 # mm, rigid PLA +CABLE_D = 3.5 # mm, flexible TPU +GRID_X = 2 # columns across +GRID_Y = 2 # columns deep +LAYERS = 1 +SPACING = 84.0 # mm, column center-to-center + +# Same azimuth as the T3 assets, but a higher elevation: from the T3 +# camera the six columns stack up visually and the lattice reads as a +# tangle, so the lattice view looks down a little more steeply. +AZ = math.radians(27.0) +EL = math.radians(32.0) + + +def project(p): + """Orthographic projection of one xyz point (mm) to (u, v, depth).""" + x, y, z = p + u = -math.sin(AZ) * x + math.cos(AZ) * y + v = (-math.cos(AZ) * math.sin(EL) * x - math.sin(AZ) * math.sin(EL) * y + + math.cos(EL) * z) + d = (math.cos(AZ) * math.cos(EL) * x + math.sin(AZ) * math.cos(EL) * y + + math.sin(EL) * z) + return u, v, d + + +def prism_members(cx, cy, z0, rot_deg, twist_deg): + """Members of one prism whose bottom triangle sits at rot_deg.""" + bot, top = [], [] + for i in range(3): + ab = math.radians(90 + 120 * i + rot_deg) + at = math.radians(90 + 120 * i + rot_deg + twist_deg) + bot.append((cx + R * math.cos(ab), cy + R * math.sin(ab), z0)) + top.append((cx + R * math.cos(at), cy + R * math.sin(at), z0 + H)) + out = [] + for i in range(3): + out.append((bot[i], top[i], STRUT_D, C_STRUT)) # strut + out.append((bot[i], bot[(i + 1) % 3], CABLE_D, C_CABLE)) # bottom + out.append((top[i], top[(i + 1) % 3], CABLE_D, C_CABLE)) # top + out.append((bot[(i + 1) % 3], top[i], CABLE_D, C_CABLE)) # saddle + for p in bot + top: # joints + out.append((p, p, JOINT_D, C_STRUT)) + return out + + +def column_vertices(cx, cy): + """Vertex positions of one column, keyed by plane index 0..LAYERS.""" + planes = {} + for k in range(LAYERS + 1): + # Alternating chirality returns the triangle to rot 0 on even + # planes and leaves it at rot TWIST on odd planes. + rot = TWIST if k % 2 else 0.0 + planes[k] = [ + (cx + R * math.cos(math.radians(90 + 120 * i + rot)), + cy + R * math.sin(math.radians(90 + 120 * i + rot)), + k * H) + for i in range(3) + ] + return planes + + +def lattice_members(): + centers = {(i, j): (i * SPACING, j * SPACING) + for i, j in itertools.product(range(GRID_X), range(GRID_Y))} + out = [] + for cx, cy in centers.values(): + for k in range(LAYERS): + rot = TWIST if k % 2 else 0.0 + tw = -TWIST if k % 2 else TWIST + out.extend(prism_members(cx, cy, k * H, rot, tw)) + # Short TPU links between grid neighbors at every vertex plane: only + # the closest vertex pair(s), so the joints stay sparse and legible. + verts = {ij: column_vertices(*c) for ij, c in centers.items()} + for (i, j) in centers: + for ni, nj in ((i + 1, j), (i, j + 1)): + if (ni, nj) not in centers: + continue + for k in range(LAYERS + 1): + pairs = [(math.dist(a, b), a, b) + for a in verts[(i, j)][k] + for b in verts[(ni, nj)][k]] + d, a, b = min(pairs) # single closest pair only + out.append((a, b, CABLE_D, C_CABLE)) + return out + + +FADE = 0.35 # how much the farthest members blend toward white + + +def draw_members(ax, members, ppmm): + """Depth-sorted round-capped segments (front-surface depth), as in + build_search_space_figure.draw_structure but over an arbitrary + member list so the whole lattice occludes correctly as one body. + A single prism needs no depth cue, but with several columns behind + one another flat colors read as a tangle, so far segments fade + slightly toward the background.""" + segs = [] + for p0, p1, d_mm, color in members: + if p0 == p1: + u, v, dep = project(p0) + segs.append((dep + d_mm / 2, [u, u], [v, v], d_mm, color)) + continue + n = 32 + pts = [tuple(a + (b - a) * t / n for a, b in zip(p0, p1)) + for t in range(n + 1)] + proj = [project(p) for p in pts] + for a, b in zip(proj[:-1], proj[1:]): + segs.append(((a[2] + b[2]) / 2 + d_mm / 2, + [a[0], b[0]], [a[1], b[1]], d_mm, color)) + segs.sort(key=lambda s: s[0]) # far first + d_lo = min(s[0] for s in segs) + d_hi = max(s[0] for s in segs) + for dep, us, vs, d_mm, color in segs: + t = FADE * (d_hi - dep) / (d_hi - d_lo) + r, g, b = matplotlib.colors.to_rgb(color) + faded = (r + (1 - r) * t, g + (1 - g) * t, b + (1 - b) * t) + ax.plot(us, vs, color=faded, linewidth=d_mm * ppmm, + solid_capstyle="round", zorder=3) + + +def main(): + members = lattice_members() + us, vs = [], [] + for p0, p1, _, _ in members: + for p in (p0, p1): + u, v, _ = project(p) + us.append(u) + vs.append(v) + pad = 14.0 # mm, keeps thick round caps inside the frame + span_u = max(us) - min(us) + 2 * pad + span_v = max(vs) - min(vs) + 2 * pad + cu = (max(us) + min(us)) / 2 + cv = (max(vs) + min(vs)) / 2 + + fig = plt.figure(figsize=(13.333, 7.5), dpi=200) + fig.patch.set_facecolor("white") + ax = fig.add_axes([0.0, 0.0, 1.0, 1.0]) + box_w_in, box_h_in = fig.get_figwidth(), fig.get_figheight() + s = max(span_u / box_w_in, span_v / box_h_in) # mm per inch + ax.set_xlim(cu - s * box_w_in / 2, cu + s * box_w_in / 2) + ax.set_ylim(cv - s * box_h_in / 2, cv + s * box_h_in / 2) + ax.set_axis_off() + draw_members(ax, members, 72.0 / s) + + OUT.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(OUT, facecolor="white") + print(f"wrote {OUT} ({len(members)} members)") + + +if __name__ == "__main__": + main() diff --git a/presentation/build_references_qr.py b/presentation/build_references_qr.py new file mode 100644 index 00000000..641ce579 --- /dev/null +++ b/presentation/build_references_qr.py @@ -0,0 +1,34 @@ +"""Generate the QR code for the slide reference list. + +Regenerate after the PR merges so the code points at main instead of the +working branch: + + python presentation/build_references_qr.py --branch main +""" + +import argparse + +import qrcode +from qrcode.constants import ERROR_CORRECT_Q + +REPO = "vertical-cloud-lab/tensegrity-optimization" +DOC = "presentation/slide-references.md" +OUT = "presentation/media/qr-slide-references.png" + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--branch", default="claude/issue-83-20260715-2018") + args = parser.parse_args() + + url = f"https://github.com/{REPO}/blob/{args.branch}/{DOC}" + qr = qrcode.QRCode(error_correction=ERROR_CORRECT_Q, box_size=16, border=4) + qr.add_data(url) + qr.make(fit=True) + img = qr.make_image(fill_color="black", back_color="white") + img.save(OUT) + print(f"wrote {OUT} ({img.size[0]}x{img.size[1]} px) -> {url}") + + +if __name__ == "__main__": + main() diff --git a/presentation/build_search_space_figure.py b/presentation/build_search_space_figure.py new file mode 100644 index 00000000..9dc4ae22 --- /dev/null +++ b/presentation/build_search_space_figure.py @@ -0,0 +1,281 @@ +"""Build the T3-prism search-space figure for the IDETC slide deck. + +One annotated mid-range design calls out the five geometry parameters the +optimizer changes, and four designs from the actual Sobol seed batch are +drawn to a common scale to show how different the same five numbers can +look. Requested in PR #84 (sgbaird, 2026-08-19): "a figure that shows a +few different structures and calls out the parameters that we're changing +in terms of geometry, in other words, a representation of the full search +space". + +Sources of truth: +- Geometry + connectivity: cad/t3-prism/t3-prism.scad (branch + claude/issue-95-20260806-0306). B_i at angle 90 + 120*i on a circle of + radius R at z=0; T_i at 90 + 120*i + twist at z=H. Strut i: B_i -> T_i; + bottom cable i: B_i -> B_{i+1}; top cable i: T_i -> T_{i+1}; + saddle cable i: B_{i+1} -> T_i. Joint spheres (d = 7 mm) at all six + vertices. +- Bounds: bo/t3_prism_sobol_batch.py PARAMETERS (commit 65d0d3f, the + script that generated the printed campaign batch): R 25-40 mm, + H 60-110 mm, twist 40-80 deg, strut_d 6-12 mm, cable_d 3-5.5 mm. +- Seed designs: bo/t3-prism-bo-batch.csv (commit 18c41a6, PR #35 branch), + specimens 1, 2, 3, 5 chosen for spread across the bounds. + +Output: presentation/media/fig-search-space.png (16:9, for a full slide). +""" + +from __future__ import annotations + +import math +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np + +HERE = Path(__file__).resolve().parent +OUT = HERE / "media" / "fig-search-space.png" + +# Printed-campaign bounds (bo/t3_prism_sobol_batch.py PARAMETERS). +BOUNDS = { + "R_mm": (25.0, 40.0), + "H_mm": (60.0, 110.0), + "twist_deg": (40.0, 80.0), + "strut_d_mm": (6.0, 12.0), + "cable_d_mm": (3.0, 5.5), +} +JOINT_D = 7.0 # mm, fixed across the campaign + +# Annotated design: the middle of every bound. +MID = {k: (lo + hi) / 2.0 for k, (lo, hi) in BOUNDS.items()} + +# Four of the nine Sobol seed designs (bo/t3-prism-bo-batch.csv rows 1,2,3,5). +SEEDS = [ + ("seed 2", dict(R_mm=38.9665, H_mm=99.9950, twist_deg=47.6915, + strut_d_mm=7.0737, cable_d_mm=3.9241)), + ("seed 1", dict(R_mm=33.7842, H_mm=80.0836, twist_deg=77.4080, + strut_d_mm=10.8717, cable_d_mm=3.0003)), + ("seed 3", dict(R_mm=25.1224, H_mm=72.0587, twist_deg=65.1213, + strut_d_mm=10.1789, cable_d_mm=4.6640)), + ("seed 5", dict(R_mm=36.3001, H_mm=63.2297, twist_deg=52.9940, + strut_d_mm=6.4571, cable_d_mm=4.0550)), +] + +# Entity colors: printed black PLA struts, orange TPU cables (EMC accent). +C_STRUT = "#2b2b2b" +C_CABLE = "#e97132" +INK = "#333333" +INK2 = "#595959" +GUIDE = "#8c8c8c" + +AZ = math.radians(27.0) # view azimuth +EL = math.radians(16.0) # view elevation + + +def project(p): + """Orthographic projection of one xyz point (mm) to (u, v, depth).""" + x, y, z = p + u = -math.sin(AZ) * x + math.cos(AZ) * y + v = (-math.cos(AZ) * math.sin(EL) * x - math.sin(AZ) * math.sin(EL) * y + + math.cos(EL) * z) + d = (math.cos(AZ) * math.cos(EL) * x + math.sin(AZ) * math.cos(EL) * y + + math.sin(EL) * z) + return u, v, d + + +def nodes(params): + """Bottom and top vertices per the t3-prism.scad equations.""" + R, H, tw = params["R_mm"], params["H_mm"], params["twist_deg"] + bot, top = [], [] + for i in range(3): + ab = math.radians(90 + 120 * i) + at = math.radians(90 + 120 * i + tw) + bot.append((R * math.cos(ab), R * math.sin(ab), 0.0)) + top.append((R * math.cos(at), R * math.sin(at), H)) + return bot, top + + +def members(params): + """(p0, p1, diameter_mm, color) for every member and joint.""" + bot, top = nodes(params) + sd, cd = params["strut_d_mm"], params["cable_d_mm"] + out = [] + for i in range(3): + out.append((bot[i], top[i], sd, C_STRUT)) # strut + out.append((bot[i], bot[(i + 1) % 3], cd, C_CABLE)) # bottom + out.append((top[i], top[(i + 1) % 3], cd, C_CABLE)) # top + out.append((bot[(i + 1) % 3], top[i], cd, C_CABLE)) # saddle + for p in bot + top: # joints + out.append((p, p, JOINT_D, C_STRUT)) + return out + + +def draw_structure(ax, params, ppmm): + """Depth-sorted round-capped segments; ppmm = display points per mm. + + Segments sort by the depth of their front *surface* (centerline depth + plus radius), not the centerline alone. With centerline sorting, a + thin cable whose centerline sits marginally in front of a thick strut + was drawn over it even though the strut's surface bulges past the + cable, which read as the orange tendons slipping through the black + struts (PR #84, me-madsen, 2026-08-20). + """ + segs = [] + for p0, p1, d_mm, color in members(params): + if p0 == p1: # joint sphere: zero-length round-capped line + u, v, dep = project(p0) + segs.append((dep + d_mm / 2, [u, u], [v, v], d_mm, color)) + continue + n = 48 + pts = [tuple(a + (b - a) * t / n for a, b in zip(p0, p1)) + for t in range(n + 1)] + proj = [project(p) for p in pts] + for a, b in zip(proj[:-1], proj[1:]): + segs.append(((a[2] + b[2]) / 2 + d_mm / 2, + [a[0], b[0]], [a[1], b[1]], d_mm, color)) + segs.sort(key=lambda s: s[0]) # far first + for _, us, vs, d_mm, color in segs: + ax.plot(us, vs, color=color, linewidth=d_mm * ppmm, + solid_capstyle="round", zorder=3) + + +def setup_axes(ax, fig, center_uv, span_mm_w, span_mm_h): + """Fix limits so both axes share one mm scale; return points-per-mm.""" + pos = ax.get_position() + box_w_in = pos.width * fig.get_figwidth() + box_h_in = pos.height * fig.get_figheight() + s = max(span_mm_w / box_w_in, span_mm_h / box_h_in) # mm per inch + cu, cv = center_uv + ax.set_xlim(cu - s * box_w_in / 2, cu + s * box_w_in / 2) + ax.set_ylim(cv - s * box_h_in / 2, cv + s * box_h_in / 2) + ax.set_axis_off() + return 72.0 / s + + +def struct_center(params): + us, vs = [], [] + bot, top = nodes(params) + for p in bot + top: + u, v, _ = project(p) + us.append(u) + vs.append(v) + return (min(us) + max(us)) / 2, (min(vs) + max(vs)) / 2 + + +def annotate_left(ax, params, cu, cv): + """Callouts anchored in the margins around the structure at (cu, cv).""" + R, H, tw = params["R_mm"], params["H_mm"], params["twist_deg"] + bot, top = nodes(params) + + def uv(p): + u, v, _ = project(p) + return u, v + + leader = dict(arrowstyle="-", color=GUIDE, lw=1.0, shrinkA=2, shrinkB=4) + + # Circumscribed circle of the bottom triangle, dashed. + ang = np.linspace(0, 2 * math.pi, 120) + circ = [uv((R * math.cos(a), R * math.sin(a), 0.0)) for a in ang] + ax.plot([c[0] for c in circ], [c[1] for c in circ], ls=(0, (4, 3)), + lw=1.1, color=GUIDE, zorder=2) + + # Radius line from center to bottom vertex 2 (front right). + c0 = uv((0, 0, 0)) + b2 = uv(bot[2]) + ax.plot([c0[0], b2[0]], [c0[1], b2[1]], ls=(0, (4, 3)), lw=1.1, + color=GUIDE, zorder=2) + mid_r = ((c0[0] + b2[0]) / 2, (c0[1] + b2[1]) / 2) + ax.annotate("triangle radius R\n25 to 40 mm", xy=mid_r, + xytext=(cu + 52, cv - 72), fontsize=11.5, + color=INK, ha="left", va="top", arrowprops=leader) + + # Height dimension line, in clear space left of the structure. + u_dim = cu - 60 + v_lo, v_hi = uv((0, 0, 0))[1], uv((0, 0, H))[1] + ax.annotate("", xy=(u_dim, v_hi), xytext=(u_dim, v_lo), + arrowprops=dict(arrowstyle="<->", color=GUIDE, lw=1.1)) + for v in (v_lo, v_hi): # extension ticks + ax.plot([u_dim - 4, u_dim + 4], [v, v], lw=1.0, color=GUIDE) + ax.annotate("height H\n60 to 110 mm", + xy=(u_dim - 6, (v_lo + v_hi) / 2), fontsize=11.5, + color=INK, ha="right", va="center") + + # Twist arc above the top plane, outside the structure so it stays + # visible: from the untwisted vertex-0 angle to the T_0 angle. + a0, a1 = math.radians(90), math.radians(90 + tw) + Ra = R + 12 + for a in (a0, a1): # dotted radius spokes from the top-plane center + sp = uv((Ra * math.cos(a), Ra * math.sin(a), H)) + ct = uv((0, 0, H)) + ax.plot([ct[0], sp[0]], [ct[1], sp[1]], ls=(0, (1.5, 2.5)), + lw=1.0, color=GUIDE, zorder=4) + arc = [uv((Ra * math.cos(a), Ra * math.sin(a), H)) + for a in np.linspace(a0, a1, 40)] + ax.plot([c[0] for c in arc[:-2]], [c[1] for c in arc[:-2]], + ls=(0, (4, 3)), lw=1.1, color=GUIDE, zorder=4) + ax.annotate("", xy=arc[-1], xytext=arc[-4], + arrowprops=dict(arrowstyle="->", color=GUIDE, lw=1.1), + zorder=4) + ax.annotate("twist angle\n40 to 80\N{DEGREE SIGN}", + xy=arc[-6], + xytext=(cu + 52, cv + 76), fontsize=11.5, + color=INK, ha="center", va="top", arrowprops=leader) + + # Strut diameter: leader from the right margin to strut 2, mid-height. + sp = uv(tuple(a + (b - a) * 0.55 for a, b in zip(bot[2], top[2]))) + ax.annotate("strut diameter\n6 to 12 mm\nrigid PLA", xy=sp, + xytext=(cu + 58, cv + 8), fontsize=11.5, color=INK, + ha="left", va="center", arrowprops=leader) + + # Cable diameter: leader from the lower-left margin to bottom cable 0->1. + cm = uv(tuple((a + b) / 2 for a, b in zip(bot[0], bot[1]))) + ax.annotate("cable diameter\n3 to 5.5 mm\nflexible TPU", xy=cm, + xytext=(cu - 56, cv - 62), fontsize=11.5, color=INK, + ha="right", va="top", arrowprops=leader) + + +def main(): + fig = plt.figure(figsize=(13.333, 7.5), dpi=200) + fig.patch.set_facecolor("white") + + # Left: annotated mid-range design (extra margin for callouts). + ax_l = fig.add_axes([0.015, 0.09, 0.40, 0.84]) + cu, cv = struct_center(MID) + ppmm = setup_axes(ax_l, fig, (cu, cv), 235, 178) + draw_structure(ax_l, MID, ppmm) + annotate_left(ax_l, MID, cu, cv) + fig.text(0.215, 0.955, "Five geometry numbers define a design", + ha="center", fontsize=14.5, color=INK, fontweight="bold") + + # Right: four Sobol seed designs on one common mm scale. + fig.text(0.71, 0.955, "Four of the nine starting designs, to scale", + ha="center", fontsize=14.5, color=INK, fontweight="bold") + grid = [(0.435, 0.575), (0.715, 0.575), (0.435, 0.155), (0.715, 0.155)] + for (x0, y0), (name, p) in zip(grid, SEEDS): + ax = fig.add_axes([x0, y0, 0.27, 0.355]) + cu, cv = struct_center(p) + ppmm_i = setup_axes(ax, fig, (cu, cv), 150, 126) + draw_structure(ax, p, ppmm_i) + ax.text(0.5, -0.03, + f"R {p['R_mm']:.0f} mm H {p['H_mm']:.0f} mm " + f"twist {p['twist_deg']:.0f}\N{DEGREE SIGN}\n" + f"strut \N{DIAMETER SIGN} {p['strut_d_mm']:.1f} mm " + f"cable \N{DIAMETER SIGN} {p['cable_d_mm']:.1f} mm", + transform=ax.transAxes, ha="center", va="top", + fontsize=10.5, color=INK2, linespacing=1.4) + + fig.text(0.5, 0.018, + "Bounds from the campaign batch generator " + "(bo/t3_prism_sobol_batch.py). Material pairing, joint size, " + "and vertical build orientation stay fixed.", + ha="center", fontsize=9.5, color=INK2, style="italic") + + OUT.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(OUT, facecolor="white") + print(f"wrote {OUT}") + + +if __name__ == "__main__": + main() diff --git a/presentation/build_search_space_gifs.py b/presentation/build_search_space_gifs.py new file mode 100644 index 00000000..839b28d5 --- /dev/null +++ b/presentation/build_search_space_gifs.py @@ -0,0 +1,211 @@ +"""Build one GIF per geometry parameter for the IDETC slide deck. + +Each GIF sweeps a single parameter across its campaign bounds while the +other four stay at the middle of their bounds, so the audience sees what +each number does to the printed structure. Follow-up to the static +search-space figure, requested in PR #84 (sgbaird, 2026-08-20): "a series +of GIF animations visually showing changes to a specific parameter". + +Geometry, bounds, colors, and the depth-sorted renderer are imported from +build_search_space_figure.py so the two figures cannot drift apart. All +five GIFs share one camera and one mm scale (sized to the largest design +any sweep can produce), so they are comparable side by side and the +ground plane stays put while a structure grows. + +Output: presentation/media/gif-param-{radius,height,twist,strut,cable}.gif +(16:9, 960x540, ~4 s ping-pong loop with holds at the bounds). +""" + +from __future__ import annotations + +import math +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +from PIL import Image + +from build_search_space_figure import ( + BOUNDS, + C_CABLE, + C_STRUT, + GUIDE, + INK, + INK2, + MID, + draw_structure, + nodes, + project, + setup_axes, +) + +HERE = Path(__file__).resolve().parent +OUT_DIR = HERE / "media" + +FIG_W_IN, FIG_H_IN, DPI = 8.0, 4.5, 120 # 960 x 540 px +STEPS = 26 # frames for one lo -> hi traverse +HOLD_MS = 900 # pause at each bound +STEP_MS = 70 # per-frame duration mid-sweep +ACCENT = "#156082" # EMC accent blue for the value readout and slider + +SWEEPS = [ + ("radius", "R_mm", "triangle radius R", "", "{:.0f} mm"), + ("height", "H_mm", "height H", "", "{:.0f} mm"), + ("twist", "twist_deg", "twist angle", "", "{:.0f}\N{DEGREE SIGN}"), + ("strut", "strut_d_mm", "strut diameter", "rigid PLA", "{:.1f} mm"), + ("cable", "cable_d_mm", "cable diameter", "flexible TPU", "{:.1f} mm"), +] + + +def sweep_values(key): + """Cosine-eased lo -> hi -> lo value sequence for one parameter.""" + lo, hi = BOUNDS[key] + up = [lo + (hi - lo) * (1 - math.cos(math.pi * t / (STEPS - 1))) / 2 + for t in range(STEPS)] + return up + up[-2:0:-1] # ping-pong without repeating the endpoints + + +def frame_params(key, value): + p = dict(MID) + p[key] = value + return p + + +def union_bbox(): + """(u, v) bounding box over every frame of every sweep, one camera.""" + us, vs = [], [] + for _, key, _, _, _ in SWEEPS: + for val in sweep_values(key): + bot, top = nodes(frame_params(key, val)) + for pt in bot + top: + u, v, _ = project(pt) + us.append(u) + vs.append(v) + return min(us), max(us), min(vs), max(vs) + + +def annotate_sweep(ax, key, params): + """Dashed guide showing which feature the swept parameter controls.""" + R, H, tw = params["R_mm"], params["H_mm"], params["twist_deg"] + bot, top = nodes(params) + + def uv(p): + u, v, _ = project(p) + return u, v + + if key == "R_mm": + ang = np.linspace(0, 2 * math.pi, 120) + circ = [uv((R * math.cos(a), R * math.sin(a), 0.0)) for a in ang] + ax.plot([c[0] for c in circ], [c[1] for c in circ], + ls=(0, (4, 3)), lw=1.2, color=GUIDE, zorder=2) + c0, b2 = uv((0, 0, 0)), uv(bot[2]) + ax.plot([c0[0], b2[0]], [c0[1], b2[1]], ls=(0, (4, 3)), lw=1.2, + color=GUIDE, zorder=2) + elif key == "H_mm": + u_dim = uv((0, 0, 0))[0] - 62 + v_lo, v_hi = uv((0, 0, 0))[1], uv((0, 0, H))[1] + ax.annotate("", xy=(u_dim, v_hi), xytext=(u_dim, v_lo), + arrowprops=dict(arrowstyle="<->", color=GUIDE, lw=1.2)) + for v in (v_lo, v_hi): + ax.plot([u_dim - 4, u_dim + 4], [v, v], lw=1.1, color=GUIDE) + elif key == "twist_deg": + a0, a1 = math.radians(90), math.radians(90 + tw) + Ra = R + 12 + ct = uv((0, 0, H)) + for a in (a0, a1): + sp = uv((Ra * math.cos(a), Ra * math.sin(a), H)) + ax.plot([ct[0], sp[0]], [ct[1], sp[1]], ls=(0, (1.5, 2.5)), + lw=1.1, color=GUIDE, zorder=4) + arc = [uv((Ra * math.cos(a), Ra * math.sin(a), H)) + for a in np.linspace(a0, a1, 40)] + ax.plot([c[0] for c in arc[:-2]], [c[1] for c in arc[:-2]], + ls=(0, (4, 3)), lw=1.2, color=GUIDE, zorder=4) + ax.annotate("", xy=arc[-1], xytext=arc[-4], + arrowprops=dict(arrowstyle="->", color=GUIDE, lw=1.2), + zorder=4) + elif key == "strut_d_mm": + sp = uv(tuple(a + (b - a) * 0.55 for a, b in zip(bot[2], top[2]))) + ax.annotate("", xy=sp, xytext=(sp[0] + 42, sp[1] + 20), + arrowprops=dict(arrowstyle="->", color=GUIDE, lw=1.2, + shrinkB=10), zorder=4) + elif key == "cable_d_mm": + cm = uv(tuple((a + b) / 2 for a, b in zip(bot[0], bot[1]))) + ax.annotate("", xy=cm, xytext=(cm[0] - 42, cm[1] - 22), + arrowprops=dict(arrowstyle="->", color=GUIDE, lw=1.2, + shrinkB=8), zorder=4) + + +def render_frame(key, label, sub, fmt, value, bbox): + """Render one frame to a PIL image.""" + lo, hi = BOUNDS[key] + u0, u1, v0, v1 = bbox + fig = plt.figure(figsize=(FIG_W_IN, FIG_H_IN), dpi=DPI) + fig.patch.set_facecolor("white") + + ax = fig.add_axes([0.02, 0.10, 0.62, 0.86]) + center = ((u0 + u1) / 2, (v0 + v1) / 2) + # Margin for the H dimension line and twist arc outside the structure. + ppmm = setup_axes(ax, fig, center, (u1 - u0) + 95, (v1 - v0) + 40) + params = frame_params(key, value) + draw_structure(ax, params, ppmm) + annotate_sweep(ax, key, params) + + # Right-hand panel: parameter name, live value, slider, fixed caption. + fig.text(0.815, 0.74, label, ha="center", va="bottom", + fontsize=15, color=INK, fontweight="bold") + if sub: + fig.text(0.815, 0.715, sub, ha="center", va="top", + fontsize=11, color=INK2, style="italic") + fig.text(0.815, 0.60, fmt.format(value), ha="center", va="center", + fontsize=26, color=ACCENT, fontweight="bold") + + sx0, sx1, sy = 0.70, 0.93, 0.47 + frac = (value - lo) / (hi - lo) + fig.add_artist(plt.Line2D([sx0, sx1], [sy, sy], color=GUIDE, lw=3, + solid_capstyle="round", + transform=fig.transFigure)) + fig.add_artist(plt.Line2D([sx0 + frac * (sx1 - sx0)], [sy], + marker="o", markersize=11, color=ACCENT, + transform=fig.transFigure)) + fig.text(sx0, sy - 0.045, fmt.format(lo), ha="center", va="top", + fontsize=10.5, color=INK2) + fig.text(sx1, sy - 0.045, fmt.format(hi), ha="center", va="top", + fontsize=10.5, color=INK2) + + fig.text(0.815, 0.30, "the other four parameters\nstay at mid-range", + ha="center", va="center", fontsize=10.5, color=INK2, + style="italic", linespacing=1.5) + + fig.canvas.draw() + img = Image.fromarray(np.asarray(fig.canvas.buffer_rgba())[..., :3]) + plt.close(fig) + return img + + +def build_gif(name, key, label, sub, fmt, bbox): + values = sweep_values(key) + frames = [render_frame(key, label, sub, fmt, v, bbox) for v in values] + palette = frames[0].quantize(colors=128) + frames_q = [f.quantize(colors=128, palette=palette, dither=0) + for f in frames] + durations = [STEP_MS] * len(frames_q) + durations[0] = durations[STEPS - 1] = HOLD_MS # pause at both bounds + out = OUT_DIR / f"gif-param-{name}.gif" + frames_q[0].save(out, save_all=True, append_images=frames_q[1:], + duration=durations, loop=0, optimize=True) + print(f"wrote {out} ({out.stat().st_size / 1e6:.2f} MB, " + f"{len(frames_q)} frames)") + + +def main(): + OUT_DIR.mkdir(parents=True, exist_ok=True) + bbox = union_bbox() + for name, key, label, sub, fmt in SWEEPS: + build_gif(name, key, label, sub, fmt, bbox) + + +if __name__ == "__main__": + main() diff --git a/presentation/build_search_space_sequence_gif.py b/presentation/build_search_space_sequence_gif.py new file mode 100644 index 00000000..44b12c67 --- /dev/null +++ b/presentation/build_search_space_sequence_gif.py @@ -0,0 +1,181 @@ +"""Build the single sequential search-space GIF for the IDETC slide deck. + +One continuous animation instead of five separate loops: the design starts +with every geometry parameter at its lower bound, then each dial is turned +up to its upper bound one after another (triangle radius, height, twist +angle, strut diameter, cable diameter) with no reset in between. Requested +in PR #84 (me-madsen, 2026-08-20): "the dials start at the minimum +parameters and then each one is dialed up one by one", to be spoken over +as "we vary the spread of the base, the height of the structure, the +angle of twist, ...". + +Geometry, bounds, colors, and the depth-sorted renderer come from +build_search_space_figure.py; the per-stage dashed guides come from +build_search_space_gifs.py, so all three assets stay consistent. One +camera and one mm scale cover every frame, so the ground plane stays put +while the structure grows. + +Revised per PR #84 (me-madsen, 2026-08-20): no caption text on the slide, +no panel header, and each dial carries its bound values at its ends. + +Output: presentation/media/gif-param-sequence.gif (16:9, 1920x1080). +""" + +from __future__ import annotations + +import math +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +from PIL import Image + +from build_search_space_figure import ( + BOUNDS, + GUIDE, + INK, + INK2, + draw_structure, + nodes, + project, + setup_axes, +) +from build_search_space_gifs import ACCENT, annotate_sweep + +HERE = Path(__file__).resolve().parent +OUT = HERE / "media" / "gif-param-sequence.gif" + +FIG_W_IN, FIG_H_IN, DPI = 8.0, 4.5, 240 # 1920 x 1080 px +STEPS = 22 # frames for one lo -> hi traverse +STEP_MS = 70 # per-frame duration mid-sweep +STAGE_HOLD_MS = 650 # pause after each dial reaches its bound +END_HOLD_MS = 1600 # pause at all-min (start) and all-max (end) +PENDING = "#c8c8c8" # dial track/knob color before a stage runs + +# Stage order matches the intended narration, base upward. +STAGES = [ + ("R_mm", "triangle radius R", "{:.0f} mm"), + ("H_mm", "height H", "{:.0f} mm"), + ("twist_deg", "twist angle", "{:.0f}\N{DEGREE SIGN}"), + ("strut_d_mm", "strut diameter", "{:.1f} mm"), + ("cable_d_mm", "cable diameter", "{:.1f} mm"), +] + + +def eased(lo, hi): + """Cosine-eased lo -> hi value sequence, STEPS frames.""" + return [lo + (hi - lo) * (1 - math.cos(math.pi * t / (STEPS - 1))) / 2 + for t in range(STEPS)] + + +def stage_params(stage, value): + """Earlier dials sit at their max, later ones at their min.""" + p = {} + for j, (key, _, _) in enumerate(STAGES): + lo, hi = BOUNDS[key] + p[key] = hi if j < stage else lo + p[STAGES[stage][0]] = value + return p + + +def frame_sequence(): + """(stage_index, params, hold_ms) for every frame, no resets.""" + frames = [] + for i, (key, _, _) in enumerate(STAGES): + lo, hi = BOUNDS[key] + for t, val in enumerate(eased(lo, hi)): + hold = STEP_MS + if i == 0 and t == 0: + hold = END_HOLD_MS + elif t == STEPS - 1: + hold = END_HOLD_MS if i == len(STAGES) - 1 else STAGE_HOLD_MS + frames.append((i, stage_params(i, val), hold)) + return frames + + +def union_bbox(frames): + """(u, v) bounding box over every frame, one camera for the whole run.""" + us, vs = [], [] + for _, params, _ in frames: + bot, top = nodes(params) + for pt in bot + top: + u, v, _ = project(pt) + us.append(u) + vs.append(v) + return min(us), max(us), min(vs), max(vs) + + +def draw_dial_panel(fig, stage, params): + """Five stacked dials; done ones full, the active one live, rest empty.""" + sx0, sx1 = 0.685, 0.945 + for j, (key, label, fmt) in enumerate(STAGES): + lo, hi = BOUNDS[key] + val = params[key] + frac = (val - lo) / (hi - lo) + y = 0.78 - j * 0.14 + active = j == stage + done = j < stage + c_track = GUIDE if (active or done) else PENDING + c_knob = ACCENT if active else (INK2 if done else PENDING) + c_label = INK if active else INK2 + c_value = ACCENT if active else (INK2 if done else PENDING) + fig.text(sx0, y + 0.045, label, ha="left", va="bottom", + fontsize=10, color=c_label, + fontweight="bold" if active else "normal") + fig.text(sx1, y + 0.045, fmt.format(val), ha="right", va="bottom", + fontsize=10, color=c_value, + fontweight="bold" if active else "normal") + fig.add_artist(plt.Line2D([sx0, sx1], [y, y], color=c_track, + lw=2.6, solid_capstyle="round", + transform=fig.transFigure)) + fig.add_artist(plt.Line2D([sx0 + frac * (sx1 - sx0)], [y], + marker="o", markersize=9, color=c_knob, + transform=fig.transFigure)) + # Bound values at the dial ends (lo left, hi right). + fig.text(sx0, y - 0.032, fmt.format(lo), ha="center", va="top", + fontsize=8, color=INK2) + fig.text(sx1, y - 0.032, fmt.format(hi), ha="center", va="top", + fontsize=8, color=INK2) + + +def render_frame(stage, params, bbox): + u0, u1, v0, v1 = bbox + fig = plt.figure(figsize=(FIG_W_IN, FIG_H_IN), dpi=DPI) + fig.patch.set_facecolor("white") + + ax = fig.add_axes([0.02, 0.05, 0.62, 0.91]) + center = ((u0 + u1) / 2, (v0 + v1) / 2) + # Margin for the H dimension line and twist arc outside the structure. + ppmm = setup_axes(ax, fig, center, (u1 - u0) + 95, (v1 - v0) + 40) + draw_structure(ax, params, ppmm) + annotate_sweep(ax, STAGES[stage][0], params) + + draw_dial_panel(fig, stage, params) + + fig.canvas.draw() + img = Image.fromarray(np.asarray(fig.canvas.buffer_rgba())[..., :3]) + plt.close(fig) + return img + + +def main(): + frames = frame_sequence() + bbox = union_bbox(frames) + imgs = [render_frame(stage, params, bbox) + for stage, params, _ in frames] + palette = imgs[-1].quantize(colors=256) + imgs_q = [f.quantize(colors=256, palette=palette, dither=0) + for f in imgs] + durations = [hold for _, _, hold in frames] + OUT.parent.mkdir(parents=True, exist_ok=True) + imgs_q[0].save(OUT, save_all=True, append_images=imgs_q[1:], + duration=durations, loop=0, optimize=True) + print(f"wrote {OUT} ({OUT.stat().st_size / 1e6:.2f} MB, " + f"{len(imgs_q)} frames)") + + +if __name__ == "__main__": + main() diff --git a/presentation/build_search_space_videos.py b/presentation/build_search_space_videos.py new file mode 100644 index 00000000..7cf657ff --- /dev/null +++ b/presentation/build_search_space_videos.py @@ -0,0 +1,170 @@ +"""Build the two search-space MP4 videos for the IDETC slide deck. + +Video versions of gif-param-sequence.gif and gif-designs-tour.gif, +requested in PR #84 (me-madsen, 2026-08-20): "make the first two gifs +into videos", with the prism in the same place, size, and orientation at +the end of the first video and the start of the second so the presenter +can cut between them without the audience noticing. + +How the seamless join is guaranteed rather than eyeballed: + +- One camera covers both videos: the (u, v) bounding box is the union + over every frame of *both* animations, and both use the same axes + rectangle and margin, so the ground plane and the mm scale never move + between files. +- The boundary frame is shared code, not matched styling: video 1 ends + on, and video 2 opens on, a frame rendered by the same function with + the same parameters (every dial at its upper bound, tour-style panel, + no sweep annotation). The script asserts the two renders are + pixel-identical before encoding. + +Motion frames land on the 30 fps output grid (one render per tick), so +the videos are smoother than the 70 ms-per-frame GIFs while keeping the +same pacing. Encoded H.264 / yuv420p / faststart so PowerPoint plays +them without re-encoding. + +Outputs: +- presentation/media/video-param-sequence.mp4 (1920x1080) +- presentation/media/video-designs-tour.mp4 (1920x1080) +""" + +from __future__ import annotations + +import subprocess +import tempfile +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +from PIL import Image + +import build_designs_tour_gif as tour +import build_search_space_sequence_gif as seq +from build_search_space_figure import ( + BOUNDS, + draw_structure, + nodes, + project, + setup_axes, +) +from build_search_space_gifs import annotate_sweep + +HERE = Path(__file__).resolve().parent +OUT_SEQ = HERE / "media" / "video-param-sequence.mp4" +OUT_TOUR = HERE / "media" / "video-designs-tour.mp4" + +FIG_W_IN, FIG_H_IN, DPI = 8.0, 4.5, 240 # 1920 x 1080 px +FPS = 30 +TICK_MS = 1000.0 / FPS + +# One render per output tick: double the GIFs' step counts and put each +# motion frame on the 30 fps grid, so pacing matches the approved GIFs +# (sequence 22 x 70 ms ~ 1.5 s per dial, tour 18 x 70 ms ~ 1.3 s per +# design) while the motion is twice as smooth. +seq.STEPS, seq.STEP_MS = 44, TICK_MS +tour.STEPS, tour.STEP_MS = 38, TICK_MS + +BOUNDARY_HOLD_MS = 1200 # static all-max hold on each side of the cut +ALL_MAX = {k: hi for k, (lo, hi) in BOUNDS.items()} + + +def union_bbox(param_sets): + us, vs = [], [] + for params in param_sets: + bot, top = nodes(params) + for pt in bot + top: + u, v, _ = project(pt) + us.append(u) + vs.append(v) + return min(us), max(us), min(vs), max(vs) + + +def render(params, bbox, stage=None): + """One 1920x1080 frame. stage=None renders the tour/boundary style + (all dials live, no sweep annotation); an integer renders the + sequence style for that active dial.""" + u0, u1, v0, v1 = bbox + fig = plt.figure(figsize=(FIG_W_IN, FIG_H_IN), dpi=DPI) + fig.patch.set_facecolor("white") + ax = fig.add_axes([0.02, 0.05, 0.62, 0.91]) + center = ((u0 + u1) / 2, (v0 + v1) / 2) + # Margin for the H dimension line and twist arc outside the + # structure (used by the sequence video; the tour shares it so the + # prism does not move at the cut). + ppmm = setup_axes(ax, fig, center, (u1 - u0) + 95, (v1 - v0) + 40) + draw_structure(ax, params, ppmm) + if stage is None: + tour.draw_dial_panel(fig, params) + else: + annotate_sweep(ax, seq.STAGES[stage][0], params) + seq.draw_dial_panel(fig, stage, params) + fig.canvas.draw() + img = Image.fromarray(np.asarray(fig.canvas.buffer_rgba())[..., :3]) + plt.close(fig) + return img + + +def encode(frames, out_path): + """frames: list of (PIL image, hold_ms). Encode via ffmpeg concat.""" + with tempfile.TemporaryDirectory() as td: + tdp = Path(td) + lines = ["ffconcat version 1.0"] + for i, (img, hold_ms) in enumerate(frames): + p = tdp / f"f{i:05d}.png" + img.save(p) + lines.append(f"file '{p}'") + lines.append(f"duration {hold_ms / 1000.0:.6f}") + # Concat demuxer ignores the last entry's duration unless the + # final file is listed once more. + lines.append(f"file '{tdp / f'f{len(frames) - 1:05d}.png'}'") + (tdp / "list.txt").write_text("\n".join(lines) + "\n") + out_path.parent.mkdir(parents=True, exist_ok=True) + subprocess.run( + ["ffmpeg", "-y", "-loglevel", "error", "-f", "concat", + "-safe", "0", "-i", str(tdp / "list.txt"), + "-vf", f"fps={FPS},format=yuv420p", + "-c:v", "libx264", "-crf", "18", "-preset", "medium", + "-movflags", "+faststart", str(out_path)], + check=True) + print(f"wrote {out_path} ({out_path.stat().st_size / 1e6:.2f} MB, " + f"{len(frames)} source frames)") + + +def main(): + seq_frames = seq.frame_sequence() # (stage, params, hold_ms) + tour_frames = tour.frame_sequence() # (params, hold_ms) + bbox = union_bbox([p for _, p, _ in seq_frames] + + [p for p, _ in tour_frames]) + + # Video 1: the five dials turned up one by one, then settle on the + # shared boundary frame (all-max, all dials live, no annotation). + v1 = [] + for i, (stage, params, hold) in enumerate(seq_frames): + if i == len(seq_frames) - 1: + hold = seq.STAGE_HOLD_MS # settle replaces the long end hold + v1.append((render(params, bbox, stage=stage), hold)) + boundary_v1 = render(ALL_MAX, bbox) + v1.append((boundary_v1, BOUNDARY_HOLD_MS)) + + # Video 2: opens on the same boundary frame, then tours the seeds. + v2 = [] + for i, (params, hold) in enumerate(tour_frames): + if i == 0: + hold = BOUNDARY_HOLD_MS + v2.append((render(params, bbox), hold)) + + # The join must be pixel-identical, not merely close. + if not np.array_equal(np.asarray(boundary_v1), np.asarray(v2[0][0])): + raise SystemExit("boundary frames differ; videos would pop") + print("boundary check: last frame of video 1 == first frame of " + "video 2 (pixel-exact)") + + encode(v1, OUT_SEQ) + encode(v2, OUT_TOUR) + + +if __name__ == "__main__": + main() diff --git a/presentation/build_seed_designs_figure.py b/presentation/build_seed_designs_figure.py new file mode 100644 index 00000000..6841df70 --- /dev/null +++ b/presentation/build_seed_designs_figure.py @@ -0,0 +1,62 @@ +"""Build the nine-seed-designs figure for the IDETC slide deck. + +All nine campaign seed designs (S0, then Sobol specimens 1 to 8, i.e. +the rows of bo/t3-prism-bo-batch.csv) on one full-bleed slide, drawn in +the same rendering style as the search-space GIFs, on one common mm +scale, in specimen order left to right, top to bottom. No dials and no +text, per PR #84 (me-madsen, 2026-08-20). + +Geometry, colors, and the depth-sorted renderer come from +build_search_space_figure.py; the seed values from +build_designs_tour_gif.py, so the still and the tour GIF cannot drift +apart. + +Output: presentation/media/fig-seed-designs.png (16:9, for a full slide). +""" + +from __future__ import annotations + +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt + +from build_designs_tour_gif import SEEDS +from build_search_space_figure import ( + draw_structure, + setup_axes, + struct_center, +) + +HERE = Path(__file__).resolve().parent +OUT = HERE / "media" / "fig-seed-designs.png" + + +def main(): + fig = plt.figure(figsize=(13.333, 7.5), dpi=200) + fig.patch.set_facecolor("white") + + # 3 x 3 grid, specimen order left to right, top to bottom. Every cell + # shares one mm scale: setup_axes picks the scale from the given span, + # so the same span in every identical cell means the same mm/inch. + cols, rows = 3, 3 + cw, ch = 1.0 / cols, 1.0 / rows + for i, p in enumerate(SEEDS): + r, c = divmod(i, cols) + ax = fig.add_axes([c * cw + 0.012, (rows - 1 - r) * ch + 0.012, + cw - 0.024, ch - 0.024]) + cu, cv = struct_center(p) + # Span sized so the tallest seed (H 104 mm plus twist overhang) + # fits its cell; identical for all cells -> common scale. + ppmm = setup_axes(ax, fig, (cu, cv), 125, 118) + draw_structure(ax, p, ppmm) + + OUT.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(OUT, facecolor="white") + print(f"wrote {OUT}") + + +if __name__ == "__main__": + main() diff --git a/presentation/build_supplement_deck.py b/presentation/build_supplement_deck.py new file mode 100644 index 00000000..8d28b369 --- /dev/null +++ b/presentation/build_supplement_deck.py @@ -0,0 +1,818 @@ +"""Rebuild the IDETC supplement deck: message titles, big type, real video. + +Design rules enforced here (the v1 deck broke all of them): + * one full-sentence message per slide, in the title, <= 2 rendered lines + * body text >= 24 pt, <= ~160 characters per slide + * the visual owns the slide; text does not + * every media slot holds a real clip or figure, not a dashed placeholder + +Sterling's EMC 2026 Bayesian-optimization block is carried over untouched +(byte-identical slide XML, animations intact) from the v1 supplement deck. + +Usage: python presentation/build_supplement_deck.py +""" + +from pathlib import Path + +from pptx import Presentation +from pptx.dml.color import RGBColor +from pptx.enum.shapes import MSO_SHAPE +from pptx.enum.text import PP_ALIGN +from pptx.util import Emu, Inches, Pt + +HERE = Path(__file__).parent +DECKS = HERE / "Slide Decks" +MEDIA = HERE / "media" +# Read from the pristine EMC block, never from the output deck: SRC == OUT +# made the build non-idempotent, and a second run silently sliced its own +# freshly-written slides 4-14 out in place of the BO explainer. +SRC = HERE / "emc-bo-block.pptx" +OUT = DECKS / "IDETC Supplement Slides (BO block + gap + video + accel).pptx" + +# EMC slides to keep from SRC (1-indexed): the whole BO explainer block. +KEEP = list(range(1, 12)) + +NAVY = RGBColor(0x0E, 0x28, 0x41) +BLUE = RGBColor(0x15, 0x60, 0x82) +ORANGE = RGBColor(0xE9, 0x71, 0x32) +GRAY = RGBColor(0x59, 0x59, 0x59) + +SW, SH = Inches(13.333), Inches(7.5) +MARGIN = Inches(0.55) +TITLE_TOP = Inches(0.35) +TITLE_H = Inches(1.30) +BODY_TOP = Inches(1.90) +BODY_BOTTOM = Inches(6.95) + + +def title_only(prs): + return prs.slides.add_slide(prs.slide_layouts[1]) + + +def set_message(slide, text, size=30): + """Put the slide's one message in the title placeholder, Doumont-style.""" + ph = slide.shapes.title + ph.left, ph.top = MARGIN, TITLE_TOP + ph.width, ph.height = SW - 2 * MARGIN, TITLE_H + tf = ph.text_frame + tf.word_wrap = True + tf.text = text + p = tf.paragraphs[0] + p.alignment = PP_ALIGN.LEFT + for r in p.runs: + r.font.size = Pt(size) + r.font.color.rgb = NAVY + return ph + + +def textbox(slide, left, top, width, height, lines, size=24, color=NAVY, + bold_first=False, space=10, align=PP_ALIGN.LEFT): + box = slide.shapes.add_textbox(left, top, width, height) + tf = box.text_frame + tf.word_wrap = True + for i, line in enumerate(lines): + p = tf.paragraphs[0] if i == 0 else tf.add_paragraph() + p.text = line + p.alignment = align + p.space_after = Pt(space) + for r in p.runs: + r.font.size = Pt(size) + r.font.color.rgb = color + r.font.bold = bold_first and i == 0 + return box + + +def credit(slide, text, left=None, top=None, width=None): + left = MARGIN if left is None else left + top = Inches(7.02) if top is None else top + width = SW - 2 * MARGIN if width is None else width + box = slide.shapes.add_textbox(left, top, width, Inches(0.34)) + tf = box.text_frame + tf.word_wrap = True + tf.text = text + for r in tf.paragraphs[0].runs: + r.font.size = Pt(12) + r.font.color.rgb = GRAY + r.font.italic = True + return box + + +def fit(box_l, box_t, box_w, box_h, native_w, native_h): + """Letterbox a native-aspect asset into a box, centered.""" + scale = min(box_w / native_w, box_h / native_h) + w, h = int(native_w * scale), int(native_h * scale) + return (int(box_l + (box_w - w) / 2), int(box_t + (box_h - h) / 2), w, h) + + +def add_video(slide, stem, native, box, poster=None): + left, top, w, h = fit(*box, *native) + return slide.shapes.add_movie( + str(MEDIA / stem), Emu(left), Emu(top), Emu(w), Emu(h), + poster_frame_image=str(MEDIA / poster) if poster else None, + mime_type="video/mp4", + ) + + +def add_image(slide, name, box): + from PIL import Image + + with Image.open(MEDIA / name) as im: + native = im.size + left, top, w, h = fit(*box, *native) + return slide.shapes.add_picture(str(MEDIA / name), Emu(left), Emu(top), + Emu(w), Emu(h)) + + +def notes(slide, text): + slide.notes_slide.notes_text_frame.text = text + + +# -------------------------------------------------------------------------- +# generated figures +# -------------------------------------------------------------------------- + +# Callout geometry for the tensegrity anatomy figure. The labels are drawn at +# canvas resolution rather than as PowerPoint text boxes so the leader lines +# can land on specific members; the sizes below put the label text at ~26 pt +# once the figure is placed on the slide. +ANATOMY_CANVAS = (1600, 800) +ANATOMY_PHOTO = (30, 45, 700) # left, top, height in canvas px +ANATOMY_LABEL_X = 800 + +# (anchor x, anchor y) in photo-native px; label top; heading; sub-line +ANATOMY_CALLOUTS = [ + ((300, 165), 150, "Struts", "carry compression only"), + ((360, 263), 320, "No strut touches another", + "load passes through the cables"), + ((600, 400), 500, "Cables", "carry tension only"), +] + + +def build_anatomy_figure(): + """Draw fig-tensegrity-anatomy.png from the 2D teaching-model still.""" + from PIL import Image, ImageDraw, ImageFont + + out = MEDIA / "fig-tensegrity-anatomy.png" + photo = Image.open(MEDIA / "photo-tensegrity-2d-model.jpg").convert("RGB") + left, top, height = ANATOMY_PHOTO + scale = height / photo.height + photo = photo.resize((int(photo.width * scale), height), Image.LANCZOS) + + canvas = Image.new("RGB", ANATOMY_CANVAS, "white") + canvas.paste(photo, (left, top)) + draw = ImageDraw.Draw(canvas) + + font_dir = "/usr/share/fonts/truetype/dejavu" + bold = ImageFont.truetype(f"{font_dir}/DejaVuSans-Bold.ttf", 52) + plain = ImageFont.truetype(f"{font_dir}/DejaVuSans.ttf", 44) + orange, navy, gray = (0xE9, 0x71, 0x32), (0x0E, 0x28, 0x41), (0x59,) * 3 + + for (ax, ay), ly, head, tail in ANATOMY_CALLOUTS: + cx, cy = left + ax * scale, top + ay * scale + draw.line([(ANATOMY_LABEL_X - 30, ly + 34), (cx, cy)], fill=orange, + width=7) + draw.ellipse([cx - 13, cy - 13, cx + 13, cy + 13], fill=orange) + draw.text((ANATOMY_LABEL_X, ly), head, font=bold, fill=navy) + draw.text((ANATOMY_LABEL_X, ly + 66), tail, font=plain, fill=gray) + for text, font in ((head, bold), (tail, plain)): + if ANATOMY_LABEL_X + draw.textlength(text, font=font) > \ + ANATOMY_CANVAS[0] - 20: + raise ValueError(f"callout overruns the canvas: {text!r}") + + canvas.save(out) + return out + + +def strip_to_emc_block(prs): + """Delete every v1 slide except the imported EMC block, preserving XML.""" + id_lst = prs.slides._sldIdLst + entries = list(id_lst) + keep_ids = {entries[i - 1] for i in KEEP} + for entry in entries: + if entry not in keep_ids: + rid = entry.get( + "{http://schemas.openxmlformats.org/officeDocument/2006/" + "relationships}id" + ) + id_lst.remove(entry) # unlink first so the rel refcount drops to 0 + prs.part.drop_rel(rid) + # python-pptx names a new slide part slide.xml rather than filling + # gaps, so the survivors must be renumbered 1..n or new slides collide with + # them and the package ends up with duplicate zip entries. + from pptx.opc.packuri import PackURI + + for i, slide in enumerate(prs.slides, start=1): + slide.part.partname = PackURI(f"/ppt/slides/slide{i}.xml") + return len(keep_ids) + + +def move_block_after(prs, block_len, position): + """Move the leading `block_len` slides to sit after `position` slides.""" + id_lst = prs.slides._sldIdLst + entries = list(id_lst) + block, rest = entries[:block_len], entries[block_len:] + order = rest[:position] + block + rest[position:] + for e in entries: + id_lst.remove(e) + for e in order: + id_lst.append(e) + + +# -------------------------------------------------------------------------- +# slide builders +# -------------------------------------------------------------------------- + +def s_readme(prs): + s = title_only(prs) + set_message(s, "How to use this supplement (delete before the talk)", 28) + textbox(s, MARGIN, BODY_TOP, SW - 2 * MARGIN, Inches(4.4), [ + "Slides 8–18 are Sterling's EMC 2026 originals — animations intact. " + "Copy with “Keep Source Formatting.”", + "Every video on these slides is a real embedded clip; click to play, " + "no internet needed.", + "Working slides (this one, and the last two) are planning aids, not " + "talk slides.", + ], size=22, color=NAVY) + notes(s, "Not a talk slide. Built by presentation/build_supplement_deck.py.") + return s + + +def s_hook(prs): + s = title_only(prs) + set_message(s, "A tensegrity lander can hit the ground at highway speed, " + "bounce, and still get up.") + add_video(s, "clip-titan-descent.mp4", (1280, 720), + (Inches(2.15), BODY_TOP, Inches(9.0), Inches(5.0)), + poster="poster-titan.jpg") + credit(s, "NASA Super Ball Bot / NIAC Titan mission concept — 20 s excerpt. " + "Credit: NASA.") + notes(s, "Open here. Play the clip silently, say nothing for the first " + "five seconds. Then the scope line: a printed PLA-TPU T3 prism is " + "our proxy for developing the workflow, not flight hardware.") + return s + + +def s_what_is_tensegrity(prs): + s = title_only(prs) + set_message(s, "A tensegrity holds itself up with rigid struts that never " + "touch, floating in a net of cables.") + add_video(s, "clip-tensegrity-2d-teaching.mp4", (1280, 718), + (Inches(2.15), BODY_TOP, Inches(9.0), Inches(5.0)), + poster="poster-mould-teaching.jpg") + credit(s, "Steve Mould, “Tensegrity Explained” (youtube.com/watch?v=" + "0onncd0_0-o) — 35 s excerpt, played with sound, used with " + "on-screen credit.") + notes(s, + "BACKGROUND, first beat. Play the whole 35 s with the sound up and " + "stay quiet — the narration does the teaching, and the 2D model is " + "the fastest way to make the mechanism obvious to anyone who has " + "never seen a tensegrity.\n\n" + "Watch for: the model standing with nothing glued or hinged; the " + "push; the spring back. That elastic return is the whole reason a " + "tensegrity is interesting as an energy absorber.\n\n" + "The earlier 18 s silent crop cut the push-and-recovery off the " + "end, which threw away the part that teaches. Source: the Box " + "folder shared on PR #84.") + return s + + +def s_tensegrity_anatomy(prs): + """Freeze the teaching model and name its parts.""" + s = title_only(prs) + set_message(s, "Every load path is either pure compression in a strut or " + "pure tension in a cable — nothing in between.") + add_image(s, "fig-tensegrity-anatomy.png", + (MARGIN, BODY_TOP, SW - 2 * MARGIN, Inches(4.85))) + credit(s, "Still from the same clip — Steve Mould, “Tensegrity " + "Explained” (youtube.com/watch?v=0onncd0_0-o).") + notes(s, + "BACKGROUND, second beat. Freeze the model and name the parts, so " + "the vocabulary is in place before the tensegrity-inspired caveat " + "later on.\n\n" + "Say: this is why a tensegrity can be light and still stiff — no " + "member ever sees a bending moment, and the cables set the " + "stiffness. It is also why the geometry matters so much: change a " + "strut length or a cable pre-tension and the whole response " + "changes, which is what makes it an optimization problem.\n\n" + "This is the mechanism visual the mock audience said the grad " + "student needed in order to tell a tensegrity from a lattice.") + return s + + +def s_toy_to_lander(prs): + s = title_only(prs) + set_message(s, "NASA's idea started with a baby toy: throw it at the floor, " + "nothing breaks — that is a landing robot.") + add_video(s, "clip-nasa-toy-lander.mp4", (1280, 720), + (Inches(2.15), BODY_TOP, Inches(9.0), Inches(5.0)), + poster="poster-nasa-toy.jpg") + credit(s, "Adrian Agogino, NASA Ames — “NASA 360 Talks: Super Ball Bot” " + "(youtube.com/watch?v=0eC4A2PXM-U), first 16 s. Credit: NASA.") + notes(s, "PLAY THIS ONE WITH SOUND — the audio is the slide. He holds a " + "tensegrity baby toy, says they are made as baby toys because they " + "are almost impossible to break, throws it at the floor, and lands " + "on “hey, that's a landing robot.” Say nothing over it; pick up " + "with our own version of that idea. He reaches “planetary landers” " + "verbatim a few seconds later in the source video if you ever want " + "the longer cut (24.5-31 s).") + return s + + +def s_reusable(prs): + s = title_only(prs) + set_message(s, "Crushable honeycomb absorbs one impact; a tensegrity " + "structure survives drop after drop.") + add_video(s, "clip-superball-3m-drop.mp4", (1280, 722), + (Inches(2.15), BODY_TOP, Inches(9.0), Inches(5.0)), + poster="poster-superball.jpg") + credit(s, "SUPERball v2 3.4 m drop — IEEE Spectrum / NASA Ames " + "(youtube.com/watch?v=hkzeE6BVNIk), 10 s excerpt.") + notes(s, "This is the reusability argument Sterling asked for: the same " + "hardware takes the next drop. It is also why our own campaign " + "can re-test a specimen instead of consuming it.") + return s + + +def s_prior_work(prs): + s = title_only(prs) + set_message(s, "Tensegrity impact structures already exist — every one of " + "them was designed and assembled by hand.") + textbox(s, MARGIN, BODY_TOP, Inches(11.5), Inches(4.4), [ + "NASA SUPERball v2 — hand-built, one design, no optimization loop", + "Davami 2025 — printed tensegrity, characterized but not optimized", + "Gu & Dotov — tensegrity crutch tip: the application pull is real", + ], size=26) + credit(s, "TODO before freeze: verify citations against " + "manuscript/references.bib.") + notes(s, "Three names, ten seconds. The point is not a literature review; " + "it is that the design step is still manual everywhere.") + return s + + +def s_gap(prs): + s = title_only(prs) + set_message(s, "Nobody closes the loop: print the structure, drop it, and " + "let the measured data choose the next design.") + textbox(s, MARGIN, BODY_TOP, Inches(5.7), Inches(4.2), [ + "TODAY", "Hand-tuned geometry", "Simulation, rarely validated", + "One-off demonstrations", + ], size=26, color=GRAY, bold_first=True, space=14) + textbox(s, Inches(7.0), BODY_TOP, Inches(5.7), Inches(4.2), [ + "THIS WORK", "The drop test is the objective", + "Trade-offs learned from shock data", "One printer, one tower, one loop", + ], size=26, color=BLUE, bold_first=True, space=14) + arrow = s.shapes.add_shape(MSO_SHAPE.RIGHT_ARROW, Inches(6.05), + Inches(3.45), Inches(0.9), Inches(0.6)) + arrow.fill.solid() + arrow.fill.fore_color.rgb = ORANGE + arrow.line.fill.background() + notes(s, "This is the motivation-to-gap transition. Do not read the " + "columns; point left, point right, move on.") + return s + + +def s_caveat(prs): + s = title_only(prs) + set_message(s, "Honest caveat, in the same breath: as printed, these are " + "tensegrity-inspired, not true tensegrity.") + textbox(s, MARGIN, Inches(2.35), Inches(7.4), Inches(3.8), [ + "The black TPU members are not pre-tensioned — and they stretch.", + "Next: pre-tensioned prints, plus hand-built true-tensegrity " + "validation specimens.", + ], size=26, space=26) + add_image(s, "photo-specimen.jpg", + (Inches(8.35), Inches(1.95), Inches(4.4), Inches(4.9))) + credit(s, "Printed PLA–TPU T3 prism, prc1kn — frame from our own 959 fps " + "drop footage.") + notes(s, "Say this the moment multi-material printing appears — do not let " + "the audience discover it in Q&A. Pair it with the path forward " + "(issue #87 pre-tensioning) so it reads as scope, not weakness.") + return s + + +def s_print_timelapse(prs): + s = title_only(prs) + set_message(s, "One machine lays down the rigid struts and the stretchy " + "cables in a single build.") + add_video(s, "clip-print-timelapse.mp4", (1120, 720), + (Inches(6.35), Inches(1.95), Inches(6.45), Inches(4.9)), + poster="poster-print-timelapse.jpg") + textbox(s, MARGIN, Inches(2.55), Inches(5.5), Inches(3.8), [ + "Bambu Lab H2D, two materials: PLA struts, TPU cables", + "No jig, no knots, no post-assembly step", + ], size=26, space=26) + credit(s, "Our own print timelapse, TT3_01 — 16 s excerpt " + "(youtube.com/watch?v=nQNmi-NiL5I, BYU Vertical Cloud Lab).") + notes(s, "Spoken line: twenty-odd minutes of printing and no assembly step " + "is what makes a design-per-day loop possible at all. Play it " + "once, silently.\n\n" + "Pulled from our own YouTube channel through the lab Raspberry " + "Pi — the CI runner is bot-blocked by YouTube. Embedded here, so " + "it needs no internet at the podium.") + return s + + +def s_support_removal(prs): + s = title_only(prs) + set_message(s, "Printing it in one build is not the same as getting it out " + "clean — supports still come off by hand.") + add_video(s, "clip-support-removal.mp4", (960, 540), + (Inches(4.1), Inches(2.0), Inches(8.7), Inches(4.8)), + poster="poster-support-removal.jpg") + textbox(s, MARGIN, Inches(2.6), Inches(3.3), Inches(3.4), [ + "The honest bottleneck in the loop today", + ], size=24, color=GRAY) + credit(s, "Our own footage — manual support removal on a T3 prism " + "(PR #35, 2026-06-09).") + notes(s, "Challenge slide, ten seconds. Say the mitigation out loud: " + "painted manual supports and a 0.4 mm tip cut removal time, and " + "automating this step is exactly what stands between us and a " + "self-driving lab. Do not over-dwell; it is a caveat, not a " + "confession.") + return s + + +def s_drop_room(prs): + s = title_only(prs) + set_message(s, "The whole experiment takes a quarter of a second — here it " + "is in real time.") + add_video(s, "clip-drop-afar.mp4", (1280, 720), + (Inches(2.15), BODY_TOP, Inches(9.0), Inches(5.0)), + poster="poster-drop-afar.jpg") + credit(s, "Our own footage — drop tower, BYU Smart Materials lab. " + "Play with sound.") + notes(s, "PLAY THIS WITH SOUND ON. Seven seconds, real time, whole room. " + "The bang is the point: it tells the audience how violent a " + "millisecond-scale event this is before any plot appears. Then " + "say the standard condition out loud — 60 in onto the same felt " + "stack every time, repeated per specimen — and move on.") + return s + + +def s_drop_phone(prs): + s = title_only(prs) + set_message(s, "A phone in slow motion is enough to see the drop — and to " + "hear the impact.") + add_video(s, "clip-drop-phone-audio.mp4", (540, 960), + (Inches(8.0), Inches(1.85), Inches(4.6), Inches(5.05)), + poster="poster-drop-phone.jpg") + textbox(s, MARGIN, Inches(2.45), Inches(6.9), Inches(3.9), [ + "Free, instant, and good enough to catch setup mistakes", + "How we caught a specimen lifting off and a sensor coming loose", + ], size=25, space=24) + credit(s, "Our own footage — specimen n0jdwk, 13 in drop (PR #67). " + "Play with sound.") + notes(s, "Play with sound. Roughly eight times slower than real time " + "(1.6 s of descent for a fall that takes under 0.26 s), which is " + "the usual 240 fps phone mode. This is the cheap instrument and " + "it earned its place: phone slow-mo is how we caught the specimen " + "lifting off the base plate on the bungee-assisted tower, and how " + "we caught the accelerometer coming loose. It sets up the next " + "two slides, which say what the fast camera is and is not for.") + return s + + +def s_elastic_recovery(prs): + s = title_only(prs) + set_message(s, "The top vertex leaves the impact at 0.7 times the speed " + "it arrived — it springs back.") + add_video(s, "clip-drop-highspeed.mp4", (528, 720), + (Inches(8.2), Inches(1.9), Inches(4.4), Inches(4.95)), + poster="poster-drop-highspeed.jpg") + textbox(s, MARGIN, Inches(2.45), Inches(7.1), Inches(3.9), [ + "Elastic re-extension, not permanent collapse", + "Measured on both validation specimens; both intact afterwards", + ], size=25, space=24) + credit(s, "Our own 960 fps footage — specimen 7xadt6, 60 in / 5-felt " + "validation campaign (PR #86 branch).") + notes(s, "This is the reusability claim made with our own data instead of " + "NASA's, and it is squarely in the post-pulse regime the previous " + "slide said the camera owns. Snap-back at ~0.7x impact speed with " + "visibly intact specimens is in the 60in-5felts video writeup. " + "Say it plainly: a crushable absorber would not do this, and it " + "is why one specimen can serve many drops in the campaign.") + return s + + +def s_pipeline(prs): + s = title_only(prs) + set_message(s, "One drop becomes one row of data: three numbers and the " + "scatter around them.") + steps = [ + ("Raw", "1.25 MHz\n4 channels"), + ("Baseline", "pre-trigger\nmedian"), + ("J211 filter", "CFC-180\nand CFC-1000"), + ("Metrics", "peak force, SEA,\ncompaction"), + ] + x = MARGIN + w, gap = Inches(2.72), Inches(0.35) + for i, (head, sub) in enumerate(steps): + box = s.shapes.add_shape(MSO_SHAPE.ROUNDED_RECTANGLE, x, Inches(2.55), + w, Inches(1.85)) + box.fill.solid() + box.fill.fore_color.rgb = BLUE if i < 3 else ORANGE + box.line.fill.background() + tf = box.text_frame + tf.word_wrap = True + tf.text = head + for r in tf.paragraphs[0].runs: + r.font.size, r.font.bold = Pt(24), True + p = tf.add_paragraph() + p.text = sub + for r in p.runs: + r.font.size = Pt(16) + if i < 3: + arrow = s.shapes.add_shape(MSO_SHAPE.RIGHT_ARROW, x + w + Emu(30000), + Inches(3.25), gap - Emu(60000), + Inches(0.45)) + arrow.fill.solid() + arrow.fill.fore_color.rgb = GRAY + arrow.line.fill.background() + x = x + w + gap + textbox(s, MARGIN, Inches(4.95), Inches(12.2), Inches(1.8), [ + "Replicate drops give the noise model the optimizer needs — a single " + "drop is never one point.", + ], size=24, color=NAVY) + credit(s, "scripts/analysis/drop_test_*_analysis.py on the PR #86 branch; " + "raw CSVs committed next to every campaign.") + notes(s, "Answers issue #94 directly: this is the black box opened. Four " + "steps, one sentence each. The only judgement calls are step 2 " + "and step 3 — and both of them bit us, which is the next few " + "slides.") + return s + + +def s_filter_class(prs): + s = title_only(prs) + set_message(s, "SAE J211 sets the filter, not us — and the class you pick " + "changes the peak by a factor of two.") + add_image(s, "fig-baseline-and-cfc.png", + (MARGIN, Inches(1.80), SW - 2 * MARGIN, Inches(4.4))) + textbox(s, MARGIN, Inches(6.25), Inches(12.2), Inches(0.7), [ + "Same drop, same sensor: raw 567 G · CFC-1000 344 G · CFC-180 245 G", + ], size=24, color=BLUE) + credit(s, "Specimen bpx68c, Signal 11 (data/drop-tests/pu-configs/). " + "Two-pole Butterworth, forward and backward, per SAE J211-1.") + notes(s, "For issue #94. CFC-180 is a 300 Hz corner, CFC-1000 is 1650 Hz — " + "the number in the class name times 1.65. Neither is more correct; " + "CFC-180 is the automotive-crash convention for what a body feels, " + "CFC-1000 keeps the structural ringing we need to hear the " + "specimen. That is why our tables report both, and why moving " + "between them changed a recommendation.") + return s + + +def s_two_bands(prs): + s = title_only(prs) + set_message(s, "We report both bands because they answer two different " + "questions.") + textbox(s, MARGIN, Inches(2.1), Inches(6.0), Inches(4.3), [ + "CFC-180 · 300 Hz", + "“What does the payload feel?”", + "Smooth pulse, stable peak, the number that goes in the force " + "constraint", + ], size=25, color=BLUE, bold_first=True, space=18) + textbox(s, Inches(7.0), Inches(2.1), Inches(5.7), Inches(4.3), [ + "CFC-1000 · 1650 Hz", + "“What is the structure doing?”", + "Keeps the 500–550 Hz specimen mode — the part that actually differs " + "between designs", + ], size=25, color=ORANGE, bold_first=True, space=18) + credit(s, "Specimen first mode measured at 519–549 Hz across the ringdown " + "analyses in this repo.") + notes(s, "The honest version of the answer to Sterling's question in #94. " + "We did not abandon CFC-180; we stopped using it as the only " + "band, because at a 300 Hz corner the specimen's own mode is " + "filtered away and every design looks alike. If asked which one " + "feeds the optimizer: the constraint is CFC-180, the " + "discrimination diagnostics are CFC-1000.") + return s + + +def s_adversary(prs): + s = title_only(prs) + set_message(s, "We paid an adversary to break our own analysis, and it " + "broke it.") + textbox(s, MARGIN, Inches(1.75), Inches(12.2), Inches(1.5), [ + "Our script assumed the record began at the trigger. It does not — " + "0.41 ms of it is pre-impact.", + ], size=24, space=6) + add_image(s, "fig-baseline-flip.png", + (Inches(1.55), Inches(2.75), Inches(10.2), Inches(3.35))) + textbox(s, MARGIN, Inches(6.18), Inches(12.2), Inches(0.75), [ + "Verdict we adopted: none of the four — that sweep could not decide.", + ], size=24, color=BLUE) + credit(s, "Edison Scientific adversarial review, task d9092c5a; " + "recomputation reproduced independently in-repo.") + notes(s, "This is the credibility slide, and it is the direct answer to " + "issue #94's “we would like to spot-check you.” Deliver it " + "without drama: we asked for the analysis to be attacked, four " + "of our grounds fell, we marked the document superseded rather " + "than quietly patching it. If a reviewer asks what else it " + "touched: two sibling analyses share the baseline, both flagged " + "for re-run.") + return s + + +def s_checkable(prs): + s = title_only(prs) + set_message(s, "Every number on these slides can be re-derived from " + "committed raw data.") + textbox(s, MARGIN, BODY_TOP, Inches(12.2), Inches(4.4), [ + "Raw CSVs and the analysis script live beside each campaign in the " + "repo", + "The filter is a published standard: SAE J211-1 / ISO 6487, channel " + "class × 1.65 Hz", + "The adversarial re-analysis, its recomputed tables, and its notebook " + "are committed too", + ], size=25, space=18) + credit(s, "Backup slide: repo paths and the tutorial notebook for issue " + "#94 are listed in presentation/issue-94-analysis-slides.md.") + notes(s, "Backup slide for Q&A, and the place to put the Colab link once " + "the tutorial notebook from issue #94 exists. The spoken version " + "is one sentence: nothing here is a black box you have to take " + "on trust.") + return s + + +def s_video_capture(prs): + s = title_only(prs) + set_message(s, "High-speed video shows the structure spring back and hold " + "its shape.") + add_video(s, "clip-our-slomo-drop.mp4", (720, 720), + (Inches(7.35), BODY_TOP, Inches(5.4), Inches(5.0)), + poster="poster-our-drop.jpg") + textbox(s, MARGIN, Inches(2.35), Inches(6.3), Inches(4.4), [ + "Sony RX100 IV, 959 fps", + "Rebound, sag and recovery over the next 150 ms", + "No permanent deformation — the specimen goes back in the tower", + ], size=26, space=40) + credit(s, "Our own footage: prc1kn specimen, 60 in drop, 5-felt input " + "(data/drop-tests/prc1kn-60in-5felt/video/).") + notes(s, "Play it once, silently, and talk about what they can see: it " + "bounces, and it comes back intact. That is the reusability " + "argument in our own data.\n\n" + "PRESENTER-ONLY, do not volunteer: one frame is 1.04 ms, so the " + "1.6 ms deceleration itself spans 1-2 frames — the accelerometer " + "is what measures the pulse. Resolving compression *during* the " + "pulse would need >=5000 fps DIC " + "(docs/drop-test-prc1kn-video-analysis.md, section 3). Only bring " + "this up if someone asks what the camera can resolve.") + return s + + +def s_video_processing(prs): + s = title_only(prs) + set_message(s, "One clean shock, an elastic rebound, and the brake catches " + "the carriage 79 ms later.") + add_image(s, "fig-video-montage.png", + (MARGIN, Inches(1.95), SW - 2 * MARGIN, Inches(4.9))) + credit(s, "prc1kn drop 1, 959.04 fps.") + notes(s, "Walk it left to right in one sentence: entry, contact, " + "turnaround, rebound, brake catch, hold. What it proves is rig " + "physics and specimen integrity — one shock per drop, no plastic " + "deformation, and the anti-rebound brake catching 150 mm above " + "the stack.\n\n" + "PRESENTER-ONLY: contact and turnaround are adjacent frames, so " + "do not claim the montage resolves the deceleration itself.") + return s + + +def s_instrument_split(prs): + s = title_only(prs) + set_message(s, "The camera measures the bounce; the accelerometer measures " + "the force.") + add_image(s, "fig-video-impact-zoom.png", + (MARGIN, Inches(2.00), Inches(7.5), Inches(4.85))) + textbox(s, Inches(8.35), Inches(2.35), Inches(4.4), Inches(4.3), [ + "Camera → rebound: e* = 0.45, and the structure survives", + "Accelerometer → peak force, energy absorbed, compaction", + "Together they cover the whole event", + ], size=25, space=24) + credit(s, "prc1kn drop 1, 959 fps; TP4 accelerometer at 125 kHz.") + notes(s, "One sentence: the video tells us how much of the drop comes " + "back, the accelerometer tells us what the payload felt. The " + "coefficient of restitution 0.45 is a free reusability metric — " + "it needs no pixel scale.\n\n" + "PRESENTER-ONLY: the objectives come from the accelerometer, not " + "the video. And do not say the two instruments 'agree on the same " + "drop' — the prc1kn videos were shot ~5.5 h before that DAQ " + "campaign, so drop-level pairing is not possible " + "(docs/drop-test-prc1kn-video-analysis.md).") + return s + + +def s_accel_setup(prs): + s = title_only(prs) + set_message(s, "A drop tower with an instrumented payload measures the " + "force that actually reaches the payload.") + textbox(s, MARGIN, BODY_TOP, Inches(12.2), Inches(3.6), [ + "Lansmont M23 tower · Dytran 3133A4 accelerometers", + "TP4 DAQ at 125 kHz — this is the instrument that sees the pulse", + "Standard test: 60 in drop onto 5 felts, repeated per specimen", + ], size=26, space=16) + credit(s, "Setup and standard conditions per the drop-test protocol " + "(PR #86 branch); 25 kHz is the measured floor for the current " + "pipeline (issue #89 sample-rate study).") + notes(s, "Add a photo of the tower here before the talk if one is " + "available; the text is a stand-in, not a design choice.") + return s + + +def s_accel_processing(prs): + s = title_only(prs) + set_message(s, "SAE J211 filtering turns raw ringing into the three " + "numbers the optimizer actually uses.") + add_image(s, "fig-impact-zoom-cfc.png", + (MARGIN, Inches(1.95), SW - 2 * MARGIN, Inches(2.7))) + textbox(s, MARGIN, Inches(4.95), Inches(12.2), Inches(1.9), [ + "Peak transmitted force · specific energy absorbed · compaction " + "efficiency", + ], size=26, color=BLUE) + credit(s, "Raw vs. CFC-1000 vs. CFC-180 on the same impact window.") + notes(s, "Do not explain the filter classes. Say: raw data ring at " + "mounting resonance, the standard filter removes it, and what " + "survives is the objective function.") + return s + + +def s_sensors_lied(prs): + s = title_only(prs) + set_message(s, "Our sensors lied to us first — cross-calibration caught a " + "5% error before it reached the optimizer.") + textbox(s, MARGIN, BODY_TOP, Inches(12.2), Inches(3.6), [ + "A mis-entered sensitivity made channel 5 read 0.953× channel 4.", + "Channel 1 was clipping at the highest drops and we could not see it.", + "Fix: regress every channel against every other, every campaign.", + ], size=25, space=16) + credit(s, "Calibration story documented in PR #74.") + notes(s, "This is the challenge slide Sterling asked to intersperse. It " + "buys enormous credibility: we found our own error, and the " + "cross-check is now routine.") + return s + + +def s_challenges_map(prs): + s = title_only(prs) + set_message(s, "Working slide: challenges and where each one gets told", 28) + textbox(s, MARGIN, BODY_TOP, Inches(12.2), Inches(4.6), [ + "Sensor sensitivity error → cross-calibration → own slide (33)", + "Wrong baseline window → adversarial re-analysis → own slide (34)", + "Camera and DAQ cover different timescales → each gets its own job " + "→ slides 24, 27", + "Supports still removed by hand → painted supports → slide 21", + "Channel clipping at high drops → headroom check → spoken on slide 28", + "Print defects confound specimens → replicates → Q&A backup", + "Cables not in tension → pre-tensioned prints → slide 19", + ], size=20, space=8) + notes(s, "Planning aid. Delete before the talk.") + return s + + +def s_media_shortlist(prs): + s = title_only(prs) + set_message(s, "Working slide: media inventory and what still needs " + "shooting", 28) + textbox(s, MARGIN, BODY_TOP, Inches(12.2), Inches(4.6), [ + "EXTERNAL, EMBEDDED: Titan descent · 2D tensegrity · NASA " + "toy-to-lander (sound) · SUPERball drop", + "OURS, EMBEDDED: print timelapse · support removal · whole-room drop " + "(sound) · phone slow-mo (sound) · 960 fps drop · 7xadt6 snap-back", + "DUPLICATE: the print timelapse also opens the background addendum — " + "keep one copy, not both", + "STILL NEEDED: drop-tower photo, campaign ledger and Pareto-front " + "figures, Colab link for issue #94", + ], size=19, space=10) + notes(s, "Planning aid. Delete before the talk.") + return s + + +def main(): + build_anatomy_figure() + prs = Presentation(str(SRC)) + kept = strip_to_emc_block(prs) + print(f"kept {kept} EMC slides") + + builders = [ + s_readme, s_hook, s_what_is_tensegrity, s_tensegrity_anatomy, + s_toy_to_lander, s_reusable, + s_prior_work, s_gap, + # <- EMC block gets moved in here (position 8) + s_caveat, s_print_timelapse, s_support_removal, + s_drop_room, s_drop_phone, s_video_capture, s_elastic_recovery, + s_video_processing, s_instrument_split, + s_accel_setup, s_accel_processing, + s_pipeline, s_filter_class, s_two_bands, s_sensors_lied, s_adversary, + s_checkable, + s_challenges_map, s_media_shortlist, + ] + for b in builders: + b(prs) + + move_block_after(prs, kept, 8) + prs.save(str(OUT)) + print(f"wrote {OUT} with {len(prs.slides.__iter__.__self__._sldIdLst)} slides") + + +if __name__ == "__main__": + main() diff --git a/presentation/data-catalogue.md b/presentation/data-catalogue.md new file mode 100644 index 00000000..9abb732d --- /dev/null +++ b/presentation/data-catalogue.md @@ -0,0 +1,692 @@ +# Data Catalogue — tensegrity-optimization + +A comprehensive, manually-swept catalogue of where all existing project data lives: every issue comment, every PR comment (including inline review comments), and every file on every branch, as of **2026-07-26**. Compiled for IDETC presentation prep (issue #83 / PR #84). + +Structure: **Part 1** is a quick-reference index organized by data type (start here when hunting an asset for a slide or figure). **Part 2** catalogues each issue/PR thread, asset by asset, with direct links and comment provenance. **Part 3** catalogues committed files per branch (branches that only duplicate `main` are listed at the end). + +⚠️ marks files >5 MB. Comment-attachment URLs (`github.com/user-attachments/...`) require being logged in to GitHub; Box links are public. + +--- + +## Part 1 — Quick reference by data type + +### Slow-motion / drop-test videos +- **Raw high-speed footage (Box, designated home):** https://byu.box.com/s/kkhmvnj9ni19b57dryk3gdroqrp5uf0b (raw unprocessed drops, PR #86) and https://byu.box.com/s/ncvfn5shhg1ignoibg1c3wd03p31p6c2 (100-drop prc1kn campaign videos, PR #86) +- **Committed slow-mo MP4s:** branch `copilot/add-drop-test-protocol-again` — `data/drop-tests/prc1kn-60in-5felt/video/prc1kn_video{1,2}_slomo.mp4` (70.8/85.5 MB, 959 fps Sony + XML metadata) and `data/drop-tests/60in-5felts-validation/{7xadt6,9GMQYQ}_slomo.mp4`; branch `copilot/get-video-drop-test-data` — `data/drop-tests/burn-in-wax/video-example/drop5.mp4` (52 MB, settles the 960 fps HFR container question) +- **~60 slow-mo attachments in PR #67 comments** (vertex/acrylic, clip-height, input-output, key-seat series); 8 first-test videos in issue #36; 10 tuning videos in issue #71; 30 calibration videos in PR #74 +- **YouTube:** drop-test shorts https://youtube.com/shorts/Nab3hfuF4Dw + https://youtube.com/shorts/zkum2JlHpYk (7xadt6 / 9GMQYQ), 500-drop run https://youtu.be/uqn3qnJPfN8, SOP https://youtu.be/dL2djikfJFE, auto-drop setup https://youtu.be/TXerxMYEsDM, TP4 training https://youtu.be/RNjpAmWWmkQ + +### Processed video analysis (figures + scripts) +- Branch `copilot/add-drop-test-protocol-again`: `data/drop-tests/60in-5felts-validation/video/figures/` (kinematics, impact zoom, montages) and `data/drop-tests/prc1kn-60in-5felt/video/figures/` (kinematics, 20 mm grid parallax, drop montages, three-specimen comparison); scripts in `scripts/analysis/` +- Branch `copilot/get-video-drop-test-data`: `data/drop-tests/key-mounted/video-figures/01_centroid_descent_overlay.png` (960 fps centroid tracking) +- Camera/DAQ requirement studies: `analysis/issue-89-*` on the four `claude/issue-89-*` branches (min fps ≈1000 for the ~1.6 ms pulse; 120–240 fps post-impact; sample-rate + record-length + bandwidth studies) + +### Accelerometer data (raw + processed) +- **Committed raw campaigns (~450+ TP4 CSVs + ZIPs):** branch `copilot/add-drop-test-protocol-again` (superset; ~4 GB): `data/drop-tests/{100drops, 200drops, 500drops, 500drops-nobot, 5in-100drops, 5vs10, felt-sheet, drift-calibration(2), ch4-trigger, input-output, key-mounted(-wax), burn-in-wax, vertex-acrylic, 30drops-real, prc1kn-60in-5felt, 7xadt6/9GMQYQ 60in-5felt zips}`; also zipped sets on `main` (`Marcus_1.zip`, `jin_{1,2}.zip`, `prc1kn - set 1 - 1.zip`) +- **Calibration record:** branch `copilot/tune-accelerometers-drop-tower` (PR #74) — 4 raw series + 26 analysis figures; resolves the ~30×/46× sensitivity-entry error; CH5 = 0.953 × CH4; CH1 hard-clipping documented; tri-axis Dytran 3133A4 spec +- **First-ever raw signals:** issue #36 (5 `.txt`, incl. no-specimen control) → analyzed in PR #67 +- **DAQ manuals:** Lansmont TP4 User's/Quick-Start Guides (issue #36 / #89 attachments); M23 tower + Polytec QTec datasheets on branch `copilot/add-drop-tower-picture` + +### Printed-specimen / rig photos +- PR #35 comments: ~70 dated specimen + failure photos (spaghetti → supports → humidity bubbles → accelerometer igloo/key-seat) +- Harvested, provenance-tracked set: `figures/photos/` on branch `copilot/create-manuscript-template` (6 lab photos: drop tower, printed batch, mid-print, slice, CAD render) +- Outreach media pack: PR #41 (composite specimen photo, IMG_6339 high-res, trimmed drop videos) +- Tendon print-angle close-ups: issue #61; joint candidates CAD renders: PR #39; drop-rig build: issue #36 + +### FEA / simulation data + visualizations +- Branch `copilot/explore-simulations-for-tensegrity` (PR #33): 6-engine ladder (MuJoCo/Newton/PyBullet/PyChrono/DiffPD/PolyFEM+IPC), 10 GIF + 10 MP4 drop animations, ~2,600 scored designs (Sobol tiers A/B/C + sim-BO CSVs), Pareto/sensitivity/violin figures, `outputs_bundle.zip` +- CalculiX print-support FEA + tendon-wobble simulation ladder: PR #66 branch `copilot/add-manual-supports` +- Sim-vs-experiment context: Edison trajectories `simulation-bo-value` (CFC-180 parity, co-kriging) and `sobol-t3-results` on the PR #33 branch + +### Bayesian optimization setup + campaign +- **Canonical search space:** `bo/tensegrity_campaign.py` on branch `copilot/scaffold-bayesian-optimization-script` (PR #30) — 13-D (7 continuous + 1 ordered + 5 categorical), objectives/thresholds F_peak ≤ 2500 N, SEA ≥ 0.05 J/g, η ≥ 0.20, qNEHVI +- **Seed batch:** `bo/t3-prism-bo-batch.csv` (9-specimen Sobol, 5 geometric params) + per-specimen STLs on PR #35 branch; search-space figure `bo/t3-prism-search-space.png` on `claude/issue-85-20260717-2257` (issue #85) +- **Ax diagnostic example figures** (LOO-CV, convergence BO-vs-random, Pareto): `figures/examples/` on PR #20/#76 manuscript branches +- Honegumi scaffold: `bo/generate_scaffold.py` (PR #30) + +### CAD / STL / slices (printable artifacts) +- T3 prism lineage: PR #35 branch `cad/t3-prism/` (SCAD, STLs, H2D 3MFs incl. PLA-struts/TPU-cables production slice) + OnShape mirrors (links in PR #35 comments) +- Support systems: PR #66 branch (baked pillars/cages + verification renders/GIFs); reference tensegrity family: 18 STLs on PR #22 branch; joint designs A–F: PR #39 branch; anchor-bulb DOE plate: PR #52 branch + +### MRG proposal + manuscript + abstracts (reusable figures/content) +- MRG proposal: `proposal.pdf` + `figures/overview-updated.png` (closed-loop workflow figure) on `main`; source `.pptx` offered by sgbaird (not yet in repo) +- IDETC abstract: `idetc-abstract.{tex,pdf}` on `main`; JMD manuscript + photos + Ax figures + 813-entry master bib: PR #20/#76 branches; TMS 2027 abstracts: PR #18 (crutch tip), PR #73, issue #78 branches +- Prior-art anchors for the gap argument: Davami et al. 2025 PDF (issue #57 / PR #58 branch), Gu & Dotov tensegrity crutch https://arxiv.org/abs/2605.02710 (PR #41), Edison tensegrity-design survey (50 sources, PR #22), NASA-priorities synthesis (PR #16), `literature/` PDFs on `main` (incl. Khatri & Egan 2024 TPU/ABS honeycomb SEA) + +### Presentation assets (this PR, #84) +- Draft 1 deck + 14 previews, EMC bare template, demo deck, Doumont template/notes, `video-snips.md`, Edison mock-audience reports — all under `presentation/` on `claude/issue-83-20260715-2018` +- Snipped hook videos (Super Ball Bot ×3 + Steve Mould 2D tensegrity 0:35): Box https://byu.box.com/s/g719ao0glalis8l6l2plkq2ztvvh48s4 +- Print timelapse / process YouTube videos (embeddable): manual-support painting https://youtu.be/esYGqPv2fb0, support removal https://youtu.be/V86ctTIHKDY, print short https://youtube.com/shorts/KGSePOjRa_I + +--- + +## Part 2 — By issue / PR thread + +#### #2 — Restructure MRG proposal to match grant format with Edison Scientific-informed revisions (PR, closed) +- **[PDF — compiled proposal]** MRG proposal PDF (11 pp, Edison-revised) — https://github.com/vertical-cloud-lab/byu-mentored-research-tensegrity/blob/03da8e0/proposal.pdf?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/2#issuecomment-4028968463), Copilot, 2026-03-10) +- **[PDF — latexdiff]** proposal-diff.pdf showing changes — https://github.com/vertical-cloud-lab/byu-mentored-research-tensegrity/blob/03da8e0/proposal-diff.pdf?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/2#issuecomment-4028968463), Copilot, 2026-03-10) +- **[PDF — compiled proposal]** proposal.pdf after Edison revision-plan implementation (10 pp) — https://github.com/vertical-cloud-lab/byu-mentored-research-tensegrity/blob/18a2196/proposal.pdf?raw=true (diff: https://github.com/vertical-cloud-lab/byu-mentored-research-tensegrity/blob/18a2196/proposal-diff.pdf?raw=true) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/2#issuecomment-4032207784), Copilot, 2026-03-10) +- **[PDF — compiled proposal]** near-final proposal.pdf shared for PI review — https://github.com/vertical-cloud-lab/byu-mentored-research-tensegrity/blob/0dc9229bb077fb4003ad2f225e04a6e4e83d89f6/proposal.pdf?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/2#issuecomment-4035135236), sgbaird, 2026-03-10) +- **[Edison trajectory ref]** Edison LITERATURE mock review + revision-plan trajectory (task `94f614a2`, retrieved after timeout; informed "tensegrity-inspired" reframing, Pajunen/Ye/Khatri refs, Go/No-Go gate) — no direct URL, described in comments — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/2#issuecomment-4032207784), Copilot, 2026-03-10) + +#### #4 — Shift proposal to experiment-driven approach, disperse mentoring throughout (PR, closed) +- **[PDF — compiled proposal]** experiment-driven MRG proposal for Jeff's review — https://github.com/vertical-cloud-lab/byu-mentored-research-tensegrity/blob/b34bd8a/proposal.pdf?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/4#issuecomment-4048931672), Copilot, 2026-03-12) +- **[PDF — latexdiff]** proposal-diff.pdf (additions/deletions marked) — https://github.com/vertical-cloud-lab/byu-mentored-research-tensegrity/blob/b34bd8a/proposal-diff.pdf?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/4#issuecomment-4048931672), Copilot, 2026-03-12) + +#### #8 — Add mock MRG review committee feedback, update biosketches, and fix budget.tex (PR, closed) +- **[image — rendered biosketch p.10]** compiled biosketch page (Hill) — https://github.com/user-attachments/assets/eba02690-e482-49bb-9e8c-3c150d50399c — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/8#issuecomment-4050725554), Copilot, 2026-03-12) +- **[image — rendered biosketch p.11]** compiled biosketch page (Baird) — https://github.com/user-attachments/assets/0b1761eb-0f66-49fe-b223-e495724c094c — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/8#issuecomment-4050725554), Copilot, 2026-03-12) +- **[PDF — compiled proposal]** proposal.pdf with ScienCV biosketches — https://github.com/vertical-cloud-lab/byu-mentored-research-tensegrity/blob/79a6385/proposal.pdf?raw=true (diff: .../79a6385/proposal-diff.pdf?raw=true) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/8#issuecomment-4050736433), Copilot, 2026-03-12) +- **[PDF — compiled proposal]** proposal.pdf after budget "simulation"→"design" fix — https://github.com/vertical-cloud-lab/byu-mentored-research-tensegrity/blob/c82f76e/proposal.pdf?raw=true — ([review comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/8#discussion_r2928692553), Copilot, 2026-03-13) +- **[repo data files]** `reviews/mock_reviews.md` (3-reviewer mock MRG panel), `sterling-cv/` (CV.tex, mypublications.bib, custom-publications.bib, resume.cls), and Jeff's biosketch source PDF `cv-1605798 _ Hill.pdf` — repo paths on PR branch — ([PR body](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/8), Copilot, 2026-03-12) + +#### #12 — Draft IDETC-CIE 2026 two-page extended abstract for tensegrity BO campaign (PR, closed) +- **[PDF — IDETC extended abstract]** accepted 2-page abstract (BO campaign design: qNEHVI, mixed-variable search space — 4 continuous geometric vars, integer strut count, categorical topology/tiling) — https://github.com/vertical-cloud-lab/byu-mentored-research-tensegrity/blob/165da43/idetc-abstract.pdf?raw=true (earlier 2-col version: .../761555e/idetc-abstract.pdf?raw=true; branch copy: https://github.com/vertical-cloud-lab/byu-mentored-research-tensegrity/blob/copilot%2Fwrite-draft-abstract/idetc-abstract.pdf) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/12#issuecomment-4282894440), Copilot, 2026-04-20) +- **[PDF — latexdiff]** idetc-abstract-diff.pdf — https://github.com/vertical-cloud-lab/byu-mentored-research-tensegrity/blob/165da43/idetc-abstract-diff.pdf?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/12#issuecomment-4282894440), Copilot, 2026-04-20) +- **[CSV — conference reference data]** `idetc-cie-2026-tracks-topics.csv` (IDETC-CIE 2026 track/topic list, used for DAC Topic 10 targeting) — repo path on branch `copilot/write-draft-abstract` — ([PR review](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/12#pullrequestreview-4102035060), copilot-pull-request-reviewer, 2026-04-13) +- **[figure — BO overview]** `figures/overview-updated.png` (MRG overview figure, embedded as Fig 1 wrapfigure) — repo path — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/12#issuecomment-4282697872), Copilot, 2026-04-20) +- **[external — Slack]** tensegrity book link (deliberately not uploaded to repo) — https://verticalcloudlabbyu.slack.com/archives/D0AJJB0KZU0/p1776707491348179 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/12#issuecomment-4283091082), sgbaird-alt, 2026-04-20) +- **[Edison trajectory ref]** PRECEDENT feedback task `243ca39e-868f-4b8d-9411-f3cceef099da` (submitted non-blocking) — no committed artifact linked in this PR — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/12#issuecomment-4282697872), Copilot, 2026-04-20) +> Notable: canonical record of the accepted IDETC-CIE 2026 abstract and the first concrete BO search-space/campaign definition (objectives: peak transmitted force, SEA, compaction efficiency). + +#### #13 — Create a BYU NASA Space Grant application proposal narrative (issue, open) +- **[PDF — application form]** Utah NASA Space Grant fellowship application PDF (committed to branch as `nasa-26.pdf`) — https://engineering.byu.edu/0000019c-fdd3-dc4f-adfe-fff75ee70000/nasa-26-pdf — ([issue body](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/13), sgbaird, 2026-05-07) + +#### #14 — Add NASA Space Grant fellowship proposal — Marcus Madsen, space-focused, Bambu H2D (PR, open) +- **[PDF — proposal]** NASA Space Grant proposal (5 pp, first-person Marcus voice, Bambu H2D campaign) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/4ef778e/nasa-space-grant/proposal.pdf?raw=true (earlier: .../0a13c3c/... and .../00a9d51/... on byu-mentored-research-tensegrity) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/14#issuecomment-4408918207), Copilot, 2026-05-08) +- **[PDF — latexdiff]** nasa-space-grant/proposal-diff.pdf — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/4ef778e/nasa-space-grant/proposal-diff.pdf?raw=true (earlier: https://github.com/vertical-cloud-lab/byu-mentored-research-tensegrity/blob/a0ada34/nasa-space-grant/proposal-diff.pdf?raw=true) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/14#issuecomment-4408918207), Copilot, 2026-05-08) +- **[figure — reused MRG overview]** `../figures/overview-updated.png` wrapfigure (closed-loop print–test–optimize workflow) — repo path — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/14#issuecomment-4403142681), Copilot, 2026-05-08) +- **[Edison analysis ref]** readability ANALYSIS task `3dd7d17c-151a-43dc-869a-c299e545c93e` (proposal.tex/pdf, references.bib, overview-updated.png uploaded) — described in comments — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/14#issuecomment-4408918207), Copilot, 2026-05-08) + +#### #16 — Submit Edison literature query tying tensegrity MRG framework to NASA space priorities (PR, open) +- **[Edison literature synthesis — MD]** ~66k-char NASA-priorities literature review (task `97002b89`, 10 sections: NASA tensegrity heritage, landing-load attenuation benchmarks, in-space AM, PLA/TPU space environment, BO for aerospace lattices, mission framings) — https://github.com/vertical-cloud-lab/byu-mentored-research-tensegrity/blob/8a83c92/edison-responses/2026-05-08-nasa-priorities-literature-97002b89.md?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/16#issuecomment-4402864540), Copilot, 2026-05-08) + +#### #17 — Explore potential application for impact absorption for crutches (issue, open) +- **[external — Claude transcript]** brainstorming conversation where the shock-absorbing crutch idea originated — https://claude.ai/share/c7bea94b-9655-4cde-8027-1449abb04b07 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/17#issuecomment-4408580895), Jeffrayhill1, 2026-05-08) + +#### #18 — Explore tensegrity crutch-tip impact-absorber and industry partners via Edison literature queries (PR, open) +- **[Edison trajectories 01–04 — MD+JSON]** committed under `edison-trajectories/`: 01 tensegrity crutch-tip feasibility (task `39708fbc`, ~38k chars), 02 medical motivation + prior art (`9832f01a`, ~54k), 03 vibration/economic burden/slip standards (`f21cf79c`), 04 TPU+PETG engineering + BO design space (`7a21d00e`); each with full `formatted_answer` + `model_dump_json()` — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/18#issuecomment-4408874719), Copilot, 2026-05-08); platform links: https://platform.edisonscientific.com/tasks/39708fbc-5964-4fb5-a042-9b13b3475d40 and .../9832f01a-6bb9-4488-bd88-3131d915f96a ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/18#issuecomment-4408576429), sgbaird-alt, 2026-05-08) +- **[Edison trajectory 05 — placeholder]** industry-partner/commercialization query (task `c18a2313`, Copilot-account-locked, still placeholder) — `edison-trajectories/05-industry-partners-and-commercialization.md` — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/18#issuecomment-4411452108), Copilot, 2026-05-09) +- **[abstract — MD]** TMS 2027 crutch-tip abstract (150 words, iteratively Edison-revised) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/7a06a8b/crutch-tip-abstract.md (earlier revs at commits `03b9835`, `61e756a`, `3c58e75`, `5044b18`) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/18#issuecomment-4860817630), claude[bot], 2026-07-01) +- **[Edison trajectory 06 — MD+JSON]** abstract fact-check review (task `74ac013b`, ~34k chars) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/1be2458/edison-trajectories/06-abstract-feedback.md — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/18#issuecomment-4859722224), claude[bot], 2026-07-01) +- **[Edison trajectories 07–08 — MD+JSON]** 07 ferrule envelope / SEA benchmarks (1–8 J/g) / 21 CFR 890.3790 regulatory (task `98a30884`); 08 PETG–TPU interface fatigue / slip resistance / vibration (task `46e06bf8`) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/183a797/edison-trajectories/07-ferrule-envelope-quantitative-benchmarks-regulatory.md and .../08-interface-fatigue-slip-resistance-vibration.md — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/18#issuecomment-4860030619), claude[bot], 2026-07-01) +- **[Edison trajectory 09 — MD+JSON]** TMS organizer-persona mock review (task `6e00f3ca`, ~36.7k chars) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/61e756a/edison-trajectories/09-organizer-persona-mock-review.md — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/18#issuecomment-4860593344), claude[bot], 2026-07-01) +- **[Edison trajectories 10–13 — MD+JSON]** four low-effort per-organizer mock reviews (Bandyopadhyay `5b6de9f1`, Sachdev `8c0ea7de`, Rodgers `39a29dbf`, Bose `13c4f31b`) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/5044b18/edison-trajectories/10-mock-review-bandyopadhyay.md (…`11-mock-review-sachdev.md`, `12-mock-review-rodgers.md`, `13-mock-review-bose.md`) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/18#issuecomment-4860692173), claude[bot], 2026-07-01) +- **[PDF — external symposium flyer]** TMS 2027 Biomedical Materials and Devices CFA flyer — https://www.tms.org/tms2027/downloads/flyers/TMS2027-CFA-Flyer-017.pdf — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/18#issuecomment-4859922439), sgbaird, 2026-07-01) +> Notable: the repo's largest Edison literature trove — 13 committed trajectories (~200k+ chars of cited synthesis) covering crutch-tip feasibility, medical motivation, TPU/PETG engineering data, regulatory pathway, and commercialization, plus the evolving TMS 2027 abstract. + +#### #20 — Populate ASME JMD manuscript draft from repo context (PR, closed) +- **[PDF — manuscript builds]** `manuscript.pdf` (clean), `manuscript-todos.pdf` (todonotes+listoftodos), `manuscript-diff.pdf` — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/9de8a4a/manuscript/manuscript.pdf?raw=true (later rebuilds at `6791546`) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/20#issuecomment-4408575088), Copilot, 2026-05-08) +- **[image — preview]** side-by-side page-1 preview (clean vs todos) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/9de8a4a/manuscript/preview/manuscript-versions.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/20#issuecomment-4408575088), Copilot, 2026-05-08) +- **[BibTeX — curated]** `manuscript/references.bib` (62 entries incl. 27 crutch-tip prior-art refs parsed from PR #18 trajectories) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/9de8a4a/manuscript/references.bib — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/20#issuecomment-4408575088), Copilot, 2026-05-08) +- **[BibTeX — master library]** `manuscript/references-full.bib` — 813 unique refs from 38 Edison tasks across 34+ branches (604 DOIs, 393 abstracts after Crossref verification pass) + extractor `scripts/edison/build_master_bib.py` and verifier `scripts/edison/verify_bib_dois.py` — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/b1100d8/manuscript/references-full.bib — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/20#issuecomment-4633934179), Copilot, 2026-06-05; verification: [comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/20#issuecomment-4634714047), 2026-06-05) +- **[Edison mock review 1 — MD+JSON]** JMD 3-reviewer + AE mock review (task `6c140449`, verdict Reject-and-Resubmit) + triage — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/639a15d/edison-trajectories/2026-05-09-mock-jmd-review-6c140449.md and .../reviews/mock-jmd-review-analysis.md — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/20#issuecomment-4411367872), Copilot, 2026-05-09) +- **[figure — mechanistic drop-test example]** synthetic-but-physics-anchored deceleration-curve figure (125 kHz raw, SAE J211 CFC-180, control ≈1792 G vs tensegrity ≈370–463 G ⇒ ~74–79% reduction, per issue #36 drop-test data) + generator script — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/a138300/figures/examples/mechanistic-data-figure-example.png?raw=true (impulse-consistent revision: .../93d14c7/...) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/20#issuecomment-4664784933), Copilot, 2026-06-09; revised [comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/20#issuecomment-4673786958), 2026-06-10) +- **[Edison analysis — figure feedback]** ANALYSIS task `e0c4e062` critique of the mechanistic figure (caught impulse-inconsistency bug) — `edison-trajectories/mechfig-feedback/mechfig-feedback-e0c4e062-….md/.json` at commit `ab0d457` — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/20#issuecomment-4664958219), Copilot, 2026-06-09) +- **[figures — real Ax BO diagnostics]** LOO-CV, parameter-sensitivity, convergence (BO vs random), Pareto-front example figures from a real Ax/BoTorch loop (SAASBO/qNEHVI, categorical cable diameter {1.2,1.8,2.4,3.0,4.5} mm) on synthetic objectives + generator — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/5fd41ef/figures/examples/ax-placeholder-figures-contact-sheet.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/20#issuecomment-4673681822), Copilot, 2026-06-10) +- **[photos — printed specimens/rig, harvested]** `figures/photos/` — six real photos extracted from repo comment history with provenance table (OpenSCAD T3-prism render: PR #35 c4513151049; Bambu Studio dual-nozzle slice: PR #35 c4464541324; H2D mid-print: PR #35 c4519769283; as-printed T3 prism: PR #35 c4634008108; bungee drop tower: PR #36 c4509083060) + extractor `scripts/figures/fetch_repo_photos.py`; preview: https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/21186c7/preview/manuscript-real-figures.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/20#issuecomment-4677758892), Copilot, 2026-06-11) +- **[images — placeholder-slot screenshots]** sgbaird's screenshots of empty Fig 3 / Fig 2 slots — https://github.com/user-attachments/assets/7a69aba0-3077-4ee1-9db7-834669507fb8 and https://github.com/user-attachments/assets/49afad02-d484-40f7-a1ab-e998c2d09fbb — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/20#issuecomment-4677526117), sgbaird, 2026-06-11) +- **[Edison mock review 2 — MD+JSON]** second JMD panel (task `3fde560e`, verdict improved to Major Revision) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/906807c/edison-trajectories/mock-jmd-review-2/mock-jmd-review-2-3fde560e-1bb9-4c6b-8fcc-eaeef4570bf4.md — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/20#issuecomment-4686510817), Copilot, 2026-06-12) +- **[Edison bib-verification set]** `edison-trajectories/bib-doi-verification/needs-list.md` (6 wrong/404 DOIs + ~210 DOI-less entries; follow-up task `dbd490f6`) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/90ba846/edison-trajectories/bib-doi-verification/needs-list.md — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/20#issuecomment-4634714047), Copilot, 2026-06-05) +- **[script — BO search space]** T3-prism Sobol batch BO script defining the actual campaign search space — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/65d0d3f2b1d673f74755e1c8900af5af2500fc53/bo/t3_prism_sobol_batch.py — ([review comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/20#discussion_r3383480632), sgbaird, 2026-06-09) +- **[reference — external methods template]** Ye et al. 2023 multimaterial PLA/TPU print-parameter methods quoted verbatim as template for the fabrication section — https://www.nature.com/articles/s41467-023-37343-w — ([review comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/20#discussion_r3383632684), sgbaird, 2026-06-09) +> Notable: the manuscript hub — consolidates nearly every data stream in the repo (813-entry master bibliography from 38 Edison tasks, harvested specimen/drop-tower photos, drop-test accelerometer benchmarks from issue #36, real Ax BO diagnostic figures, and two full mock peer-review trajectories); work continued in issue #75 / PR #76 after Copilot errors. + +#### #22 — Add reference STL models for canonical tensegrity structures + Edison literature surveys (PR, open) +- **[render/figure]** 3-panel preview render of T3/T4/icosahedron STL models (thin cables) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/3ffd5e6d158223a4818c60abc0b91ee7c6d8bb93/figures/tensegrity_models_preview.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/22#issuecomment-4408909142), sgbaird-alt, 2026-05-08) +- **[render/figure]** Regenerated preview with 2.4 mm TPU-thickness cables (matches `cad/t3-prism/` from PR #35) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/7cc4533/figures/tensegrity_models_preview.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/22#issuecomment-4408922198), Copilot, 2026-05-08) +- **[render/figure]** 6-panel preview adding T6 prism, stacked T3 column, truncated-octahedron cell — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/df60ac4/figures/tensegrity_models_preview.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/22#issuecomment-4411480016), Copilot, 2026-05-09) +- **[STL models]** Parametric STLs incl. `models/stl/6bar_prism.stl`, `stacked_t3_column.stl`, `truncated_octahedron.stl` (generator `models/generate_stl.py`) — https://github.com/vertical-cloud-lab/tensegrity-optimization/tree/df60ac4/models/stl — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/22#issuecomment-4411480016), Copilot, 2026-05-09) +- **[Edison survey]** Tensegrity-designs literature survey (50 sources; task `fad054b3`), md + references + json — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/df60ac4/edison-trajectories/2026-05-09-tensegrity-designs-fad054b3.md — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/22#issuecomment-4411480016), Copilot, 2026-05-09) +- **[render/figure]** Extended preview of 7 additional design families (Geiger dome, biotensegrity spine, SUPERball, Tibert/Pellegrino mast, patent antenna, bistable double-prism, cuboctahedron tessellation) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/fd32935/figures/tensegrity_models_extended_preview.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/22#issuecomment-4412543233), Copilot, 2026-05-09) +- **[STL models]** 7 additional family STLs: `geiger_cable_dome.stl`, `biotensegrity_spine.stl`, `superball_with_payload.stl`, `tibert_pellegrino_mast.stl`, `patent_us6441801_antenna.stl`, `bistable_double_prism.stl`, `cuboctahedron_tessellation.stl` — https://github.com/vertical-cloud-lab/tensegrity-optimization/tree/fd32935/models/stl — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/22#issuecomment-4412543233), Copilot, 2026-05-09) +- **[Edison survey]** Design-gaps follow-up (18 missing families; task `6226a551`), md/refs/json + submit script — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/119d9c6/edison-trajectories/2026-05-12-tensegrity-design-gaps-6226a551-b46a-49b4-936a-bca600cd8d30.md — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/22#issuecomment-4427443930), Copilot, 2026-05-12) +- **[external/Slack PDF]** "Floating fabric over Georgia Dome" (Civ. Eng. ASCE 1991) paper PDF shared on Slack — https://verticalcloudlabbyu.slack.com/archives/C0B2MFR5V3L/p1778691467813409 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/22#issuecomment-4443492056), ctrhjk, 2026-05-13) +> Notable: primary source of all reference tensegrity geometry (13 STL families + preview renders) and the two Edison design-space surveys that feed PR #24/#30. + +#### #24 — Explore TPU+PLA BO design variables and base structures (PR, open) +- **[Edison survey]** TPU+PETG BO-variables literature result (task `5ae24eaf`), verbatim md + full json — repo paths `edison-trajectories/tpu-petg-bo-variables-5ae24eaf-5b6e-45cf-9f6c-1c7fbd881738.{md,json}` (committed in 3478122) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/24#issuecomment-4408896622), Copilot, 2026-05-08) +- **[BO search-space doc]** PR #22 design-gaps synthesis + Ax `HierarchicalSearchSpace` spec (§D) — repo path `edison-trajectories/tpu-petg-bo-variables-additions-from-pr22.md` (commit 39a2e7c) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/24#issuecomment-4432760248), Copilot, 2026-05-12) + +#### #26 — Brainstorm project + repo names; Edison rounds 1 & 2 (PR, open) +- **[Edison trajectory]** Naming round-1 brainstorm (task `60470477`), md + json under `edison-trajectories/` — https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/26 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/26#issuecomment-4408930862), Copilot, 2026-05-08) +- **[Edison trajectory]** Naming round-2 venues/professionalism critique (task `6d3434cb`) — repo paths `edison-trajectories/2026-05-09-naming-followup-6d3434cb-1f29-446c-9112-8c6c3144830a.{md,json}` — ([PR body](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/26), Copilot, 2026-05-09) + +#### #27 — Picture of drop tower from Jeff (issue, open) +- **[equipment PDF]** Lansmont Model 23 Shock Test System brochure ("drop tower") attached by Jeff — https://github.com/user-attachments/files/27532742/Lansmont_M23_Shock.pdf — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/27#issuecomment-4408498939), Jeffrayhill1, 2026-05-08) +- **[YouTube video]** Drop-tower data-collection process walkthrough (timestamped 4:31; also referenced from issue #36) — https://youtu.be/RNjpAmWWmkQ?si=vzIhtzIMCsPZY0CF&t=271 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/27#issuecomment-4461766243), sgbaird-alt, 2026-05-15) + +#### #28 — Document Lansmont M23 drop tower and Polytec QTec laser vibrometer (PR, open) +- **[equipment PDFs]** `equipment/lansmont-m23/Lansmont_M23_Shock.pdf` (Jeff's brochure) + `M23_Data_Sheet_official.pdf` (lansmont.com); `equipment/polytec-qtec/Polytec_Datasheet_VibroFlex_QTec.pdf` — https://github.com/vertical-cloud-lab/tensegrity-optimization/tree/94fd831/equipment — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/28#issuecomment-4408560233), Copilot, 2026-05-08) +- **[equipment photos]** Vendor stock images: `equipment/lansmont-m23/images/m23_lansmont_datasheet_photo.png` + six Polytec images (VFX-I-160 head, VFX-F-110 front end, VibroFlex family, QTec SNR diagram, long-range head, datasheet hero) with `SOURCES.md` provenance — https://github.com/vertical-cloud-lab/tensegrity-optimization/tree/05b6344/equipment — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/28#issuecomment-4411371855), Copilot, 2026-05-09) +- **[Edison survey]** M23 + QTec instrument literature survey (task `1a0f4a70`; 9 M23 + 6 QTec publications; novelty gap: no prior M23+LDV combo) — repo paths `edison-trajectories/2026-05-08-equipment-m23-qtec-1a0f4a70.{md,json}` + `equipment/*/literature.md` — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/28#issuecomment-4411371855), Copilot, 2026-05-09) + +#### #30 — Scaffold Bayesian-optimization script with honegumi + customized tensegrity campaign (PR, open) +- **[proposal PDF]** MRG undergraduate mentoring grant proposal (variables/objectives source) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/main/proposal.pdf — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/30#issuecomment-4408882823), sgbaird, 2026-05-08) +- **[abstract PDF]** IDETC 2026 accepted abstract — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/copilot/write-draft-abstract/idetc-abstract.pdf — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/30#issuecomment-4408882823), sgbaird, 2026-05-08) +- **[proposal PDF]** BYU NASA Space Grant application — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/copilot/create-latex-template-proposal/nasa-space-grant/proposal.pdf — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/30#issuecomment-4408882823), sgbaird, 2026-05-08) +- **[BO campaign code]** Canonical 13-D search space + qNEHVI objectives — `bo/tensegrity_campaign.py` (`PARAMETERS` L117–L219, `OBJECTIVES` L221–L225) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/834ca15/bo/tensegrity_campaign.py — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/30#issuecomment-5008175231), claude[bot], 2026-07-17) +- **[BO scaffold code]** honegumi generator + rendered Ax loop + tests — `bo/generate_scaffold.py`, `bo/tensegrity_bo.py`, `bo/requirements.txt`, `bo/tests/`; run emits `bo/campaign_pareto.png` (gitignored) — https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/30 — ([PR body](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/30), Copilot, 2026-05-08) +> Notable: single source of truth for the BO campaign setup (7 continuous + 1 integer + 5 categorical parameters, 3 objectives with hypervolume thresholds, no constraints yet); PETG+TPU bounds traced to PR #24's Edison table. + +#### #32 — Begin exploring state-of-the-art simulations for tensegrity (issue, open) +- **[YouTube video]** Highly relevant tensegrity simulation videos compilation (timestamped 0:35) — https://www.youtube.com/watch?v=0eC4A2PXM-U (https://youtu.be/0eC4A2PXM-U?si=TehhmhPOYeN7Wlkr&t=35) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/32#issuecomment-4547614259), sgbaird, 2026-05-26) + +#### #33 — Add runnable tensegrity simulation demos + 3D animated renders + Edison survey + Sobol T3-prism sweeps (PR, open) +- **[sim plots]** Crutch-tip + NASA-lander regime timeseries and k-sweep plots (`regime_{crutch_tip,nasa_lander}_{timeseries,sweep}.png` + CSVs) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/ce1edba/simulations/outputs/regime_crutch_tip_timeseries.png?raw=true (et al.) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4409015350), Copilot, 2026-05-08) +- **[Edison survey]** Simulation-stack survey (task `782657e0`; PolyFEM+IPC / DiffPD / MuJoCo 3-tier recommendation) — `edison-trajectories/2026-05-08-sim-survey-782657e0.{md,json}` — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4409015350), Copilot, 2026-05-08) +- **[sim plots]** Printable-design (PETG/PLA strut + TPU) heatmap + Pareto sweeps per regime — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/5031a67/simulations/outputs/regime_crutch_tip_printable_heatmap.png?raw=true (+ `_pareto`, both regimes) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4409062085), Copilot, 2026-05-08) +- **[sim plots]** Newton/Warp XPBD drop + tendon-diameter sweep (TPU 85A) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/065cc62/simulations/outputs/newton_drop.png?raw=true and `newton_tendon_sweep.png` — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4411470661), Copilot, 2026-05-09) +- **[FEA plot]** PolyFEM+IPC source-build cube-drop smoke test — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/80d52f2/simulations/outputs/polyfem_drop.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4427449251), Copilot, 2026-05-12) +- **[3D animations]** MuJoCo drop GIFs with strain-coloured tendons (red=tensioned/blue=slack): `mujoco_drop.gif`, `regime_crutch_tip_drop.gif`, `regime_nasa_lander_drop.gif` (+ MP4 counterparts in `simulations/outputs/`) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/ec778ad/simulations/outputs/mujoco_drop.gif?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4427518087), Copilot, 2026-05-12) +- **[3D animations]** Debugged regime renders + spot-checks (`spotcheck_bare_prism.gif`, `spotcheck_suspended_plate.gif`, fixed floor-penetration) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/ffa7301/simulations/outputs/spotcheck_bare_prism.gif?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4427907951), Copilot, 2026-05-12) +- **[3D animations]** Fixed crutch/lander drop GIFs (axial-load payload model, stiffened contact) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/b8c1aa9/simulations/outputs/regime_crutch_tip_drop.gif?raw=true and `regime_nasa_lander_drop.gif` — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4462079160), Copilot, 2026-05-15) +- **[Edison survey]** Payload-vs-no-payload modeling clarification (task `37ae0665`) — `edison-trajectories/payload-vs-no-payload-37ae0665-*` — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4462805615), Copilot, 2026-05-15) +- **[sim plots]** PLA-update regeneration: 20 files under `simulations/outputs/` incl. refreshed `newton_tendon_sweep.png`, printable heatmaps — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/761a49b/simulations/outputs/newton_tendon_sweep.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4464705363), Copilot, 2026-05-16) +- **[FEA plot]** Welded PLA/TPU T-prism PolyFEM+IPC drop (gmsh mesh 5,481 tets / 2,168 nodes via `simulations/tprism_mesh.py`) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/124bba2/simulations/outputs/polyfem_drop_tprism.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4464919575), Copilot, 2026-05-16) +- **[Edison brief]** Modeling-feedback contact list (task `78fb09a2`) — `edison-trajectories/modeling-feedback-contacts/` — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4500909142), Copilot, 2026-05-20) +- **[Edison analysis]** Simulation-BO value analysis (task `4e74f66c`; CFC-180 parity, co-kriging, qNEHVI recs) — `edison-trajectories/simulation-bo-value/` — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4663641407), Copilot, 2026-06-09) +- **[data bundle]** `simulations/outputs_bundle.zip` (11→25 MB archive of the full `simulations/outputs/` set, refreshed repeatedly) — repo path `simulations/outputs_bundle.zip` — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4686649573), Copilot, 2026-06-12) +- **[campaign data + plots]** Sobol T3-prism sweep over PR #35 box: `sobol_t3_tierC.csv` (512 designs × 2 regimes), `sobol_t3_tierB.csv` (32 Newton designs), Pareto fronts, Spearman sensitivity heatmap, Tier-C-vs-Tier-B scatter (ρ=+0.74) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/33fdf25/simulations/outputs/sobol_t3_pareto.png?raw=true (+ `_sensitivity`, `_tierC_vs_tierB`) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4687097105), Copilot, 2026-06-12) +- **[campaign data + plot]** Full C→B→A 5-engine ladder: `sobol_t3_tierA.csv` (PolyFEM, 8), `sobol_t3_pybullet.csv` (32), `sobol_t3_pychrono.csv` (16), engine-ladder Spearman figure + `sobol_t3_tierA.png` — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/f8c5c49/simulations/outputs/sobol_t3_engine_ladder.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4687883210), Copilot, 2026-06-12) +- **[violin plots]** Jittered raw-point violins (PNG + interactive HTML): `sobol_t3_violin_objectives.{png,html}`, `sobol_t3_violin_engines.{png,html}` — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/5598733/simulations/outputs/sobol_t3_violin_objectives.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4700396029), Copilot, 2026-06-14) +- **[Edison analysis]** Sobol-T3 results mock review (task `ff8faab3`; re-analysed the CSVs, artifact-vs-physics flags) — `edison-trajectories/sobol-t3-results/` — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4713166938), Copilot, 2026-06-15) +- **[diagnostics data + plot]** Artifact-ablation diagnostics: `sobol_t3_diag_{base_reaction,cfc,constmass}.csv`, 3-panel `sobol_t3_diagnostics.png`, write-up `sobol_t3_diagnostics.md` — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/2c85d19/simulations/outputs/sobol_t3_diagnostics.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4756552196), Copilot, 2026-06-20) +- **[BO campaign plots]** Sim-only closed-loop BO (qNEHVI, mirrors PR #35): initial `sim_bo_convergence.png` + `sim_bo_pareto.png` — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/59c48d5/simulations/outputs/sim_bo_convergence.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4759606831), Copilot, 2026-06-20) +- **[BO campaign data + plots]** Per-(tier,regime,seed) reworked outputs: `sim_bo__.csv`, `_pareto.csv`, mean±σ convergence, per-seed LOO-CV figures, `sim_bo_campaign.md` — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/069b3ea/simulations/outputs/sim_bo_C_crutch_convergence.png?raw=true (+ `sim_bo_B_lander_convergence.png`, `sim_bo_C_crutch_seed0_cv.png`) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4759997844), Copilot, 2026-06-20) +- **[Edison analysis]** Sim-BO mock-reviewer feedback (task `491f90ae`; Tier-B regime-blindness fix followed) — `edison-trajectories/sim-bo-review/` — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4760748348), Copilot, 2026-06-21) +- **[Pareto renders + animations]** Dense 2048-design/regime Pareto mapping with 3D render callouts: `pareto_{crutch,lander}_annotated.png`, per-pick stills, `pareto__{best,worst}_drop.{gif,mp4}`, `pareto_.csv` — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/bb904cd/simulations/outputs/pareto_crutch_annotated.png?raw=true (+ `pareto_crutch_best_drop.gif`, `pareto_crutch_worst_drop.gif`, `pareto_lander_annotated.png`) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4760916629), Copilot, 2026-06-21) +- **[Edison analysis]** Fair-evaluation (mass/volume/footprint confound) mock review (task `e43abed6`) — `edison-trajectories/fair-evaluation/` + `simulations/fair_evaluation_analysis.md` — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/33#issuecomment-4760973103), Copilot, 2026-06-21) +> Notable: the repo's largest data trove — full multi-engine simulation ladder (MuJoCo/Newton/PyBullet/PyChrono/DiffPD/PolyFEM+IPC) with drop-test GIF/MP4 animations, ~2,600+ scored designs across Sobol/BO campaign CSVs, seven Edison trajectories, and the `outputs_bundle.zip` archive; feeds directly into PR #35 hardware BO via `bo_evaluator.py`. + +#### #34 — Get a bambu sliced print for a T3-prism (issue, open) +- **[reference image]** T3-prism annotated diagram (green struts / red cables, from Wikipedia Tensegrity article) — https://github.com/user-attachments/assets/7d4a9cff-f1a0-4c2b-8f6e-aa40a2c904dc — ([issue body](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/34), sgbaird-alt, 2026-05-08) + +#### #35 — Add T3-prism (3-strut tensegrity) parametric CAD with Bambu PETG `.gcode.3mf` slice + re-importable project `.3mf` + MM PLA/TPU variants (PR, open) + +- **[repo CAD/slice artifacts]** Core parametric CAD + slice tree, repeatedly regenerated across the PR: `cad/t3-prism/t3-prism.scad`, `t3-prism{,-struts,-cables,-struts-scaffold}.stl`, `t3-prism-iso.png`, `render_print.sh`, `flatten_bambu_profile.py`, `patch_mm_extruder.py`, `render_supports.py`, and `cad/t3-prism/slices/t3-prism.H2D{,-PETG,-PETG.gcode,-MM,-MM-PLAcables,-MM-PLAstruts-TPUcables}.3mf` — https://github.com/vertical-cloud-lab/tensegrity-optimization/tree/copilot/get-bambu-sliced-print-t3-prism/cad/t3-prism — ([PR body](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35), Copilot, 2026-05-08) +- **[3D slice files]** First H2D/X1C/A1mini PETG `.gcode.3mf` slices (X1C/A1mini later dropped, H2D-only) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/a8a38ef/cad/t3-prism/slices/t3-prism.H2D-PETG.gcode.3mf?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4408746118), Copilot, 2026-05-08) +- **[3D project file]** Marcus's hand-made Bambu project used for the first successful print — repo path `cad/t3-prism/t3-prism.3mf` (a8a38ef) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4408746118), Copilot, 2026-05-08) +- **[photos]** First in-progress print (printer cam) + Bambu "no geometry data" import-error screenshot (2 images) — https://github.com/user-attachments/assets/15d67245-d665-44df-a1a1-510424628b62 , https://github.com/user-attachments/assets/72d360f5-220d-4506-bbf3-0bdee17a2382 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4408792220), sgbaird-alt, 2026-05-08) +- **[Edison trajectory]** Edison ANALYSIS task 25c1c897 (import-error + bridge-failure diagnosis), committed as `edison-trajectories/2026-05-08-t3-prism-bambu-import-25c1c897.{md,json}` — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/21fd799/edison-trajectories/2026-05-08-t3-prism-bambu-import-25c1c897.md — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4409435497), Copilot, 2026-05-08) +- **[photo]** PETG spaghetti print failure (top-cable bridge failure mode) — https://github.com/user-attachments/assets/e115d2e1-1750-4612-9251-ac6ff5f3a194 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4409344661), sgbaird-alt, 2026-05-08) +- **[specimen photos]** Failed vs successful PETG prints side-by-side + close-up of "waving" strings (2 photos) — https://github.com/user-attachments/assets/ec7b7977-ae6a-4826-9398-eb11e566c36d , https://github.com/user-attachments/assets/3a61443b-f1bc-4ecf-88bf-601c85c0ab0f — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4426810002), me-madsen, 2026-05-12) +- **[screenshots]** Bambu auto-support comparison at 1.0× vs 1.3× scale (2 screenshots; `t3-prism1.1` 3mf also pushed to branch) — https://github.com/user-attachments/assets/d684af50-a170-43eb-be9e-137c8e581d7e , https://github.com/user-attachments/assets/de203464-5933-4392-9890-42e0f3bd6a12 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4432773923), me-madsen, 2026-05-12) +- **[3D project files]** MM filament-swap variants: `H2D-MM.3mf` (PLA struts/PETG cables), `H2D-MM-PLAcables.3mf`, and production `H2D-MM-PLAstruts-TPUcables.3mf` — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/57baf6b/cad/t3-prism/slices/t3-prism.H2D-MM-PLAstruts-TPUcables.3mf?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4456003473), Copilot, 2026-05-13/15) +- **[render/plot]** G-code support-verification renders `t3-prism.H2D-PETG-supports.png` (native tree supports; two generations) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/764b34e/cad/t3-prism/t3-prism.H2D-PETG-supports.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4462488082), Copilot, 2026-05-15) +- **[render]** Modeled-in PLA scaffold (42 pillars) iso render `t3-prism-iso-with-scaffold.png` — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/5437366/cad/t3-prism/t3-prism-iso-with-scaffold.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4464341507), Copilot, 2026-05-15) +- **[screenshots]** Bambu error banner + started MM print screenshots (2) — https://github.com/user-attachments/assets/b3962ab7-b208-46ac-bded-a5844e7a93f9 , https://github.com/user-attachments/assets/9b03f81c-7343-4fcd-a027-d2a435c68f74 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4464541324), sgbaird-alt, 2026-05-15) +- **[photo]** Over-painted supports (too much mass) on TPU tendons — https://github.com/user-attachments/assets/6b20a240-1da4-47e8-b4a0-9f325e0408bb — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4502096572), sgbaird porting achris0520, 2026-05-20) +- **[specimen photos]** First manually-painted-support PLA/TPU tensegrity print, TPU encased in supports (batch of 7 photos) — https://github.com/user-attachments/assets/1daf0a52-274b-4d26-97fe-78fcdcbeb939 (+ 90f4c169, 1a6b7199, a39997b8, 924b69ad, 6496625d, b96b3b18) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4502126554), sgbaird porting ctrhjk, 2026-05-20) +- **[photos/screenshots]** Thin-painted-support breakthrough: paint-from-bottom screenshots + first fully successful PLA/TPU print photo (3 images) — https://github.com/user-attachments/assets/d0f270b1-6c5a-46dd-abc8-c77c7ba74f15 , https://github.com/user-attachments/assets/48e8f918-454d-4196-b311-945f99e302bf , https://github.com/user-attachments/assets/789e75e9-bb51-4561-ad57-ae6b84e8b65c — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4502140147), sgbaird porting achris0520, 2026-05-20) +- **[video + YouTube + ZIPs]** Part-flexing video clip (https://github.com/user-attachments/assets/44a991b7-1c2a-42a7-8fcf-641e2006b06b); 12-min manual-support-painting walkthrough https://youtu.be/esYGqPv2fb0; sliced project ZIP https://github.com/user-attachments/files/28076463/t3-prism-pla-tpu-sgb-manual-support-paint.zip; starting-STL ZIP https://github.com/user-attachments/files/28076471/t3-prism-separate-assembly.zip (also mirrored as https://github.com/user-attachments/files/28076134/t3-prism-separate-assembly.zip) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4502171087), sgbaird, 2026-05-20) +- **[BO batch dataset]** Sobol batch-1 generator + design table + plate artifacts: `bo/t3_prism_sobol_batch.py`, `bo/t3-prism-bo-batch.{csv,json,scad,stl}`, `bo/t3-prism-bo-batch-{struts,cables}.stl`, `bo/per-specimen-stls/` (18 STLs), `bo/slices/t3-prism-bo-batch.H2D-MM-PLAstruts-TPUcables.3mf` — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/7d98451/bo/t3-prism-bo-batch.csv — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4503182316), Copilot, 2026-05-20) +- **[plots]** BO batch plate/iso preview PNGs, regenerated across iterations (7d98451, 88978ae, 0abc0b2, 6740366, 482b62e, dfc8729) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/6740366/bo/t3-prism-bo-batch-plate.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4513576369), Copilot, 2026-05-20/22) +- **[screenshots]** BO batch imported into Bambu Studio (spacing check, 2 screenshots) — https://github.com/user-attachments/assets/14f1a14d-152d-46d8-b229-1cca7392f294 , https://github.com/user-attachments/assets/0d13673d-aea8-4bd7-9693-05169c35b0bb — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4503427854), sgbaird, 2026-05-20) +- **[renders]** Captive-core joint iso renders + STL links (d4431b0, dfc8729) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/dfc8729/cad/t3-prism/t3-prism-iso.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4513151049), Copilot, 2026-05-21/22) +- **[screenshots]** 15-hr 9-specimen batch print in progress: printer cam + Audrey's plate screenshot (2) — https://github.com/user-attachments/assets/fae2c851-09d3-40ae-946c-8770fafdd387 , https://github.com/user-attachments/assets/77bbd2c9-2301-4010-9884-c830d8ca5e5d — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4519769283), sgbaird, 2026-05-22) +- **[gcode ZIP + SOP]** The actual manually-sliced production g-code + written manual-support-painting SOP — https://github.com/user-attachments/files/28156629/t3-prism.H2D-MM-PLAstruts-TPUcables_ORIGINAL_MANUAL_SLICE.gcode.zip — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4520949470), achris0520, 2026-05-22) +- **[photo]** Badly stringing batch specimen — https://github.com/user-attachments/assets/24d4989d-7840-4789-b44f-4ea5775c91da — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4521380707), sgbaird, 2026-05-22) +- **[YouTube]** Support-removal video + specimen ID scheme (`yqpmx1`/`m6cyoq`/`n0jdwk`) — https://youtu.be/V86ctTIHKDY — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4522905787), sgbaird, 2026-05-22) +- **[specimen photos]** Three printed batch-1 specimens (3 phone photos) — https://github.com/user-attachments/assets/c2ee2b2d-db35-40d6-8122-78aa6196b94e (+ 37073bb8, 547f3657) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4634008108), me-madsen, 2026-06-05) +- **[specimen photos]** PLA-only supports fused too close, IMG_6062–6064 (3 photos) — https://github.com/user-attachments/assets/b67514b2-4f60-4182-87f2-b01f0f72ca57 (+ 31436e74, 04ead93a) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4794006620), ctrhjk, 2026-06-24) +- **[photos + print videos + YouTube]** TT3 CS_001 updates: 2 photos (1713ddaa, 5e24ecdd), 2 zipped print videos https://github.com/user-attachments/files/29312978/20260609_165158.zip + https://github.com/user-attachments/files/29312984/20260609_171049.zip, YouTube short https://youtube.com/shorts/KGSePOjRa_I — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4794252735), achris0520, 2026-06-24) +- **[specimen photos]** TT3_001 batch-1 remainder printing (3 photos) — https://github.com/user-attachments/assets/3a57b361-ea3d-4fb7-bc0f-d3b192488f15 (+ 8af27338, c9e47774) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4794268567), achris0520, 2026-06-24) +- **[video]** Half-TPU supports wobbling mid-print (rationale for PLA-only supports) — https://github.com/user-attachments/assets/c262c8b0-335e-4d7e-9982-ed4a608ab4d3 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4794280199), achris0520, 2026-06-24) +- **[specimen photos]** Humidity-era prints: TPU bubbles (a602308c), first success in a while (de9d6e65, fa8a9ec8), flush-tower spaghetti (919c831a), failed accel-housing print (f43df0ce) — 5 photos — https://github.com/user-attachments/assets/a602308c-f2b5-4608-b141-fbe133aa96f9 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4835595840), achris0520, 2026-06-29) +- **[renders/STLs]** Accelerometer "igloo" mount design cycle: iso renders + struts STLs at 2f06a9a, 0b57d1a, a6d5829, be51d5e; canonical 3-STL print set — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/0b57d1a/cad/t3-prism/t3-prism-struts.stl?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4813814457), Copilot, 2026-06-26) +- **[photos]** Accelerometer housing shorter than sensor (IMG_6144, UUID QD8PWD) + igloo void screenshot — https://github.com/user-attachments/assets/32188309-a55b-4846-9868-043791bd964f , https://github.com/user-attachments/assets/36adcea5-4e72-452a-b02e-9ce41c41dee8 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4848946178), sgbaird, 2026-06-26/07-01) +- **[STLs]** Side-mounted hovering key-seat design ("three-legged alien") full artifact set at 75fac28; A2 tight-fit set at a7c32ef; A3 (6.2×6.2×6.8 mm pocket) set at 18c41a6 — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/18c41a6/cad/t3-prism/t3-prism-struts.stl?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4940023633), Copilot, 2026-07-01/07/10) +- **[photos + photo ZIP]** TPU clog PLA-only print (b2117729) then success at 235–240 °C: https://github.com/user-attachments/files/29666292/1000021732.zip + 4 screenshot extracts (764d245a, 4853bfab, 8a4027b2, 43a2adab) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4884103569), achris0520, 2026-07-04) +- **[photo]** Accelerometer rotating in oversized key-seat (IMG_6281) — https://github.com/user-attachments/assets/b616c1e5-f4a2-4d83-a0b2-79421bdfe0b6 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4895789291), ctrhjk, 2026-07-06) +- **[photo]** TPU assist module installed on H2D — https://github.com/user-attachments/assets/aff62c19-0ec7-4618-9939-95917e029586 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4910591253), achris0520, 2026-07-08) +- **[OnShape CAD]** Auto-uploaded part studios (doc https://cad.onshape.com/documents/eb55c8b08e361b8a052cec02/w/a47ada8519cce1ad893190b5 — struts/scaffold/cables/full, two upload rounds) + Jinkwan's manually corrected struts doc https://cad.onshape.com/documents/06f50ecfb09c089bfc449b83/w/34307f9439b58c9106cd00e6/e/9112f92297c97cce39acdd69 (current print source; 6.1 mm pocket-height empirical fit) + achris0520's request doc 14c65d27e0b3bf078b3c6cb7 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4918617139), Copilot/achris0520, 2026-07-08/09/11) +- **[photos]** Key-seat too-short fit checks IMG_6303/6304 (2 photos) — https://github.com/user-attachments/assets/cc3cefe7-7799-4ea0-85cf-7e6b118399e1 (+ 03098143) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4927872672), ctrhjk, 2026-07-09) +- **[screenshot]** Bucket-fill manual-support painting technique in Bambu Studio — https://github.com/user-attachments/assets/ef8b1747-031a-41ed-8080-46add047ceb6 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4930026388), achris0520, 2026-07-09) +- **[specimen photos]** IMG_6308–6316: latest print results, housing still too small, new manual supports worked (9 photos) — https://github.com/user-attachments/assets/cdaa5079-eb85-45b9-baa7-a2c7c5dece57 (+ 05b54af8, f9eb3137, 815c04f7, cd0c6eb0, fdcdae1e, 47830592, 8f674f72, 8ca3c717) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4937524982), ctrhjk, 2026-07-10) +- **[specimen photos]** IMG_6324–6330: key-seat fits accelerometer perfectly, bubbles on diagonal tendon (7 photos) — https://github.com/user-attachments/assets/717bbcfd-671f-4d54-8def-c674723f379a (+ 17f10ba6, d591b642, 0752a844, 79d3abc1, 3b4c74d2, b90759f9) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4960119866), ctrhjk, 2026-07-13) +- **[specimen photos]** IMG_6333–6337: near-final prints, minor diagonal-tendon bubbles (5 photos) — https://github.com/user-attachments/assets/fdedd8ab-db3b-4739-aac4-74db1e4a505a (+ b9a7e3e9, a6047b70, e83bbc14, 015c0f2a) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4971583990), ctrhjk, 2026-07-14) +- **[specimen photos]** IMG_6342–6346: new TPU filament, serious bubbles at >10% humidity (5 photos) — https://github.com/user-attachments/assets/adff712a-0560-497c-a472-17004fcbbb50 (+ 44cc9029, 65b1fd85, 67d3b156, f9e3e527) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-4983604148), ctrhjk, 2026-07-15) +- **[Box video + photo + CAD sketches]** Tendon wobbling during printing (byu.box slow-mo/video https://byu.box.com/s/r6zw5hffqbv9opx8apoqyi434hgz598r), tendon-imperfection photo (3d74c470), proposed 3-pillar support-cage sketches (a05406a7, 6e27c2e8) — https://github.com/user-attachments/assets/3d74c470-0576-4ff7-9d7a-ffbb6f75d272 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/35#issuecomment-5040197700), me-madsen, 2026-07-21) + +> Notable: the single richest thread in the repo — the entire T3-prism manufacturing lineage lives here: parametric SCAD → STL/3mf/gcode artifacts pinned to ~20 commits, the 9-specimen Sobol BO batch dataset (`bo/*.csv/json/stl/3mf` + per-specimen STLs), an Edison failure-analysis trajectory, the manual-support-painting SOP (video + ZIP + gcode), OnShape CAD mirrors with empirically corrected accelerometer-housing dimensions, and ~70 dated specimen/failure photos plus 5 print videos chronicling every print iteration (spaghetti → supports → humidity/bubbles → accelerometer igloo/key-seat fit). + +#### #36 — Get a video and record data for a first crush/drop test (issue, open) + +- **[YouTube]** Jeff's 16-min drop-tower training video (accelerometer mounting, high-speed camera, shaker, slug-launcher discussion) — https://youtu.be/RNjpAmWWmkQ — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/36#issuecomment-4503020184), sgbaird, 2026-05-20) +- **[images]** Video stills: accelerometers, drop-tower plate setup, top-plate second accelerometer (4 images) — https://github.com/user-attachments/assets/cbca1c5f-5f4b-4e15-938e-ef43aa906813 (+ 0fb6672a, 92a6bae3, faa26cfe) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/36#issuecomment-4509083060), sgbaird, 2026-05-21) +- **[images]** Lab-equipment stills: shaker w/ accelerometer, slug-firing machine (2 views), laser vibrometer, instruction manuals on desk (5 images) — https://github.com/user-attachments/assets/a9551e6a-5287-4573-baf7-47e65f288e16 (+ 3b2ad99f, 58da05b3, 34fd643c, 38604621) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/36#issuecomment-4509305026), sgbaird, 2026-05-21) +- **[PDF manuals]** Drop-tower TP4 Quick Start Guide + User's Guide — https://github.com/user-attachments/files/28125774/TP4_Quick.Start.Guide_W20000-98-15_Rev.A.PDF , https://github.com/user-attachments/files/28125775/TP4_User.s.Guide_W20000-98-14_Rev.A.PDF — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/36#issuecomment-4513715511), ctrhjk, 2026-05-21) +- **[external link]** Gemini chat share: cage/contraption material decision — https://gemini.google.com/share/d77b7e644e2d — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/36#issuecomment-4520433457), ctrhjk, 2026-05-22) +- **[images]** Specimen-cage fixture plans drawing + acrylic-plates/rods build photo (2 images) — https://github.com/user-attachments/assets/60f09def-7c1f-461d-a9c5-466f2969e18d , https://github.com/user-attachments/assets/dd236566-4455-40c4-901b-ed8f68f0dd7d — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/36#issuecomment-4521516377), me-madsen, 2026-05-22) +- **[photo]** Finished cut-and-threaded guide rods — https://github.com/user-attachments/assets/11829137-24bd-4265-bd9b-5cefa29639e6 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/36#issuecomment-4523574847), me-madsen, 2026-05-23) +- **[drop-test video]** First drop-test video: tensegrity separating from bottom plate pre-impact — https://github.com/user-attachments/assets/878f940a-0778-4de7-a0bf-0d070e62d0bb — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/36#issuecomment-4545983664), sgbaird, 2026-05-26) +- **[repo Edison trajectory]** Drop-test Edison report `edison-trajectories/drop-test/drop-test-653d7d39-b9c4-4d3f-9ae1-a1bc8fabd877.md` (+ https://platform.edisonscientific.com/tasks/653d7d39-b9c4-4d3f-9ae1-a1bc8fabd877) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/112aa3a2c91090abc25ac42d7b53a02fa89bae74/edison-trajectories/drop-test/drop-test-653d7d39-b9c4-4d3f-9ae1-a1bc8fabd877.md — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/36#issuecomment-4547124252), sgbaird, 2026-05-26) +- **[drop-test videos]** Rod-tolerance comparison (3 videos): original tight tolerance; tight tolerance w/o structure; tight tolerance w/ structure — https://github.com/user-attachments/assets/ba06aa90-9665-454e-bc35-1c381f4efad2 , https://github.com/user-attachments/assets/c269e30a-6afd-4c86-84f4-17a2424754cf , https://github.com/user-attachments/assets/66a2c26b-54b0-4993-84d7-c8a7f04030aa — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/36#issuecomment-4547152777), me-madsen, 2026-05-26) +- **[drop-test videos]** Configuration-matrix drops (4 videos): (1) structure only; (2) plate balanced on structure; (3a) accel + rods w/o bottom plate; (3b) w/ bottom plate — https://github.com/user-attachments/assets/2343b896-c86d-442a-9190-37dc90b54258 , https://github.com/user-attachments/assets/8465698b-28cf-4ce5-b5c6-fae58c5fb7e1 , https://github.com/user-attachments/assets/d0684710-af3d-466c-baa3-0377ff3ab568 , https://github.com/user-attachments/assets/49f00147-0220-4103-b920-260bc1500568 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/36#issuecomment-4547220915), ctrhjk, 2026-05-26) +- **[accelerometer data]** First raw accelerometer signals from 2026-05-22 tests (5 .txt files): Signal 10 PETG, Signals 11–13 "audrey", Signal 14 control (no specimen) — https://github.com/user-attachments/files/28369848/Signal.10.-.PETG.txt , https://github.com/user-attachments/files/28369849/Signal.11.-.audrey.txt , https://github.com/user-attachments/files/28369850/Signal.12.-.audrey.txt , https://github.com/user-attachments/files/28369847/Signal.13.-.audrey.txt , https://github.com/user-attachments/files/28369846/Signal.14.-.control.no.specimen.both.acrylic.plates.attached.txt — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/36#issuecomment-4568785386), me-madsen, 2026-05-28) +- **[image + video]** Bungee/notched-plate retention concept: still + demo video of strings holding structure — https://github.com/user-attachments/assets/f5f81e7c-2516-4156-97fb-a5feb9a5ce49 , https://github.com/user-attachments/assets/7cd5c014-b9c9-45b0-9248-00984da0a333 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/36#issuecomment-4568826378), me-madsen, 2026-05-28) +- **[YouTube]** Drop-test process SOP video https://youtu.be/dL2djikfJFE + auto-drop (cyclic testing) data-recording setup https://youtu.be/TXerxMYEsDM — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/36#issuecomment-4577444576), me-madsen, 2026-05-29) + +> Notable: the complete first-drop-test record — the only raw accelerometer data files in either thread (5 signal .txt uploads incl. a no-specimen control), 8 embedded drop-test videos spanning fixture-tolerance and configuration sweeps, drop-tower PDF manuals, fixture build photos, an Edison drop-test trajectory, and 3 YouTube SOP/training videos that everything downstream (accelerometer igloo mounts in #35, cyclic testing) builds on. + +#### #38 — Extensively explore and validate the joint design for PETG + TPU prints to mimic tensegrity (issue, open) +- **[Edison trajectory links]** 2 Edison platform trajectories on joint design to fetch/commit — https://platform.edisonscientific.com/trajectories/1400ca69-3ef4-4847-8b0a-f457e34617b1 and https://platform.edisonscientific.com/trajectories/3b9d76b5-af7f-45fd-9b52-590bf0f2fe80 — ([issue body](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/38), sgbaird-alt, 2026-05-08) + +#### #39 — Explore and validate joint design for PETG/PLA + TPU prints (5 candidate joints + OpenSCAD CAD + anchor-upset variants + Design F captive-core + Edison Phases 1–5) (PR, open) +- **[CAD render image]** CAD-vs-reference side-by-side montage of the 5 candidate joints (A anchor-bulb, B dovetail, C sleeve-overmold, D eyelet, E rebar) vs curated Wikimedia prior-art photos — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/21360bd/cad/joint-design/renders/all_compare_montage.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/39#issuecomment-4411590078), Copilot, 2026-05-09) +- **[CAD render image]** Post-Phase-3 refined CAD-vs-reference montage (B dovetail resized per Wang 2026 / Ermolai 2024; A anchor-bulb 1.71× pull-through) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/fb1f679/cad/joint-design/renders/all_compare_montage.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/39#issuecomment-4412559613), Copilot, 2026-05-09) +- **[CAD render image]** 4×5 multi-plane section montage (iso + X/Y/Z cuts × 5 designs; surfaced the non-physical B/D geometry bugs) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/ef6bb2e/cad/joint-design/renders/all_multiplane_section_montage.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/39#issuecomment-4427586306), Copilot, 2026-05-12) +- **[CAD renders + STL]** Design-A anchor-upset shape variants A0–A5 (sphere/frustum/torus/countersunk/star/mushroom): iso + Y=0 cutaway grid montage, plus per-variant PNG+STL and 3 contact-sheet montages in `cad/joint-design/A_variants/` — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/97dffa0/cad/joint-design/A_variants/renders/all_variants_grid_montage.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/39#issuecomment-4461680803), Copilot, 2026-05-15) +- **[CAD render image]** Design F captive-TPU-core-in-PLA-shell (teardrop fillet + layer-interlock teeth, 2.5× pull-out): iso + 3 orthogonal sections grid — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/9d20e5e/cad/joint-design/renders/F_captive_core_grid_montage.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/39#issuecomment-4461777483), Copilot, 2026-05-15) +- **[repo CAD/data files]** `cad/joint-design/`: 6 joint-family SCAD sources (+`*_section_{X,Y,Z}.scad`), `render.sh`/`render_F.sh`, per-design iso+section PNGs, 5+ STL exports, 5 downloaded Wikimedia reference JPEGs (`cad/joint-design/references/`) — https://github.com/vertical-cloud-lab/tensegrity-optimization/tree/97dffa0/cad/joint-design — ([PR body](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/39), sgbaird-alt, 2026-05-08) +- **[Edison artifacts]** Phase 1–5 joint-design Edison md+json trajectories under `edison-trajectories/joint-design/` (5× 1-credit LITERATURE `0b5d7ba2/ccb7b854/5a7ffce4/727a449d/ae373eb5`, LITERATURE_HIGH `be6768ab`, ANALYSIS `c38a2046`/`ce84ddf8`/`19e0c868`/`e9a1f4cc`/`28d942ea`, LITERATURE_HIGH `f9804247`, + `task_manifest.json`, submission scripts `scripts/edison/submit_joint_cad_review.py`, `submit_joint_phase4.py`) — https://github.com/vertical-cloud-lab/tensegrity-optimization/tree/9d20e5e/edison-trajectories/joint-design — ([PR body](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/39), sgbaird-alt, 2026-05-08) +- **[PDF]** Rebuilt proposal PDFs after removing the unverified Ye et al. attribution — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/2a644f5/proposal.pdf?raw=true and https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/2a644f5/proposal-diff.pdf?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/39#issuecomment-4686432346), Copilot, 2026-06-12) +> Notable: primary visual archive of the joint-design candidates (A–F) — full OpenSCAD CAD + STL + render/montage set plus 12 committed Edison trajectory artifact pairs; the A3 countersunk variant here seeded the PR #52 DOE plate. + +#### #41 — Add reviews/target_audience.md: JMD reviewer pool + lander outreach contacts + talking points (PR, open) +- **[printed-specimen photo]** "Tensegrity Structures" composite photo of printed specimens used as the outreach-email attachment (1226×1645) — https://github.com/user-attachments/assets/f39aa2c5-88a9-4134-9d39-c25a808c3ed4 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/41#issuecomment-4633995059), sgbaird, 2026-06-05) +- **[drop-test video]** Trimmed drop-test video ("Signal1" from issue #71) for outreach emails — https://github.com/user-attachments/assets/549e69a1-8875-4f83-bf93-c6b1bd82cfb0 (Clipchamp/OneDrive source: https://1drv.ms/v/c/5c9b7864c8b38792/IQA693tOubgsT4HxOliNbyefASQNjV0CAV7dcFpM3ChD2zM?e=82B3Yl) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/41#issuecomment-4633995059), sgbaird, 2026-06-05) +- **[printed-specimen photo]** IMG_6339 — high-res (5712×4284) photo of printed structures attached to the Gu/Dotov tensegrity-crutch outreach email — https://github.com/user-attachments/assets/e28dbc9f-3150-4211-a999-17294edc3d0c — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/41#issuecomment-4972494682), ctrhjk, 2026-07-14) +- **[drop-test video]** Drop-test video attached to the same outreach email — https://github.com/user-attachments/assets/52c81e20-690d-4df8-9e9e-55165fe8f265 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/41#issuecomment-4972494682), ctrhjk, 2026-07-14) +- **[Edison artifacts]** Target-audience + outreach-topics LITERATURE_HIGH md+json pairs — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/798693c/edison-trajectories/2026-05-09-target-audience-9cc7db18.md (+`.json`) and https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/56f58de/edison-trajectories/2026-05-12-outreach-topics-f18aca01-00bb-4ca7-a8e9-f6312dfaaff7.md (+`.json`, +`scripts/edison/submit_outreach_topics.py`) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/41#issuecomment-4411479456), Copilot, 2026-05-09) +- **[external reference]** Tensegrity-crutch prior-art paper (Gu & Dotov, UNO) shared for outreach — https://arxiv.org/abs/2605.02710 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/41#issuecomment-4972111715), ctrhjk, 2026-07-14) +> Notable: holds the canonical outreach media pack — the printed-specimen group photos and trimmed slow-motion drop-test videos actually emailed to external researchers — plus meeting-notes comments (Sheikh on BO setup; Santos on prestress/FEA) grounding the experimental design. + +#### #43 — Edison-driven survey of funding venues (PR, open) +- **[Edison artifacts]** 3 funding-venue LITERATURE/LITERATURE_HIGH md+json pairs (~32/66/52 KB) incl. program-officer contacts — https://github.com/vertical-cloud-lab/tensegrity-optimization/tree/17a768f/edison-responses (`2026-05-09-funding-venues-literature-ff9cb91e`, `...-literature-high-5c3afc89`, `...-program-officers-literature-high-c668cd19` + README) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/43#issuecomment-4412591753), Copilot, 2026-05-09) + +#### #45 — Edison query on strut material (PLA vs PETG vs HF/CF) (PR, open) +- **[Edison artifacts]** Strut-material LITERATURE_HIGH artifacts (47 KB answer, 38 DOI refs) + summary with ranked table — `edison-trajectories/strut-material-selection-5bb5e5d3-...{md,json,-references.md}`, `strut-material-selection-summary.md`, `scripts/edison/submit_strut_material.py` @ branch `copilot/research-strut-material-options` commit 0e28415 — ([PR body](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/45), Copilot, 2026-05-12) + +#### #47 — Egg-drop demo exploration via Edison (+ drag-free baseline, BEAR baseline, SUPERball actuators) (PR, open) +- **[Edison artifacts]** 4 LITERATURE_HIGH md+json pairs: `egg-drop-tensegrity-1b90208d`, `egg-drop-followup-f41b7034` (drag-free baseline + V/m benchmark protocol), `egg-drop-brown-lab-a3fc0429` (BEAR/Snapp 2024), `egg-drop-superball-actuators-f8f07966` — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/91d3459/edison-trajectories/egg-drop-brown-lab-a3fc0429.md (siblings + 4 `scripts/edison/submit_egg_drop*.py` in same dir) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/47#issuecomment-4427434179), Copilot, 2026-05-12) + +#### #48 — T3-prism thick-cable variant + tree(auto) supports H2D slice (PR, closed) +- **[STL + slice files]** `cad/t3-prism/t3-prism-thick.stl` (cable_d=3.5), `slices/t3-prism.H2D-thick.3mf` (169 KB project) and `slices/t3-prism.H2D-thick-PETG-supports.gcode.3mf` (1.6 MB sliced job, 1h42m, 8.83 g part + 3.64 g supports), iso preview `cad/t3-prism/t3-prism-thick-iso.png` — on PR #48 branch (closed unmerged; opened after the first H2D PETG spaghetti-failure at the 43 mm top-cable bridge) — ([PR body](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/48), Copilot, 2026-05-12) + +#### #50 — Edison brief: ASTM standards for initial Instron stiffness tests (PR, open) +- **[Edison artifacts]** `edison-trajectories/instron-stiffness/`: 122 KB brief md, 2.2 MB raw json, auto-extracted 81-entry `.references.bib`, README — task `9f68e71e` — ([PR body](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/50), Copilot, 2026-05-15) +- **[equipment data]** BYU CB 123 tensile-frame inventory (1/3/20/100 kip + MTS AOX extensometer specs; booking link https://projectsupportcenter.simplybook.me/v2/#book/category/3/count/1/) mapped into https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/ecfb64e/edison-trajectories/instron-stiffness/equipment-selection.md — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/50#issuecomment-4462547993), sgbaird, 2026-05-15) + +#### #52 — Anchor-bulb pre-tensioning DOE: 15-specimen test plate (air gap × joint size) (PR, open) +- **[CAD render image]** Full 3×5 DOE plate iso render (A3 countersunk head, horizontal cable, node dia 7.5/9.5/12 mm × gap 0.1–0.6 mm) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/d7be0e5/cad/anchor-bulb-tensioning-array/renders/tensioning_array_iso.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/52#issuecomment-4462459256), Copilot, 2026-05-15) +- **[CAD render image]** 15-specimen contact-sheet montage + X=0 section montage — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/d7be0e5/cad/anchor-bulb-tensioning-array/renders/all_specimens_montage.png?raw=true and https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/d7be0e5/cad/anchor-bulb-tensioning-array/renders/section_montage.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/52#issuecomment-4462459256), Copilot, 2026-05-15) +- **[repo CAD files]** `cad/anchor-bulb-tensioning-array/`: `_common.scad`, 15 `H-S{0-2}G{0-4}.scad` specimens, `tensioning_array.scad`, `render.sh`, per-specimen PNG+STL, full-plate STL, README with pull-test protocol (F_pre 5–25 N) — https://github.com/vertical-cloud-lab/tensegrity-optimization/tree/d7be0e5/cad/anchor-bulb-tensioning-array — ([PR body](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/52), Copilot, 2026-05-15) +> Notable: the anchor-bulb DOE plate itself — complete printable CAD + renders for the 15-cell pre-tensioning pull-test experiment (xref #87 for follow-up). + +#### #57 — Analyze Davami et al. 2025 dynamic AM tensegrity (issue, open) +- **[PDF attachment]** Full text of Davami et al. 2025, *Int. J. Impact Eng.* (shared by Jeff) — https://github.com/user-attachments/files/28065877/1-s2.0-S0734743X24003336-main.pdf — ([issue body](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/57), sgbaird, 2026-05-20) + +#### #58 — Analyze Davami et al. 2025 + Edison follow-up (PR, open) +- **[PDF + Edison artifacts]** Archived paper `literature/davami2025-dynamic-am-tensegrity.pdf`, analysis doc `literature/davami2025-analysis.md`, and Edison LITERATURE_HIGH follow-up (task `0944224d`, ~36 KB md + 55 KB json, 16 ranked related articles) under `edison-trajectories/davami2025-followup/` + `scripts/edison/submit_davami2025_followup.py` — on PR #58 branch — ([PR body](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/58), Copilot, 2026-05-20) + +#### #60 — Edison briefs: objectives/outcomes per drop-test data source + ANALYSIS synthesis (PR, open) +- **[Edison artifacts]** 5 per-modality LITERATURE_HIGH md+json pairs in `edison-trajectories/objective-functions/` — 01 accelerometer/M23 (`cfd30f3e`), 02 high-speed/slow-mo video (`7d6b43bf`), 03 shaker transmissibility (`31126ee7`), 04 gas gun (`9d74ab2e`), 05 LDV (`f40e41a7`) + README + `scripts/edison/submit_objective_functions.py` — ([PR body](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/60), Copilot, 2026-05-21) +- **[Edison submission record]** ANALYSIS synthesis task `789de8ab` upload manifest (all 10 briefs uploaded via `client.upload_file()`) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/c8e1ade/edison-trajectories/objective-functions/synthesis-SUBMITTED.json — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/60#issuecomment-4511331479), Copilot, 2026-05-21) +> Notable: defines the sensor-to-BO-objective mapping (accelerometer, slow-mo video, shaker, gas gun, LDV) and the SEA/VEA mass-volume normalization decision for the drop-test campaign. + +#### #61 — Explore Tendon/Cable Printing Angles (issue, open) +- **[printed-specimen photos]** 3 close-up photos of printed TPU tendons at 0° (parallel), ~45°, and ~90° (perpendicular) bead angle relative to tendon direction — https://github.com/user-attachments/assets/4df5c1c9-d78e-45a7-82a1-23e1e56d475c , https://github.com/user-attachments/assets/dd558628-3899-4a4b-bbbe-9922a889dcaf , https://github.com/user-attachments/assets/817ab1f5-5c2b-4a55-9934-298dd162d7a9 — ([issue body](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/61), me-madsen, 2026-05-22) + +#### #65 — Figure out how to add manual supports (issue, open) +- **[gcode ZIP]** Audrey's original hand-painted manual-support slice of the T3-prism (PLA struts + TPU cables) — https://github.com/user-attachments/files/28156629/t3-prism.H2D-MM-PLAstruts-TPUcables_ORIGINAL_MANUAL_SLICE.gcode.zip — ([issue body](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/65), sgbaird, 2026-05-22) + +#### #66 — Replace manual support painting: Bambu PLA recipe + baked pillar/tree supports + cage + verification (PR, open) +- **[gcode-preview images]** 3-panel T3-prism slice previews for the path-(a) recipe: θ=40 (https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/fb41388/cad/print-supports/verification/t3-prism-pr35-gcode-preview.png?raw=true), θ=10 + before/after comparison (https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/9a863be/cad/print-supports/verification/t3-prism-pr35-threshold-comparison.png?raw=true), and H2D/OrcaSlicer re-renders @ 686d8e7 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/66#issuecomment-4523951730), Copilot, 2026-05-23) +- **[gcode-preview image]** TPU-safe enforcer-path slice preview (545,992 support segments) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/f85519c/cad/print-supports/verification/t3-prism-pr35-tpu-enforced-preview.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/66#issuecomment-4523789364), Copilot, 2026-05-23) +- **[STL files]** gcode-reified support STLs for viewer inspection: `t3-prism-pr35-th10-supports.stl` (5.4 MB) + `t3-prism-pr35-tpu-enforced-supports.stl` (13.3 MB) @ 86b69a7; combined object+supports STLs (6.8/14.6 MB) + preview PNG @ 9ae600d — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/86b69a7/cad/print-supports/verification/t3-prism-pr35-th10-supports.stl — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/66#issuecomment-4525829499), Copilot, 2026-05-23) +- **[STL + render + GIF]** Baked narrowing-pillar artifacts (iterations: 96 pillars @ 02dd45a → ray-cast 183 @ e81c4be → tree-mode 121 tips/7 feet @ e61ee9d/e624cab → multi-hit 181 @ 68e7a8b → 188 tips/31 feet @ 7bd7466): `t3-prism-pr35-pillars.stl`, `t3-prism-pr35-with-pillars.stl`, `t3-prism-pr35-pillars-preview.png`, rotating 360° GIF `t3-prism-pr35-pillars-rotating.gif` — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/68e7a8b/cad/print-supports/verification/t3-prism-pr35-pillars-rotating.gif?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/66#issuecomment-4693863994), Copilot, 2026-06-12) +- **[screenshots]** Manual Bambu Studio verification screenshots: pillars attached to TPU cables (https://github.com/user-attachments/assets/abe70aca-a6ab-4630-9cfb-5de2a1c08690), redundant bottom-vertex pillars (https://github.com/user-attachments/assets/48a20bbc-74ab-4818-b722-f8879e0f545e), "211 non-manifold edges" repair dialog (https://github.com/user-attachments/assets/a6004eea-5720-46f6-91d2-891801aad84d) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/66#issuecomment-4546383138), sgbaird, 2026-05-26) +- **[gcode-preview image]** True PLA+TPU multi-material slice (patched vertical-cloud-lab/BambuStudio PR #2 CLI; 605 layers, 578 toolchanges, ~21 h) — https://raw.githubusercontent.com/vertical-cloud-lab/tensegrity-optimization/copilot/add-manual-supports/cad/print-supports/verification/t3-prism-pr35-mm-pillars-preview.png — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/66#issuecomment-4547110841), Copilot, 2026-05-26) +- **[STL print files]** The 3 STLs for the actual PLA+TPU print: struts https://raw.githubusercontent.com/vertical-cloud-lab/tensegrity-optimization/65d0d3f2b1d673f74755e1c8900af5af2500fc53/cad/t3-prism/t3-prism-struts.stl, cables https://raw.githubusercontent.com/vertical-cloud-lab/tensegrity-optimization/65d0d3f2b1d673f74755e1c8900af5af2500fc53/cad/t3-prism/t3-prism-cables.stl (PR #35 @ 65d0d3f), pillars `t3-prism-pr35-pillars.stl` (this branch) + `HOW-TO-PRINT.md` guide — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/66#issuecomment-4654407947), Copilot, 2026-06-08) +- **[print video]** Video of the printed tree-support connection points looking too wide (prompted the 0.4 mm tip change) — https://github.com/user-attachments/assets/7d15ca74-7061-4088-8afe-3cd86fbcfd66 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/66#issuecomment-4673401417), sgbaird, 2026-06-10) +- **[failed-print photo]** Photo of the failed print with unsupported vertical TPU cables circled in green (root-caused the `multiple_hits=False` ray-cast bug) — https://github.com/user-attachments/assets/36bff96d-f439-4393-88e8-7f2a28c3a49d — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/66#issuecomment-4693738004), sgbaird, 2026-06-12) +- **[FEA figure + scripts]** CalculiX layer-by-layer support-stability FEA figure + verification tooling (`verify_support_geometry.py`, `fea_support_stability.py`, `render_pillars_preview.py`) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/7bd7466/cad/print-supports/verification/t3-prism-pr35-fea-stability.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/66#issuecomment-4805632854), Copilot, 2026-06-26) +- **[gcode-preview image]** On-hardware-profile BambuStudio 02.06.00.51 H2D slice of the baked-pillar mesh (646 layers, 60.9 g, ~5 h 36 m) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/475d530/cad/print-supports/verification/t3-prism-pr35-pillars-gcode-preview.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/66#issuecomment-4805743449), Copilot, 2026-06-26) +- **[STL + JSON + render + GIF]** Anti-wobble tendon-cage artifacts: `t3-prism-pr35-cages.stl`, `t3-prism-pr35-cage-report.json`, `verify_cage_geometry.py`, preview https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/2ec7372/cad/print-supports/verification/t3-prism-pr35-cages-preview.png?raw=true, rotating GIF https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/11c6f7a/cad/print-supports/verification/t3-prism-pr35-cages-rotating.gif?raw=true (sim-tuned regeneration @ 955069f/7cf6df3) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/66#issuecomment-5054301744), claude[bot], 2026-07-23) +- **[simulation figures + Edison artifacts]** Tendon-wobble simulation ladder: analytic model figure https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/45400e4/cad/print-supports/verification/t3-prism-pr35-tendon-wobble-model.png?raw=true, cage-design sweep (`6908f8b/...cage-design-sweep.png`), CalculiX contact FEA (`73e9467/...fea-tendon-wobble.png`), gcode-thermal FEA (`30d3d02/...gcode-thermal-wobble.png`), + Edison print-advice artifacts (task `722c51c6`) https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/5bf0f99/outputs/edison-pr35-print-advice/answer.md (+`references.md`, task dump) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/66#issuecomment-5080376228), claude[bot], 2026-07-25) +> Notable: the largest print-verification trove — every iteration of the baked-support/tree/cage STLs with rotating-GIF and gcode previews, real failed-print photos/video, CalculiX FEA + wobble-simulation figures, the definitive 3-STL upload set for the PLA+TPU T3-prism print, and a documented mesh-drift warning (cage artifacts fit the pinned 65d0d3f PR #35 mesh, not PR #35 HEAD). + +#### #67 — Add drop-test protocol, Edison synthesis, and first-data analysis (PR, open) +Branch `copilot/get-video-drop-test-data` holds all committed data (`data/drop-tests/*`, `scripts/analysis/*`, `docs/*`, `edison-trajectories/*`); thread continued in PR #82. +- **[repo data/report]** Edison LITERATURE_HIGH drop-tower troubleshooting synthesis (~57 KB md + JSON) — `edison-trajectories/drop-test/drop-test-653d7d39-….md` (branch) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4546706091), Copilot, 2026-05-26) +- **[repo data/analysis]** First drop-test analysis of 5 TP4 exports (Signal 10–14, "audrey"/PETG/control; CSVs originally posted on issue #36) — `data/drop-tests/raw/`, `data/drop-tests/figures/`, `scripts/analysis/drop_test_analysis.py`, `docs/drop-test-analysis.md` (branch) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4568944461), Copilot, 2026-05-28) +- **[CSV ×8]** Vertex vs acrylic-plate TP4 exports, 4 specimens (n0jdwk, m6cyoq, T3_0103, T3_0000) × 2 mounts, 13 ft drops — e.g. https://github.com/user-attachments/files/29265861/n0jdwk_Signal1.csv — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4783408053), ctrhjk, 2026-06-23) +- **[video ×9]** Slow-mo drop videos for each vertex/acrylic run + clip-failure demo — e.g. https://github.com/user-attachments/assets/e71b5200-c9b5-42b0-838e-7edec1bae756 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4783408053), ctrhjk, 2026-06-23) +- **[photo ×5]** Setup photos (vertex hot-glue mount, plate config) + damage photos (m6cyoq cut strut, T3_0103 cut TPU tendon) — e.g. https://github.com/user-attachments/assets/f7bcfb4d-1f1c-4cbd-b6b6-a5e6980be5cf — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4783408053), ctrhjk, 2026-06-23) +- **[plot]** CFC-180 peak-g bar chart, vertex vs acrylic — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/239b833/data/drop-tests/vertex-acrylic/figures/02_cfc180_peak_bars.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4785005546), Copilot, 2026-06-24) +- **[photo ×3]** Clip-height sweep setup (extra bungees, clips at 0.5–2 in) — e.g. https://github.com/user-attachments/assets/c60547e0-2354-4ea1-9e1b-83a944ef4172 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4794351098), ctrhjk, 2026-06-24) +- **[video ×8]** Clip-height sweep drops (0.5/1/1.5/2 in × 2; 0/8 triggered so video-only data) — e.g. https://github.com/user-attachments/assets/5033e171-265e-42ae-b130-86d6802be1d3 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4794351098), ctrhjk, 2026-06-24) +- **[CSV+video+photo]** Base-plate accelerometer check: 1 triggered CSV https://github.com/user-attachments/files/29313852/Accelerometer.check_Signal1.csv, video https://github.com/user-attachments/assets/444792a3-33ca-42a2-966c-27c570c509ef, setup photo — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4794438322), ctrhjk, 2026-06-24) +- **[plot/repo]** Clip-height analysis + Edison ANALYSIS trajectory (task 91e293a8) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/bf8ebfc/data/drop-tests/clip-height/figures/01_baseplate_impact_window.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4794813820), Copilot, 2026-06-25) +- **[CSV ×20]** Input-output transmissibility series: 4 specimens (practice, n0jdwk, yqpmx1, h8Lbev) × 5 drops @13 in, bungees removed — e.g. https://github.com/user-attachments/files/29359885/practice_Signal1.csv — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4804858562), ctrhjk, 2026-06-25) +- **[video ×20]** Slow-mo video per input-output drop — e.g. https://github.com/user-attachments/assets/4525f053-c37e-4725-bc17-5078eae4aa31 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4804858562), ctrhjk, 2026-06-25) +- **[photo ×7]** Input-output rig setup (3) + specimen ID photos (4) — e.g. https://github.com/user-attachments/assets/0b828674-6b88-4319-a736-7a8bde79d2f7 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4804858562), ctrhjk, 2026-06-25) +- **[plot/repo]** Transmissibility bar chart + Edison ANALYSIS trajectory (task fe044079, reproduced T values) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/555d199/data/drop-tests/input-output/figures/02_transmissibility_bars.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4805044500), Copilot, 2026-06-25) +- **[video ×8]** Bungee-fixity validation drops (n0jdwk, yqpmx1, h8Lbev, UVcMuU × with/without top plate; no DAQ) — e.g. https://github.com/user-attachments/assets/3a2ff0e0-7473-46f0-926d-3c0458cf686d — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4813080485), ctrhjk, 2026-06-26) +- **[CSV ×5 + video ×5 + photo]** Key-seat mount test, specimen `prc1kn` (Signal1,2,3,5,6; incl. sensor fall-off clip) — e.g. https://github.com/user-attachments/files/29480185/key.mounted_Signal1.csv, fall-off video https://github.com/user-attachments/assets/9360cd0a-1863-43ba-b5eb-8c5d5f5ed458 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4837240958), ctrhjk, 2026-06-29) +- **[plot/repo]** Key-seat drift analysis figures — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/3deccb1/data/drop-tests/key-mounted/figures/02_drift_per_drop.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4837463147), claude[bot], 2026-06-29) +- **[plot/repo]** Video-kinematics analysis of the 960 fps RX100 IV clips (centroid tracking; raw ~250 MB videos cached from comment URLs, not committed) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/899cfc2/data/drop-tests/key-mounted/video-figures/01_centroid_descent_overlay.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4837778001), claude[bot], 2026-06-29) +- **[CSV ×5 + video ×5 + photo]** Key-seat + wax retainer retest (Signal7–11) — e.g. https://github.com/user-attachments/files/29485675/Key.mounted2_Signal7.csv — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4838901017), ctrhjk, 2026-06-30) +- **[CSV ×8 + video ×5]** Burn-in wax series (Signal1–3 burn-in no video; Signal4–8 recorded) — e.g. https://github.com/user-attachments/files/29532027/Burn_in_wax_Signal1.csv — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4849968630), ctrhjk, 2026-07-01) +- **[video/repo]** Raw camera-file `drop5.mp4` (52 MB, Clipchamp-re-encoded 30 fps container of 960 fps HFR) committed to branch to settle the frame-rate question — repo file on `copilot/get-video-drop-test-data` — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4859816531), sgbaird, 2026-07-01) +- **[CSV ×30 + photo]** Drift-calibration #1: 30 auto-drops @13 in on `prc1kn` (sensor fell off at drop 26) — e.g. https://github.com/user-attachments/files/29578647/drift.calibration_Signal1.csv — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4861564582), ctrhjk, 2026-07-02) +- **[plot/repo]** Drift-cal figures + metrics JSON — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/b11bbb3/data/drop-tests/drift-calibration/figures/01_full_series.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4861565944), claude[bot], 2026-07-02) +- **[CSV ×50 + photo]** Drift-calibration #2: 50 auto-drops with cable tie-off — e.g. https://github.com/user-attachments/files/29614641/drift.calibration2_Signal1.csv — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4870839517), ctrhjk, 2026-07-02) +- **[CSV ×32 + photo ×3]** 30-drop campaign on near-real specimen `RW5F61` (three-sensor layout incl. new bottom tri-axis CH6–8; 5 spurious triggers) — e.g. https://github.com/user-attachments/files/29622157/30drops.with.real_Signal1.csv — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4872786485), ctrhjk, 2026-07-03) +- **[ZIP ×4 + photo]** 100-drop campaign @13 in on `RW5F61` (100 TP4 CSVs zipped in 25s) — https://github.com/user-attachments/files/29719730/drop1.zip …29719734 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4897632381), ctrhjk, 2026-07-06) +- **[CSV ×50]** CH4-trigger qualification: 50 auto-drops on `RW5F61` — e.g. https://github.com/user-attachments/files/29722993/ch4trigger_Signal1.csv — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4898336565), ctrhjk, 2026-07-06) +- **[ZIP ×4 + photo]** 5-in 100-drop campaign on `RW5F61` (taped housings) — https://github.com/user-attachments/files/29816659/drop1.zip …29816667 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4918818168), ctrhjk, 2026-07-08) +- **[ZIP ×8 + photo]** 200-drop campaign @10 in on fresh intact print `7xadt6` (Signal3–202) — https://github.com/user-attachments/files/29977985/200drops_1.zip …29977998 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4961087426), ctrhjk, 2026-07-13) +- **[CSV ×30]** Post-campaign check run #1 (Signal203–232) — e.g. https://github.com/user-attachments/files/29981927/check_Signal203.csv — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4961982027), ctrhjk, 2026-07-13) +- **[CSV ×30]** Check run #2 (Signal233–262; analysis handed off to PR #82) — e.g. https://github.com/user-attachments/files/29986146/check2_Signal233.csv — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67#issuecomment-4963014205), ctrhjk, 2026-07-13) +- **[external link]** Drop-tower training video — https://youtu.be/RNjpAmWWmkQ (referenced in `docs/drop-test-protocol.md`) — ([PR body](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/67), Copilot, 2026-05-26) +> Notable: the project's central experimental trove — ~450 raw TP4 accelerometer CSVs across 12+ drop campaigns, ~60 slow-mo videos, dozens of rig/specimen photos, all mirrored with analysis scripts/figures/Edison trajectories on branch `copilot/get-video-drop-test-data`. + +#### #68 — Open-ended tensegrity-inspired objects (issue, open) +- **[photo ×2]** Failed-print open-ended (non-tensegrity) T3 prism specimen — https://github.com/user-attachments/assets/8ebacafd-5df5-4d0d-890b-6e9c116f6c53 , https://github.com/user-attachments/assets/526e0575-2a7b-4c48-a483-aa9a0dc16d8d — ([issue body](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/68), me-madsen, 2026-05-29) +- **[video ×3]** Demos of its energy-absorbent / metastable-shell behavior — https://github.com/user-attachments/assets/565947e3-7e8e-4413-82e3-e134a98f6f6f , …/85586938-fc5a-4bd3-9a42-30f2d803fef8 , …/7c282ad5-e678-4b4d-a6ef-cf80e3c28d13 — ([issue body](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/68), me-madsen, 2026-05-29) + +#### #71 — "Tune" the accelerometers on the drop tower (issue, open) +- **[CSV ×14]** 06/02/2026 accelerometer-tuning TP4 exports (table export `06.02.2026.csv` + Signal1–13; Signals 7/8/12 flagged as faults) — e.g. https://github.com/user-attachments/files/28604211/06.02.2026.csv , https://github.com/user-attachments/files/28604208/06.02.2026_Signal1.csv — ([issue body](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/71), me-madsen, 2026-06-04) +- **[video ×10]** Slow-mo video per valid signal (S1–6, 9, 10, 11, 13) — e.g. https://github.com/user-attachments/assets/fa74f126-d4d3-4d07-a38b-b9de8e761a29 — ([issue body](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/71), me-madsen, 2026-06-04) +- **[plot ×9]** Signal-trace graphs for each dataset — e.g. https://github.com/user-attachments/assets/5da12b5c-8f60-4f08-8f30-92196c21833f — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/71#issuecomment-4633820479), me-madsen, 2026-06-05) +> Notable: origin of the single-vs-tri-axis discrepancy hunt; this raw data is analyzed in PR #74. + +#### #73 — Create abstract for TMS 2027 (PR, open) +- **[PDF]** TMS 2027 symposium #021 call-for-abstracts flyer — https://github.com/user-attachments/files/29353343/TMS2027-CFA-Flyer-021.pdf — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/73#issuecomment-4803220893), sgbaird, 2026-06-25) +- **[repo data]** Edison symposium-fit ANALYSIS trajectory (task 16895002; abstract + flyer attached) — `edison-trajectories/tms-symposium-fit/` on branch `copilot/create-abstract-for-tms-2027` — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/73#issuecomment-4803369782), Copilot, 2026-06-25) + +#### #74 — Analyze drop-tower accelerometer "tuning" data (issue #71) (PR, open) +- **[CSV ×8]** 06/08/2026 co-located calibration series (500G_Signal5 aborted + seven 1000 G drops) — e.g. https://github.com/user-attachments/files/28768778/500G_Signal5.csv , https://github.com/user-attachments/files/28767490/1000G_Signal6.csv — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/74#issuecomment-4663450421), ctrhjk, 2026-06-09) +- **[photo]** Co-located sensors on bare metal load (~1/4 in apart, acrylic removed) — https://github.com/user-attachments/assets/7ccc671b-22af-48ff-8c43-563ade67cf82 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/74#issuecomment-4663572321), ctrhjk, 2026-06-09) +- **[plot ×3]** Calibration analysis figures (co-located overlay, 30.8× regression, CH1 clipping) — e.g. https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/16b6f02/docs/figures/accelerometer-calibration/cross_calibration_regression.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/74#issuecomment-4663586414), Copilot, 2026-06-09) +- **[CSV ×15 + video ×15]** 06/09/2026 corrected-sensitivity metal-mount height sweep (10/15/20 in × 5; 5 in no trigger); video per drop — e.g. https://github.com/user-attachments/files/28771183/Test_10in_1.csv , video https://github.com/user-attachments/assets/cd8ad582-7382-483b-96d0-9b913c7067e0 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/74#issuecomment-4664234492), ctrhjk, 2026-06-09) +- **[plot ×2]** CH1 hard-clip + amplitude-sweep figures — e.g. https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/f444456/docs/figures/accelerometer-calibration-2/ch1_clipping.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/74#issuecomment-4664913002), Copilot, 2026-06-09) +- **[CSV ×15 + video ×15 + photo]** 06/10/2026 "AC3" five-channel series (CH5 added; 10/15/20 in × 5) — e.g. https://github.com/user-attachments/files/28809662/Test_10in_1.csv , setup photo https://github.com/user-attachments/assets/c6bde6e1-f693-4626-a66e-806973ad675d — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/74#issuecomment-4673864934), ctrhjk, 2026-06-10) +- **[plot ×2]** CH5 = 0.953 × CH4 cross-calibration regression + CH1-vs-CH5 clipping — e.g. https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/fd14204/docs/figures/accelerometer-calibration-3/cross_calibration_regression.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/74#issuecomment-4673941024), Copilot, 2026-06-10) +- **[repo data]** All 4 series' raw CSVs + Edison ANALYSIS trajectory (task 015f36e1, incl. reviewer notebook `.ipynb` + corrected peak CSV) — `data/drop-tests/accelerometer-{tuning,calibration,calibration-2,calibration-3}/`, `edison-trajectories/accelerometer-tuning/` on the PR branch — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/74#issuecomment-4637417066), Copilot, 2026-06-06) +- **[spec]** Tri-axis accelerometer identified: Dytran 3133A4, S/N 11799, 6×6×5.94 mm, sens 0.690/0.667/0.734 mV/G — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/74#issuecomment-4792400480), ctrhjk, 2026-06-24) +> Notable: complete accelerometer cross-calibration record — resolves the ~30×/~46× sensitivity-entry error and lands CH5 = 0.953 × CH4, the basis for all later drop-test instrumentation. + +#### #76 — ASME Journal of Mechanical Design - T3 prism optimization manuscript (PR, open) +- **[PDF ×4]** Manuscript, TODO copy, SI, latexdiff (round 3) — e.g. https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/2401847/manuscript/manuscript.pdf?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/76#issuecomment-4693972957), Copilot, 2026-06-12) +- **[PDF ×4]** Rebuilt set after round-3 fixes — e.g. https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/1cd90cd/manuscript/manuscript.pdf?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/76#issuecomment-4699441384), Copilot, 2026-06-13) +- **[PDF ×4]** Rebuilt set after tensegrity-AM references (round 5) — e.g. https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/5193a48/manuscript/manuscript.pdf?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/76#issuecomment-4700384559), Copilot, 2026-06-14) +- **[repo data]** Edison mock-review trajectories rounds 3–5 (tasks d17a2155, a81649dc, e4c536b4; reviews + notebooks) — `edison-trajectories/mock-jmd-review-{3,4,5}/` on the PR branch — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/76#issuecomment-4700384559), Copilot, 2026-06-14) + +#### #78 — Tensegrity-like Lattice Structures Abstract for TMS 2027 (issue, open) +- **[CSV]** Full TMS 2027 symposium-organizers list (106 symposia, 682 rows) — https://github.com/user-attachments/files/29574113/tms2027_symposium_organizers.csv — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/78#issuecomment-4860287709), sgbaird, 2026-07-01) +- **[repo data]** Edison LITERATURE_HIGH abstract review (task ea3eb635) — `reviews/edison/` on branch `claude/issue-78-20260701-2122` (commit e855d2d) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/78#issuecomment-4860148048), claude[bot], 2026-07-01) +- **[repo data]** Symposium-fit analysis + parsed organizers CSV — `reviews/tms2027/` on branch `claude/issue-78-20260701-2146` (commit 480f0a1) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/78#issuecomment-4860289136), claude[bot], 2026-07-01) +- **[repo data]** Edison symposium-105 Cordero/Hofmann persona mock reviews (task 1d71a27e; answer, tables, raw JSON) — `reviews/edison-symp105-personas/` on branch `claude/issue-78-20260701-2241` (commit 84edbad) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/78#issuecomment-4860605691), claude[bot], 2026-07-01) +- **[repo doc]** Iteratively revised 150-word abstract + author block — `tms2027-abstract.md` across branches `claude/issue-78-20260702-{0010,0022,0028,0130,0146}` (final commit 75c8da6) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/78#issuecomment-4861468861), claude[bot], 2026-07-02) +- **[external link]** TMS CFA flyers referenced — https://www.tms.org/tms2027/downloads/flyers/TMS2027-CFA-Flyer-092.pdf , …-Flyer-070.pdf — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/78#issuecomment-4859995595), claude[bot], 2026-07-01) + +#### #82 — Add drop-test protocol, Edison synthesis, and first-data analysis (PR, open, branch `copilot/add-drop-test-protocol`) +- **[CSV ×60]** 5 in vs 10 in drop-height validation, 30 TP4 exports per height (`5vs10_Signal1–60.csv`, trigger CH5 @ 500 G) — https://github.com/user-attachments/files/30024802/5vs10_Signal1.csv (files 30024793–30024883) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/82#issuecomment-4973983998), ctrhjk, 2026-07-14) +- **[ZIP ×9]** Endurance test at 10 in, aborted at drop 256/500 by TP4 overload (`500drops_1–9.zip`) — https://github.com/user-attachments/files/30058951/500drops_1.zip (files 30058951–30058969) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/82#issuecomment-4983665345), ctrhjk, 2026-07-15) +- **[ZIP ×17 + video]** Full 500-drop test at 10 in (bottom tri-axial CH6–8 removed; `500_1–17.zip`) + YouTube test video — https://github.com/user-attachments/files/30102459/500_1.zip (files 30102459–30102486); video https://youtu.be/uqn3qnJPfN8 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/82#issuecomment-4996703024), ctrhjk, 2026-07-16) +- **[CSV ×45]** Saturation sweep: drop height 20–60 in × 1–5 felt sheets, 5 signals per cell (`height.level_Signal1–45.csv`) — https://github.com/user-attachments/files/30141585/height.level_Signal1.csv (files 30141585–30141657) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/82#issuecomment-5007713855), ctrhjk, 2026-07-17) +- **[video]** Screen-recording demo of uploading data directly to the branch instead of comment attachments — https://github.com/user-attachments/assets/859da3e5-f0be-43ac-9e7b-ac74ef05249e — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/82#issuecomment-5008173317), sgbaird, 2026-07-17) +- **[plot ×2]** Felt-sheet sweep analysis figures (saturation by condition, CH5 impact traces) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/5f10909/data/drop-tests/felt-sheet/figures/01_saturation_by_condition.png?raw=true and `.../03_ch5_impact_traces.png` — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/82#issuecomment-5008317721), Copilot, 2026-07-17) +- **[repo data]** Branch holds `data/drop-tests/{raw, vertex-acrylic, clip-height, felt-sheet}/` (TP4 raw CSVs + figures), `docs/drop-test-*.md`, `edison-trajectories/drop-test/` (task `653d7d39` literature report); TP4 training video https://youtu.be/RNjpAmWWmkQ — ([PR body](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/82), Copilot, 2026-07-13) +> Notable: largest raw-accelerometer trove in the range — 105 attached TP4 CSVs + 26 ZIPs covering height validation, 500-drop endurance, and the felt-sheet saturation sweep that set the 60 in / 5-felt standard condition. + +#### #83 — IDETC Presentation Preparation (issue, open) +- **[video]** Doumont "Creating effective slides" lecture to watch/take notes on — https://youtu.be/meBXuTIPJQk — ([issue body](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/83), sgbaird, 2026-07-15) +- **[PPTX/SharePoint]** EMC 2026 example presentation (recommended starting template) — https://byu-my.sharepoint.com/:p:/g/personal/sbaird9_byu_edu/IQBkaWNY4Dc8SaIReVu16NeaAW-baU-Lv20tLTG_rZoMa2U?e=G3tQLZ — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/83#issuecomment-4984075678), sgbaird, 2026-07-15) +- **[PPTX/SharePoint]** SDL101 workshop example presentation — https://byu-my.sharepoint.com/:p:/g/personal/sbaird9_byu_edu/IQCPPLlTXZn2Sa3HxkIWn1cOAcNsTC-Zp5bZRzFJdqiKzz0?e=b4aUYB — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/83#issuecomment-4984075678), sgbaird, 2026-07-15) +- **[PDF]** Doumont TM&Th-3.2 presentation-structure template — https://principiae.be/pdfs/TM&Th-3.2-template.pdf — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/83#issuecomment-4984906237), sgbaird, 2026-07-15) + +#### #84 — IDETC presentation prep: Doumont template and video notes (PR, open, branch `claude/issue-83-20260715-2018`) +- **[PDF]** me-madsen's handwritten notes on the Doumont presentation video — https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/84#issuecomment-4996599052), me-madsen, 2026-07-16) +- **[PDF]** Presentation Outline Draft 1 (7-17-2026), first pass at Doumont outline — https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/84#issuecomment-5007410130), me-madsen, 2026-07-17) +- **[YouTube ×6]** NASA Super Ball Bot candidate hook videos (NASA official overview ZBSRdGlAh5s, NIAC Titan concept 1wce-mB69mE, WSJ prototype drop L2cJej3EmcA, NASA 360 talk 0eC4A2PXM-U, SUPERball v2 hkzeE6BVNIk, unofficial re-upload Fz2wVkb13v4) — https://www.youtube.com/watch?v=1wce-mB69mE et al. — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/84#issuecomment-5011990234), claude[bot], 2026-07-18) +- **[image ×3]** Screenshots of bare-template slide-add behavior in PowerPoint — https://github.com/user-attachments/assets/7bac19e6-dbdc-4017-8395-f8a57b18a58a (+2 more) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/84#issuecomment-5039579640), sgbaird, 2026-07-21) +- **[Box folder]** "tensegrity" public folder with 4 snipped Super Ball Bot / Steve Mould MP4s (`youtube-.mp4`); correlation table committed as `presentation/video-snips.md` — https://byu.box.com/s/g719ao0glalis8l6l2plkq2ztvvh48s4 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/84#issuecomment-5039930842), sgbaird, 2026-07-21) +- **[PPTX]** `presentation/emc2026-bare-template.pptx` — stripped EMC 2026 deck (layouts only) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/b5dcc3d/presentation/emc2026-bare-template.pptx — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/84#issuecomment-5013282000), claude[bot], 2026-07-18) +- **[PPTX + PNG ×3]** `presentation/emc2026-idetc-demo.pptx` demo deck + 3 slide previews — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/20043df/presentation/emc2026-idetc-demo.pptx — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/84#issuecomment-5039567738), claude[bot], 2026-07-21) +- **[PPTX + PNG ×14]** IDETC Slides Draft 1: `presentation/Slide Decks/IDETC Tensegrity Slides Draft 1.pptx` (15 MB) with 14 rendered slide previews — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/b08d9bf/presentation/Slide%20Decks/draft1-previews/slide-01.png?raw=true (…slide-14) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/84#issuecomment-5081612419), claude[bot], 2026-07-26) +- **[repo data]** Branch also carries `presentation/edison-mock-audience/` (Edison mock PM/audience review artifacts) and transcriptions `presentation/doumont-{presentation-template,video-notes}.md` — https://github.com/vertical-cloud-lab/tensegrity-optimization/tree/claude/issue-83-20260715-2018/presentation — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/84#issuecomment-5011990234), claude[bot], 2026-07-18) +> Notable: complete presentation asset chain — source decks, outline/notes PDFs, Box folder of snipped hook videos, and the Draft 1 deck with per-slide previews. + +#### #85 — Defining the Search Space for T3 Optimization Campaign (issue, open) +- **[repo data]** Full 13-D campaign definition (7 continuous + 1 ordered + 5 categorical params, qNEHVI objectives F_peak≤2500 N / SEA≥0.05 J/g / η≥0.20) in `bo/tensegrity_campaign.py` — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/834ca15/bo/tensegrity_campaign.py — ([issue body](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/85), me-madsen, 2026-07-17) +- **[plot]** T3-prism search-space summary figure (5 geometric params, bounds + n=9 seed-0 Sobol batch, parallel coordinates; branch `claude/issue-85-20260717-2257`) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/40a9733/bo/t3-prism-search-space.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/85#issuecomment-5008302271), claude[bot], 2026-07-17) +- **[CSV]** The 9-specimen Sobol batch `bo/t3-prism-bo-batch.csv` (on PR #35 branch, with generator `bo/t3_prism_sobol_batch.py`) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/18c41a6/bo/t3-prism-bo-batch.csv — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/85#issuecomment-5008302271), claude[bot], 2026-07-17) +> Notable: canonical statement of the BO search space — the figure + CSV pair is the T3 campaign's actual seed batch; expert review pointers to PR #41 comments (Dr. Sheikh) in the 2026-07-20 me-madsen comment. + +#### #86 — Add drop-test protocol, Edison synthesis, and first-data analysis (PR, open, branch `copilot/add-drop-test-protocol-again`) +- **[YouTube ×2]** Slo-mo drop-test shorts of specimens 7xadt6 and 9GMQYQ — https://youtube.com/shorts/Nab3hfuF4Dw and https://youtube.com/shorts/zkum2JlHpYk — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/86#issuecomment-5036908261), ctrhjk, 2026-07-21) +- **[plot ×4]** Video-analysis figures for the 60 in / 5-felt validation (kinematics, impact zoom, montages of 7xadt6/9GMQYQ) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/copilot/add-drop-test-protocol-again/data/drop-tests/60in-5felts-validation/video/figures/05_video_kinematics.png — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/86#issuecomment-5038474732), claude[bot], 2026-07-21) +- **[plot ×2]** prc1kn DAQ figures: saturation audit + three-structure transmissibility comparison — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/copilot/add-drop-test-protocol-again/data/drop-tests/prc1kn-60in-5felt/figures/04_three_structure_comparison.png — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/86#issuecomment-5039933590), claude[bot], 2026-07-21) +- **[video ×2 + XML ×2]** prc1kn drop videos (959 fps Sony captures, GitHub-transcoded) + camera-metadata XMLs, with 20 mm calibration-grid discussion — https://github.com/user-attachments/assets/ce30a17c-8d38-4370-9cac-274611c31b19 , `.../97cb1761-...`; XMLs https://github.com/user-attachments/files/30246311/prc1kn-60in-5felt.video.1.XML , `.../30246312/...2.XML` — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/86#issuecomment-5039978703), me-madsen, 2026-07-21) +- **[plot ×5]** prc1kn video-analysis figures (kinematics, grid parallax, drop montages, three-specimen comparison) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/copilot/add-drop-test-protocol-again/data/drop-tests/prc1kn-60in-5felt/video/figures/08_video_kinematics.png — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/86#issuecomment-5039980816), claude[bot], 2026-07-21) +- **[Box folder]** Public non-expiring folder created for raw (unprocessed) high-speed drop videos, so Claude can download on demand — https://byu.box.com/s/kkhmvnj9ni19b57dryk3gdroqrp5uf0b — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/86#issuecomment-5040076463), sgbaird, 2026-07-21) +- **[CSV]** Input/output pair for specimen yqpmx1 (base CH5 vs top-of-specimen accelerometer) committed on branch — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/copilot/add-drop-test-protocol-again/data/drop-tests/input-output/raw/yqpmx1_Signal1.csv (+ `yqpmx1_Signal5.csv`) — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/86#issuecomment-5049562159), claude[bot], 2026-07-22) +- **[video ×2]** Drop-tower release-hook stuck-open troubleshooting videos (WD-40 attempt) — https://github.com/user-attachments/assets/b5efbce3-bd02-42be-8efc-b7fff0d049fe , `.../759b594e-...` — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/86#issuecomment-5051515083), me-madsen, 2026-07-22) +- **[Box folder]** Video data from ctrhjk's 100-drop prc1kn campaign — https://byu.box.com/s/ncvfn5shhg1ignoibg1c3wd03p31p6c2 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/86#issuecomment-5052596421), me-madsen, 2026-07-22) +- **[repo data]** Branch consolidates all drop-test data: `data/drop-tests/{raw, vertex-acrylic, clip-height, felt-sheet, 60in-5felts-validation, prc1kn-60in-5felt, input-output}/` + `docs/drop-test-*.md` + Edison trajectory `653d7d39`; TP4 training video https://youtu.be/RNjpAmWWmkQ — ([PR body](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/86), Copilot, 2026-07-20) +> Notable: the working drop-test PR — committed DAQ campaigns, slow-mo video + calibration-grid metadata, and the two Box folders that are the designated home for raw high-speed footage. + +#### #88 — alternatives to felt stack (issue, open) +- **[image]** Photo of Tractor Supply 4×6 ft rubber horse-stall mat surface showing the "dots" texture in question — https://github.com/user-attachments/assets/e7c4e107-9077-47f3-a5f6-1c76316be2bf — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/88#issuecomment-5079414584), sgbaird, 2026-07-25) +- **[plot/photo]** Annotated photogrammetric anvil/felt footprint measurement extracted from the prc1kn slow-mo video (branch `claude/issue-88-20260725-0916`) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/0ab242a/docs/figures/anvil-felt-footprint-measurement.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/88#issuecomment-5077958005), claude[bot], 2026-07-25) +- **[product snapshot]** McMaster-Carr Super-Cushioning Polyurethane Rubber Sheet 8514K521/8514K625 (70A 1/4 in — the decided replacement pads) — https://www.mcmaster.com/8514K63-8514K521/ — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/88#issuecomment-5079461202), sgbaird, 2026-07-25) +- **[repo doc]** `docs/drop-test-absorber-alternatives.md` — ranked durable-absorber alternatives + qualification protocol (branch `copilot/add-drop-test-protocol-again`, commit 5dd76e6) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/copilot/add-drop-test-protocol-again/docs/drop-test-absorber-alternatives.md — ([issue body](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/88), sgbaird, 2026-07-23) + +#### #89 — Is a high speed RPi cam a thing? (issue, open) +- **[PDF ×2]** Lansmont TP4 User's Guide + Quick Start Guide (DAQ manuals, originally attached to issue #36) — https://github.com/user-attachments/files/28125775/TP4_User.s.Guide_W20000-98-14_Rev.A.PDF and https://github.com/user-attachments/files/28125774/TP4_Quick.Start.Guide_W20000-98-15_Rev.A.PDF — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/89#issuecomment-5077793553), claude[bot], 2026-07-25) +- **[PDF ×2]** External Lansmont TP4/TP3 datasheets used for record-length spec check — https://www.rycobel.com/assets/uploads/downloads/testpartner/Lansmont_Test_Partner_4.pdf and https://www.lansmont.com/sites/default/files/2023-02/Lansmont_Test_Partner_3.pdf — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/89#issuecomment-5077793553), claude[bot], 2026-07-25) +- **[plot]** Video impact-zoom frame supporting the ~1000 fps minimum-fps finding (~1.6 ms pulse) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/32b009f/data/drop-tests/60in-5felts-validation/video/figures/06_video_impact_zoom.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/89#issuecomment-5066115004), claude[bot], 2026-07-24) +- **[plot ×3]** Post-impact deformation min-fps analysis (traces, spectra, decimation; result: ~120–240 fps suffices post-pulse; branch `claude/issue-89-20260724-0512`) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/6b60ab2/analysis/issue-89-postimpact-minfps/figures/01_postimpact_traces.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/89#issuecomment-5066381828), claude[bot], 2026-07-24) +- **[plot ×2]** Accelerometer record-length analysis (residual motion, record length vs threshold, brake catch +104 ms; branch `claude/issue-89-20260724-1400`) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/a4cada6/analysis/issue-89-accel-window/figures/01_capture_residual_motion.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/89#issuecomment-5070678518), claude[bot], 2026-07-24) +- **[plot ×4]** Minimum DAQ sample-rate study via synthetic downsampling of the 25 prc1kn captures (spectral content, pulse vs rate, metric errors, trigger margin; branch `claude/issue-89-20260725-1855`) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/538c936/analysis/issue-89-min-sample-rate/figures/02_pulse_vs_rate.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/89#issuecomment-5080128539), claude[bot], 2026-07-25) +- **[plot ×2]** Raw-peak saturation-audit bandwidth dependence (crest attenuation, silent clipping; branch `claude/issue-89-20260725-1939`) — https://github.com/vertical-cloud-lab/tensegrity-optimization/blob/5a426fd/analysis/issue-89-raw-peak-bandwidth/figures/01_crest_attenuation.png?raw=true — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/89#issuecomment-5080290500), claude[bot], 2026-07-25) +- **[video]** TP4 training video timestamped to the recording-setup section — https://youtu.be/RNjpAmWWmkQ?t=726 — ([comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/89#issuecomment-5077793553), claude[bot], 2026-07-25) +> Notable: four separate `analysis/issue-89-*` figure sets (each on its own `claude/issue-89-*` branch) quantifying camera fps and DAQ sample-rate requirements from the committed prc1kn data, plus the TP4 manuals as attachments. + +--- + +## Part 3 — By branch (committed files) + +#### Branch: `main` +- **[archive/⚠️ 12.6 MB]** `Marcus_1.zip` — raw accelerometer drop-test data set (Marcus specimen), zipped CSVs +- **[archive/⚠️ 12.6 MB]** `jin_1.zip` · **[⚠️ 12.6 MB]** `jin_2.zip` — raw accelerometer drop-test data sets (jin specimen), zipped +- **[archive/⚠️ 13.3 MB]** `prc1kn - set 1 - 1.zip` — raw drop-test data, prc1kn tensegrity specimen set 1 +- **[csv]** `idetc-cie-2026-tracks-topics.csv` (168 KB) — IDETC-CIE 2026 conference tracks/topics listing +- **[image/⚠️ 5.3 MB]** `figures/overview-updated.png` — updated project-overview figure; plus `figures/overview.png` (67 KB), `figures/overview.pdf` (45 KB), `figures/overview.mmd` (Mermaid source) +- **[pdf]** `idetc-abstract.pdf` (4.96 MB) + `idetc-abstract-diff.pdf` — compiled IDETC extended abstract and latexdiff +- **[pdf]** `proposal.pdf` + `proposal-diff.pdf` (4.94 MB each) — compiled MRG proposal and diff +- **[pdf]** `MRG_2026.pdf` (139 KB), `MRG_Dillon_Hill_2023.pdf` (1.25 MB), `cv-1605798 _ Hill.pdf` (36 KB), `MRG_Cover_page.docx` (25 KB) — grant docs/CVs +- **[pdf dir]** `literature/` — 9 reference PDFs, ~23 MB total (BO reviews, TPU/ABS honeycomb energy absorption, multifidelity BO, tensegrity papers) +- **[latex/bib]** `proposal.tex`, `idetc-abstract.tex`, `sections/*.tex`, `references.bib`, `sterling-cv/*` — proposal/abstract sources + +#### Branch: `copilot/get-video-drop-test-data` (≈ PR #67; superseded by #82/#86) +Total ≈ 1.7 GB of drop-test data under `data/drop-tests/` — each experiment dir has `README.md`, `raw/*.csv` (~3 MB per-shot accelerometer traces), `figures/*.png` + `*_metrics.json`: +- **[csv+figs]** `data/drop-tests/100drops/` (108 files, 306 MB) — 100-drop repeatability campaign +- **[csv+figs]** `data/drop-tests/200drops/` (208 files, 608 MB) — 200-drop endurance campaign +- **[csv+figs]** `data/drop-tests/200drops-check/` (34 files, 92 MB) — post-200drops sanity check +- **[csv+figs]** `data/drop-tests/30drops-real/` (39 files, 98 MB) — 30 drops with real specimen +- **[csv+figs]** `data/drop-tests/5in-100drops/` (108 files, 305 MB) — 5-inch height 100-drop series +- **[csv+figs+video]** `data/drop-tests/burn-in-wax/` (17 files, 68 MB) — wax burn-in test; incl. ⚠️ `video-example/drop5.mp4` (52 MB) high-speed drop video + `video-figures/` kinematics PNGs +- **[csv+figs]** `data/drop-tests/ch4-trigger/` (57 files, 154 MB) — channel-4 trigger configuration tests +- **[csv+figs]** `data/drop-tests/drift-calibration/` (36 files, 57 MB) and `drift-calibration2/` (56 files, 95 MB) — sensor drift calibration runs +- **[csv+figs]** `data/drop-tests/input-output/` (25 files, 39 MB) — input/output transfer tests +- **[csv+figs]** `data/drop-tests/key-mounted/` (12 files, 10 MB) and `key-mounted-wax/` (10 files, 9.9 MB) — key-mount fixture tests +- **[csv+figs]** `data/drop-tests/vertex-acrylic/` (12 files, 15.7 MB) — acrylic vertex specimen test +- **[figs]** `data/drop-tests/clip-height/` (5 files, 2.1 MB), `prc1kn-health/figures/` (3 PNGs + metrics), `data/drop-tests/figures/` (5 PNGs) +- **[txt]** `data/drop-tests/raw/Signal_*.txt` (5 files, 9.5 MB) — first raw signal exports (PETG/audrey/control) +- **[scripts]** `scripts/analysis/` — 18 Python drop-test analysis scripts (per-campaign peak-g/OLS/saturation/video analysis) +- **[docs]** `docs/drop-test-*.md` — 18 per-campaign analysis writeups + protocol +- **[edison]** `edison-trajectories/{clip-height,drop-test,input-output}/` — Edison task JSON/MD + 1.27 MB notebook + `scripts/edison/` submit/fetch scripts + +#### Branch: `copilot/add-drop-test-protocol-again` (≈ PR #86; strict superset of PR #82 branch and #67 branch) +Everything from `copilot/get-video-drop-test-data`, plus ≈ 2.5 GB more: +- **[csv+figs]** `data/drop-tests/500drops/` (261 files, 782 MB) — 500-drop endurance campaign (overload-diagnosis figures) +- **[csv+figs]** `data/drop-tests/500drops-nobot/` (505 files, 948 MB) — 500 drops without bottom sensor, runs 1–2 +- **[csv+figs]** `data/drop-tests/5vs10/` (64 files, 183 MB) — 5 in vs 10 in drop-height comparison +- **[csv+figs]** `data/drop-tests/200drops-check2/` (34 files, 92 MB) — second 200-drop verification +- **[csv+figs]** `data/drop-tests/felt-sheet/` (50 files, 86 MB) — felt-sheet absorber-layer sweep +- **[zip]** `data/drop-tests/7xadt6 _60in_5felts folder/` — ⚠️ `Marcus_1..4.zip` (4 × ~12.7 MB) raw 60 in/5-felt data, specimen 7XADT6 +- **[zip]** `data/drop-tests/9GMQYQ_60in_5felts/` — ⚠️ `jin_1..4.zip` (4 × ~12.7 MB) raw 60 in/5-felt data, specimen 9GMQYQ +- **[zip]** `data/drop-tests/7-22-2026 prc1kn 100drops/` — ⚠️ `prc1kn100_1..4.zip` (4 × ~13.1 MB) prc1kn 100-drop raw data +- **[zip+video+figs]** `data/drop-tests/prc1kn-60in-5felt/` (22 files, 213 MB) — ⚠️ `prc1kn - set 1 - 1..4.zip` (4 × ~13.3 MB), ⚠️ `video/prc1kn_video1_slomo.mp4` (70.8 MB), ⚠️ `prc1kn_video2_slomo.mp4` (85.5 MB) slow-motion drop videos + Sony XML metadata + video-kinematics/montage PNG figures +- **[video+figs]** `data/drop-tests/60in-5felts-validation/` (22 files, 16.7 MB) — validation drops; ⚠️ `7xadt6_slomo.mp4` (5.8 MB), ⚠️ `9GMQYQ_slomo.mp4` (7.2 MB), poster/maxres JPG frames, video montage PNGs +- **[figs]** `data/drop-tests/sample-size/` — CV-vs-N sample-size analysis figures + metrics JSON +- **[scripts]** `scripts/analysis/` — 10 additional analysis scripts (500drops, 5vs10, felt-sheet, 60in-5felts video, prc1kn video, sample-size) +- **[docs]** `docs/` — 11 additional analysis MDs (incl. absorber alternatives, hook troubleshooting) + +#### Branch: `copilot/add-drop-test-protocol` (≈ PR #82) +Strict subset of `copilot/add-drop-test-protocol-again`: has 500drops, 500drops-nobot, 5vs10, 200drops-check2, felt-sheet, 7xadt6/9GMQYQ zips (~1.9 GB) but lacks the prc1kn-60in-5felt videos, 60in-5felts-validation, 7-22-2026 zips, and sample-size analysis. + +#### Branch: `copilot/tune-accelerometers-drop-tower` (≈ PR #74, issue #71) +- **[csv]** `data/drop-tests/accelerometer-tuning/raw/` (14 CSVs, ~26 MB) + `peak_summary.csv` — initial accelerometer tuning shots 06.02.2026 +- **[csv]** `data/drop-tests/accelerometer-calibration/raw/` (8 CSVs, ~15 MB; 500G/1000G signals) + summary — cross-calibration run 1 +- **[csv]** `data/drop-tests/accelerometer-calibration-2/raw/` (15 CSVs, ~29 MB) and `accelerometer-calibration-3/raw/` (15 CSVs, ~34 MB) + summaries — height-sweep calibration runs +- **[image dir]** `docs/figures/accelerometer-{tuning,calibration,calibration-2,calibration-3}/` — 26 PNGs (~2.9 MB): event time series, PSD, clipping, cross-calibration regressions +- **[scripts]** `scripts/analysis/accelerometer_*_analysis.py` (4 scripts) + `scripts/edison/submit_accelerometer_tuning.py` +- **[edison]** `edison-trajectories/accelerometer-tuning/` — task JSON/MD, 107 KB notebook, reviewer-artifacts (CSV + alignment PNG) + +#### Branch: `copilot/explore-simulations-for-tensegrity` (≈ PR #33) +- **[sim outputs]** `simulations/outputs/` (128 files, 27.4 MB) — 26 CSVs (Sobol tiers A/B/C, pareto_crutch/lander, BO campaigns B/C, sweeps), 72 PNGs (convergence, CV, Pareto, violins, heatmaps), 7 NPZ raw traces (mujoco/pybullet/pychrono/newton/diffpd/polyfem drops), 10 MP4 + 10 GIF drop-simulation renders, 2 interactive HTML violin plots +- **[archive/⚠️ 25.9 MB]** `simulations/outputs_bundle.zip` — zipped bundle of all simulation outputs +- **[scripts]** `simulations/*.py` (~25 scripts) — engine drop demos (MuJoCo/PyBullet/PyChrono/Newton/DiffPD/PolyFEM), Sobol T3 campaign + diagnostics, sim-BO campaigns, regime sweeps, renderers; plus `simulations/*.md` analysis writeups +- **[edison]** `edison-trajectories/{sim-survey, fair-evaluation, modeling-feedback-contacts (2.1 MB JSON), payload-vs-no-payload (2.2 MB JSON), sim-bo-review, simulation-bo-value, sobol-t3-results}` + `scripts/edison/` (10 scripts) + +#### Branch: `copilot/explore-joint-design-for-petg-tpu` (≈ PR #39) +- **[cad]** `cad/joint-design/*.scad` (6 joint concepts A–F + section views, `_common.scad`, render.sh) and `A_variants/*.scad` (6 anchor-bulb variants A0–A5) +- **[cad renders]** `cad/joint-design/renders/` — 6 STLs + ~35 iso/section PNGs + comparison montages (`all_compare_montage.png` 2.8 MB) +- **[cad renders]** `cad/joint-design/A_variants/renders/` — 6 STLs (0.8–2.0 MB) + 12 PNGs + 3 montages +- **[image]** `cad/joint-design/references/` — 5 reference JPGs (3-prism, dovetail, hose clamp, chain link, rebar loop) +- **[edison]** `edison-trajectories/joint-design/` — per-concept literature JSON/MD (A–E), PHASE2 analysis, `PHASE3-cad-review-*.json` (2.4 MB vision review), TPU topology comparison + 3 submit scripts + +#### Branch: `copilot/add-manual-supports` (≈ PR #66) +- **[cad/scripts]** `cad/print-supports/` — support-enforcer/pillar generators (`generate_support_pillars.py` 72 KB), Bambu process JSON, HOW-TO-PRINT +- **[cad verification]** `cad/print-supports/verification/` — 20 Python FEA/gcode/slicing verification scripts; STL outputs: ⚠️ `t3-prism-pr35-tpu-enforced-object-and-supports.stl` (14.6 MB), ⚠️ `t3-prism-pr35-tpu-enforced-supports.stl` (13.3 MB), ⚠️ `t3-prism-pr35-th10-object-and-supports.stl` (6.8 MB), ⚠️ `t3-prism-pr35-th10-supports.stl` (5.6 MB), plus 4 smaller STLs; 13 preview/FEA PNGs (~5.6 MB), 2 rotating GIFs, 5 report/sweep JSONs +- **[edison]** `outputs/edison-pr35-print-advice/` — answer.md, references, 1.1 MB task_result.json + +#### Branch: `copilot/get-bambu-sliced-print-t3-prism` (≈ PR #35) +- **[cad]** `cad/t3-prism/` — `t3-prism.scad` (46 KB parametric), STLs (`t3-prism.stl` 1.1 MB, `t3-prism-struts.stl` 2.5 MB, `t3-prism-struts-scaffold.stl` 3.3 MB, cables 766 KB), 3MFs (`t3-prism.3mf`, `t3-prism1.1.3mf`), support-preview PNGs, render/upload scripts (`onshape_upload_t3prism.py`) +- **[slices]** `cad/t3-prism/slices/` — 5 Bambu H2D sliced 3MFs incl. `t3-prism.H2D-PETG.gcode.3mf` (4.6 MB) and multimaterial PLA/TPU profiles +- **[bo batch]** `bo/` — Sobol batch generator `t3_prism_sobol_batch.py` (45 KB), `t3-prism-bo-batch.{csv,json,scad}`, batch STLs, 18 per-specimen STLs (`bo/per-specimen-stls/`, ~2.7 MB total), sliced batch 3MF, plate/iso PNGs +- **[edison]** `edison-trajectories/2026-05-08-t3-prism-bambu-import-25c1c897.{json,md}` (262 KB) + +#### Branch: `copilot/sub-pr-35` (≈ PR #48) +- **[cad]** `cad/t3-prism/` earlier variant — `t3-prism.scad`, STLs (`t3-prism-cables.stl` 3.2 MB thick-cable, `t3-prism-thick.stl` 649 KB, `t3-prism.stl` 666 KB, struts 561 KB), iso PNGs, 5 sliced 3MFs (0.2–1.6 MB, incl. thick-PETG-supports gcode), `flatten_bambu_profile.py`, `patch_mm_extruder.py`, `render_print.sh` + +#### Branch: `copilot/design-array-of-prints-anchor-bulb` (≈ PR #52) +- **[cad]** `cad/anchor-bulb-tensioning-array/` — 18 specimen SCADs (H-S{0..2}G{0..4} + sections), `_common.scad`, `tensioning_array.scad`, render.sh +- **[cad renders]** `renders/` — 15 specimen STLs (~22 MB total), ⚠️ `tensioning_array.stl` (22.4 MB full 15-specimen plate), 18 iso PNGs, specimen/section montages + +#### Branch: `copilot/fetch-designs-for-tensegrity-structures` (≈ PR #22) +- **[cad]** `models/stl/` — 18 canonical tensegrity STLs (~3.7 MB total: 3/4/6-bar prisms, icosahedron, Snelson X, Geiger dome, biotensegrity spine, SUPERball w/ payload, masts, columns, tessellations) + `models/generate_stl.py` +- **[image]** `figures/tensegrity_models_{preview,extended_preview,gapfollowup_preview}.png` (~1.0 MB) +- **[edison]** `edison-trajectories/2026-05-09-tensegrity-designs-fad054b3.json` (2.1 MB) + MD/references; design-gaps followup JSON/MD + submit script + +#### Branch: `copilot/create-manuscript-template` (≈ PR #20) +- **[pdf]** `manuscript/manuscript.pdf` ⚠️ 7.6 MB, `manuscript-diff.pdf` ⚠️ 7.6 MB, `manuscript-todos.pdf` ⚠️ 7.6 MB, `supplementary.pdf` (89 KB) — compiled JMD manuscript builds + TeX sources and `references-full.bib` (886 KB master bib) +- **[latex figs]** `figures/fab-workflow.pdf` (1.6 MB TikZ) + `.tex`; `figures/examples/` — 6 Ax placeholder figure pairs (PDF+PNG, convergence/LOOCV/Pareto/sensitivity/mechanistic, ~1.6 MB) + contact sheet +- **[image]** `figures/photos/` — 6 lab photos (drop-tower.jpg, printed-batch/specimen, printing-in-progress, multimaterial-slice, cad-render; ~2.2 MB); `manuscript/preview/manuscript-versions.png`, `preview/manuscript-real-figures.png` +- **[scripts]** `scripts/figures/` (3 figure generators), `scripts/edison/` (11 bib/review scripts incl. `verify_bib_dois.py`) +- **[edison]** `edison-trajectories/{mock-jmd-review, bib-doi-verification, mechfig-feedback (380 KB JSON), mock-jmd-review-2, review-followups}` + `copilot-session-*.md` (530 KB session log) + +#### Branch: `copilot/vertical-cloud-labtensegrity-optimization` (≈ PR #76) +Superset of PR #20 branch (same manuscript/figures/photos, PDFs ⚠️ 7.6 MB ×3), plus: +- **[edison]** `edison-trajectories/mock-jmd-review-{3,4,5}/` — additional mock JMD reviews with notebooks + JSON/MD; matching submit/fetch scripts; `ISSUE_75_CONTEXT.md` + +#### Branch: `claude/issue-83-20260715-2018` (≈ PR #84, issue #83 — current branch) +- **[pptx/⚠️ 15.0 MB]** `presentation/Slide Decks/IDETC Tensegrity Slides Draft 1.pptx` — IDETC talk draft 1 +- **[image dir]** `presentation/Slide Decks/draft1-previews/` — 14 slide PNGs (~1.6 MB) +- **[pptx]** `presentation/emc2026-bare-template.pptx` (1.7 MB), `presentation/emc2026-idetc-demo.pptx` (1.7 MB) + `emc2026-idetc-demo-previews/` (3 PNGs) +- **[edison]** `presentation/edison-mock-audience/` — mock PM/audience review: `task-response.json` (2.0 MB), `slide-review-task-response.json` (3.1 MB), memory JSONL artifacts, notebooks, feedback MDs, submit/fetch scripts +- **[docs]** `presentation/doumont-presentation-template.md`, `doumont-video-notes.md`, `video-snips.md` + +#### Branches: `claude/issue-89-*` (issue #89 — camera/DAQ requirement analyses; 4 sibling branches) +- `claude/issue-89-20260724-0512`: **[analysis]** `analysis/issue-89-postimpact-minfps/` — script, 3 figure PNGs (traces/spectra/decimation), metrics JSON +- `claude/issue-89-20260724-1400`: **[analysis]** `analysis/issue-89-accel-window/` — script, 2 PNGs (capture residual, record-length-vs-threshold), metrics JSON +- `claude/issue-89-20260725-1855`: **[analysis]** `analysis/issue-89-min-sample-rate/` — script (26 KB), 4 PNGs (spectral content, pulse-vs-rate, metric errors, trigger margin), metrics JSON (44 KB) +- `claude/issue-89-20260725-1939`: **[analysis]** `analysis/issue-89-raw-peak-bandwidth/` — script, 2 PNGs (crest attenuation, silent clipping), metrics JSON + +#### Branch: `claude/issue-85-20260717-2257` (issue #85) +- **[image]** `bo/t3-prism-search-space.png` (371 KB) + `bo/plot_t3_prism_search_space.py` — BO search-space visualization + +#### Branch: `claude/issue-88-20260725-0916` (issue #88) +- **[image]** `docs/figures/anvil-felt-footprint-measurement.png` (1.4 MB) — annotated anvil/felt footprint measurement photo + +#### Branch: `claude/issue-87-20260723-1655` (issue #87) +- **[doc]** `literature/pretension-activation-methods.md` (13 KB) — pretension activation literature note + +#### Branches: `claude/issue-78-*` (issue #78, TMS 2027 abstract; PR #79 = `-2202`) +- `-2122`: **[edison]** `reviews/edison/` — mock review MD/JSON set (review 44 KB, answer 31 KB, key-references + terminology tables) +- `-2146`: **[csv]** `reviews/tms2027/tms2027_symposium_organizers.csv` (150 KB) + symposium-fit analysis MD +- `-2202` (PR #79): **[edison]** `reviews/edison-symp105-personas/` — persona review prompt/metadata +- `-2241`: **[edison]** `reviews/edison-symp105-personas/` — full persona review results (`answer_raw.json` 605 KB, formatted review 57 KB, references, comparison table) + +#### Branch: `copilot/add-drop-tower-picture` (≈ PR #28) +- **[pdf]** `equipment/lansmont-m23/` — `Lansmont_M23_Shock.pdf` (270 KB), `M23_Data_Sheet_official.pdf` (625 KB) + datasheet photo PNG (403 KB) +- **[pdf/image]** `equipment/polytec-qtec/` — `Polytec_Datasheet_VibroFlex_QTec.pdf` (561 KB), hero PNG, 5 WEBP product images +- **[edison]** `edison-trajectories/2026-05-08-equipment-m23-qtec-*.{json,md}` + +#### Branch: `copilot/create-latex-template-proposal` (≈ PR #14) +- **[pdf]** `nasa-space-grant/proposal.pdf` ⚠️ 5.0 MB, `proposal-diff.pdf` ⚠️ 5.0 MB; `nasa-space-grant-audrey/proposal.pdf` (4.98 MB) + diff; `nasa-26.pdf` (204 KB solicitation) — NASA Space Grant proposal builds + TeX/bib sources + +#### Branch: `copilot/analyze-dynamic-analysis-structures` (≈ PR #58) +- **[pdf/⚠️ 14.2 MB]** `literature/davami2025-dynamic-am-tensegrity.pdf` — Davami 2025 dynamic AM tensegrity paper + `davami2025-analysis.md` +- **[edison]** `edison-trajectories/davami2025-followup/` JSON/MD + submit script + +#### Branch: `copilot/create-abstract-for-tms-2027` (≈ PR #73) +- **[pdf]** `edison-trajectories/tms-symposium-fit/bundle/TMS2027-CFA-Flyer.pdf` (216 KB) + CFA text, abstract MD +- **[edison]** symposium-fit JSON (212 KB)/MD + submit/fetch scripts; `tms-2027-abstract.md` + +#### Branch: `copilot/run-initial-instron-tests` (≈ PR #50) +- **[edison]** `edison-trajectories/instron-stiffness/` — literature brief: JSON (2.3 MB), MD (122 KB), references.bib, equipment-selection MD + submit script + +#### Branch: `copilot/explore-objective-functions` (≈ PR #60) +- **[edison]** `edison-trajectories/objective-functions/` — 5 instrument briefs (accelerometer, high-speed camera, shaker, gas gun, LDV) JSON+MD pairs (~880 KB) + 2 submit scripts + +#### Branch: `copilot/explore-impact-absorption-crutches` (≈ PR #18) +- **[edison]** `edison-trajectories/01–13*` — 13 crutch-tip feasibility/mock-review JSON+MD pairs (~1.1 MB) + `crutch-tip-abstract.md` + +#### Branch: `copilot/explore-egg-drop-idea` (≈ PR #47) +- **[edison]** 4 egg-drop trajectory JSON+MD pairs (~570 KB) + 4 submit scripts + +#### Branch: `copilot/explore-tpu-petg-variables` (≈ PR #24) +- **[edison]** TPU/PETG BO-variables + heterogeneous-params trajectories (JSON/MD, ~300 KB) + submit script + +#### Branch: `copilot/who-is-the-target-audience` (≈ PR #41) +- **[edison]** target-audience + outreach-topics trajectories (JSON/MD ~250 KB), `reviews/target_audience.md`, submit script + +#### Branch: `copilot/search-funding-opportunities` (≈ PR #43) +- **[edison]** `edison-responses/` — 3 funding-venue/program-officer literature responses (JSON/MD, ~430 KB) + +#### Branch: `copilot/research-strut-material-options` (≈ PR #45) +- **[edison]** strut-material-selection trajectory (JSON 83 KB, MD, references, summary) + submit script + +#### Branch: `copilot/create-repo-project-name` (≈ PR #26) +- **[edison]** 2 naming trajectories (JSON/MD, ~90 KB) + +#### Branch: `copilot/scaffold-bayesian-optimization-script` (≈ PR #30) +- **[scripts]** `bo/` — `tensegrity_campaign.py` (21 KB Ax/honegumi campaign), `tensegrity_bo.py`, `generate_scaffold.py`, tests, requirements + +#### Branch: `copilot/run-literature-searches-nasa-priorities` (≈ PR #16) +- **[edison]** `edison-responses/2026-05-08-nasa-priorities-literature-*.md` (67 KB) + +#### Branch: `copilot/explore-strut-curvature-impacts` (≈ PR #54) +- **[edison]** `edison-trajectories/strut-curvature-explorations.md` (13 KB) + +#### Branch: `copilot/explore-tendon-cable-printing-angles` (≈ PR #62) +- **[doc]** `reviews/tendon_print_angle_impact.md` (18 KB) — FFF tendon print-angle analysis + +#### Branch: `copilot/fix-multi-part-assignment-issue` (≈ PR #64) +- **[doc]** `manufacturing-notes/bambustudio-cli-multimaterial-bug.md` (10 KB) + +#### Branch: `copilot/who-to-contact-at-byu` (≈ PR #70) +- **[doc]** `reviews/byu-kimball-tower-drop-outreach.md` (7 KB) + +#### Branch: `copilot/suggest-tensegrity-kits` (≈ PR #56) +- **[doc]** `reviews/tensegrity-build-kits.md` (9 KB) + +**No unique data beyond main:** `add-claude-github-actions-1782769721754` (#77), `80-claude-ping-test` (#81), `copilot/conduct-mock-reviews` (#8), `copilot/final-updates-bio-sketches` (#10), `copilot/increase-experimentation-mentoring` (#4/#5), `overleaf-2026-03-12-2145`, `overleaf-2026-03-31-2045`, and the six `claude/issue-78-{20260701-2304, 20260702-0010/0022/0028/0130/0146}` branches (successive `tms2027-abstract.md` text drafts only). diff --git a/presentation/deck-edit-screenshots/round2-final-slide39.png b/presentation/deck-edit-screenshots/round2-final-slide39.png new file mode 100644 index 00000000..04e7b2d3 Binary files /dev/null and b/presentation/deck-edit-screenshots/round2-final-slide39.png differ diff --git a/presentation/deck-edit-screenshots/round2-start-slide37.png b/presentation/deck-edit-screenshots/round2-start-slide37.png new file mode 100644 index 00000000..3c743e69 Binary files /dev/null and b/presentation/deck-edit-screenshots/round2-start-slide37.png differ diff --git a/presentation/deck-edit-screenshots/round2-video-slide38.png b/presentation/deck-edit-screenshots/round2-video-slide38.png new file mode 100644 index 00000000..2de1ea3a Binary files /dev/null and b/presentation/deck-edit-screenshots/round2-video-slide38.png differ diff --git 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b/presentation/doumont-presentation-template.md @@ -0,0 +1,419 @@ +# IDETC Presentation — Doumont Presentation Structure Template + +Transcribed from Jean-luc Doumont's presentation-structure template +([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)), +adapted from *Trees, maps, and theorems* (Principiae, 2009). © 2009 by Principiae. +All rights reserved. Can be downloaded from . + +> To design your presentation, write down your ideas for each component below. +> If useful, you can then further develop your body afterwards (the "back of the sheet"). + +**Draft 3** — applies the inline review comments from @sgbaird on PR #84 and the +priority revisions from the +[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md). +Summaries of what changed are at the bottom of this file +([Draft 2 → 3](#changes-from-draft-2), [Draft 1 → 2](#changes-from-draft-1)). + +**Scope note (frame everything below with this):** the study presented is the +optimization of a **T3-prism tensegrity-inspired structure together with FDM +processing parameters** (e.g., nozzle temperature, print speed). PLA–TPU is a +**proxy system** — not flight material — used to prototype a self-driving-lab +style, near-autonomous workflow for optimizing tensegrity-inspired structures. +The planetary lander is *motivation*, not demonstrated readiness. Everything +beyond the T3 study (lattices, flight materials, crutch tip) is future work. + +## Opening + +### Attention getter + +*A way to lead the audience to the need efficiently* + +Tensegrity provides robust, **reusable** solutions to issues posed by planetary +landings — and to problems closer to home. + +One or two sentences of setup, then let the Super Ball Bot image (or a short +clip) do the work: + +> Landing a payload on another planet means surviving an impact in conditions +> where parachutes and retrorockets struggle — thin atmospheres, rough +> terrain. NASA's Super Ball Bot concept answers this with a tensegrity +> structure: the lander *is* the energy absorber. And unlike crushable +> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the +> hit **and get back up** — it survives multiple drops and keeps working. + +Candidate Super Ball Bot videos to show (verified links): + +- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) — NASA Video + (official channel); the canonical overview. +- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA) + — Wall Street Journal; prototype drop footage. +- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE) + — NASA Video; mission-concept animation (Titan descent) — strong hook material. + +Immediately after the hook, state the present study scope in one sentence +(per the scope note above): *we study a printed PLA–TPU T3 prism as a proxy +system for prototyping an autonomous tensegrity-optimization workflow* — so +the planetary framing never outruns the demonstrated scope. + + + +### Need + +*A difference between actual and desired situations* + +**Actual situation** (a two-sided pincer — stated as a scoped engineering +judgment, not an absolute): + +- For these structures, obtaining trustworthy objective values from a + validated high-fidelity model would require substantial calibration of + interface, rate, contact, and defect behavior — and even heavy-duty + simulation falls short of the experiments we are running: peak transmitted + shock is a millisecond-scale transient that is notoriously difficult to + resolve numerically. This holds even for idealized cable–strut tensegrities + in their actual engineering materials (steel or similar), not just for + printed PLA–TPU. + +- Exhaustive physical search fails on cost: a traditional design of + experiments over this relatively high-dimensional space (geometry + FDM + process parameters) would demand hundreds of specimens, each costing print + and test time. +- Assembly does not scale: a single T3 prism can be assembled by hand, but + more complex structures — especially tensegrity *lattices* — become + extremely difficult and ultimately infeasible to hand-build. + +**Desired situation:** + +- Trustworthy performance data without hand assembly (easy creation of + tensegrity structures, scalable beyond a single prism). +- Quick iteration and testing — find good designs in dozens of experiments, + not the hundreds a traditional design of experiments would require. + +### Task + +*What I decided/was asked to do to address the need* + +Build a closed-loop design–print–test workflow that optimizes a **T3-prism +tensegrity-inspired energy absorber together with its FDM processing +parameters** (e.g., temperature, print speed) directly from real, measured +impact data — maximizing energy absorbed per gram (specific energy +absorption) and compaction efficiency, subject to a cap on the peak force +transmitted to the payload. Extensions beyond the T3 prism are future work. + +### Main message + +*The one sentence I want my audience to remember* + +By closing the loop between multi-material 3D printing and Bayesian +optimization, we can optimize tensegrity-inspired energy absorbers directly +from real impact data — in dozens of prints, not the hundreds a traditional +design of experiments would demand. + + + +### Preview + +*A map of the body (ideally three points, max. five)* + +1. When model calibration and exhaustive testing are both costly, each + physical specimen must be chosen for its information value — so the + measured experiment is our source of truth. +2. Bayesian optimization makes physical experimentation affordable: it handles + noisy measurements, small batches, and the constrained multi-objective + trade-off — and Honegumi let us scaffold it in minutes. +3. Multi-material additive manufacturing closes the loop: single-build PLA–TPU + co-fabrication turns each proposed design into test data in hours, with no + joining of parts. +4. *(Optional fourth point, if time allows)* The same closed loop points at + what comes next: tensegrity lattices, flight-relevant materials, and + safety hardware on Earth. + +## Body + +### Point 1 + +When model calibration and exhaustive testing are both costly, each physical +specimen must be chosen for its information value. + +- Scoped, not absolute (do **not** say "simulation can't be trusted"): for + these structures, a sufficiently calibrated multiphysics model is expensive + to build relative to the design campaign itself — and the hardest quantity, + peak transmitted shock, is a millisecond-scale transient that even + heavy-duty simulation struggles to resolve. The gap persists in the actual + engineering materials a real lander would use (steel struts and cables), + so this is not merely an artifact of printed PLA–TPU. +- The alternative — sweeping the design space physically — fails on cost: a + traditional design of experiments over geometry plus FDM process parameters + would take hundreds of specimens, each needing print + test time. +- Therefore: treat the physical experiment as the authoritative source of + objective values, and be ruthless about *which* experiments to run. +- Anticipate the FEA-veteran pushback ("inconvenient ≠ untrustworthy"): frame + it as a cost-and-dependence decision, and keep a backup slide with the + literature (or our own validation) on simulation–experiment discrepancy for + impact transients. + +### Transition + +If every data point must be a real experiment, we need a method that extracts +the most from every specimen — we need to make the process radically more +sample-efficient. + +### Point 2 + +Bayesian optimization makes physical experimentation affordable, even with +limited, noisy data. + +- Two-level explanation (per mock-audience feedback): one plain sentence + one + visual in the main talk; implementation detail goes to backup slides. + Spoken version: + > "After each batch, the model estimates both expected performance and + > uncertainty across the design space. The acquisition rule selects a small + > next batch that is most likely to expand the feasible trade-off frontier, + > while accounting for noisy tests and print failures." +- Noisy physical measurements: TPU batch-to-batch variation and print defects + motivate a noise-aware acquisition function (qNEHVI) rather than + non-noise-aware alternatives. (Backup slide: how observation noise is + actually modeled — replicates, noise identification — and budget-matched + baselines such as Sobol/random sampling.) +- Small parallel batches: the surrogate proposes the next batch of specimens + to print, balancing exploration and exploitation. +- Constrained multi-objective by construction: **objectives** = maximize + specific energy absorption and compaction efficiency; **constraint** = peak + transmitted force must stay under a cap; the deliverable is the *feasible* + Pareto front between the two objectives among force-cap-satisfying designs. + (Keep this formulation consistent everywhere — do not present "SEA vs. peak + force" as the trade-off.) +- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for + this advanced task (noisy, batched, constrained, multi-objective) in + minutes using [Honegumi](https://honegumi.readthedocs.io/) — the audience + can do the same for their own problems; show the code-template picker + briefly so they see how low the barrier is. + +### Transition + +The optimizer is no longer the bottleneck — the limit is now providing real +data quickly enough. + +### Point 3 + +Multi-material additive manufacturing supplies that data fast — single-build +co-fabrication, with no joining of parts. + +- Single-build PLA–TPU co-fabrication: rigid struts and flexible tension + network in one print — no joining or hand assembly of members, which closes + the "easy creation" gap from the Need and is what makes future lattices + feasible at all. (Be precise: if support removal, trimming, or conditioning + remain, say "no joining of PLA and TPU members" rather than "zero + assembly.") +- Show the mechanism, not just the word "tensegrity-inspired": a labeled + as-printed specimen (rigid PLA struts, continuous TPU tension network, + pre-tension status) plus a short deformation sequence — so the EDL engineer + doesn't file it as a "crush structure" and the grad student can tell it from + an ordinary lattice. +- Rapid iteration: each BO-proposed candidate goes from parameter vector to + tested specimen (quasi-static compression + instrumented drop-weight impact) + in hours. (Backup slide: representative print-to-result time breakdown and + which steps remain manual — candidate selection is automated; slicing, + handling, and testing are not yet.) +- Now we have the data we need to complete the loop. + +### Point 4 (optional — future applications) + +*Include if timing allows; otherwise fold into the Conclusion.* + +The same closed loop transfers wherever tests are authoritative, each test is +costly, the design space is mixed, and several performance goals conflict: + +- Tensegrity *lattices* and more complex architectures — exactly where hand + assembly becomes infeasible and single-build co-fabrication pays off. +- Migration from the PLA–TPU proxy toward flight-relevant materials and + impact regimes. +- Safety hardware on Earth (e.g., the tensegrity crutch tip — future work). +- Toward a self-driving lab: closing the remaining manual gaps (slicing, + handling, testing) in the loop. + +### Evidence / results + +*What we found — ground the conclusion in shown data* + +Build the talk around a quantitative three-slide sequence (per the +mock-audience report; insert observed values once the campaign completes): + +1. **Campaign ledger** — "The campaign used N specimens across B adaptive + batches under a fixed physical-test budget": initialization vs. adaptive + batches, replicates, print failures, cycle time, and the numerical value + (and rationale) of the force cap. +2. **Budget-matched baseline** — "Adaptive selection improved the feasible + design set faster than [predeclared baseline]": performance vs. number of + physical tests against Sobol/random sampling at the same budget, with + uncertainty where available. +3. **Measured feasible Pareto front** — specific energy absorption vs. + compaction efficiency among designs satisfying the force cap, with + replicate spread, marked infeasible designs, and specimen photos or + force–time traces for representative Pareto designs. + +Supporting raw-data candidates: drop-test force–time traces, Sobol / +first-batch results (PR #35 / PR #67), printing and impact-test footage. + +### Suggested 15-minute allocation + +*(From the mock-audience report — results get the largest block.)* + +| Time | Content | +|---|---| +| 1.5 min | Super Ball Bot hook + specimen + present study scope | +| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly | +| 2.5 min | One visual of the closed loop + the one-sentence BO explanation | +| 2 min | Design variables, objectives, force constraint, failures, noise controls | +| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes | +| 1 min | Limitations and transfer conditions | +| 1 min | Measured conclusion + bookend | + +Move kernel choices, encodings, and other implementation detail to backup +slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the +[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides). + +## Closing + +### Review + +*A recap of the body, leading into the conclusion* + +Using real, measured impact data from additively manufactured +tensegrity-inspired structures — parametric designs proposed by a Bayesian +optimization routine — we rapidly iterate toward better energy absorbers. +This feedback loop makes tensegrity optimization dramatically faster: dozens +of prints instead of the hundreds a traditional design of experiments over +this design space would require. + +### Conclusion + +*What the above means to the audience in the end* + +Because the loop runs on physical measurements, it is useful precisely where +model calibration is not worth its cost — and the same closed-loop pattern +should transfer (a conditional claim, pending evidence) to other additively +manufactured architectures whose performance is dominated by +hard-to-simulate effects. Give the audience the deployment rule explicitly: +*use this loop when tests are authoritative, each test is costly, the design +space is mixed, and several performance goals conflict.* For tensegrity +specifically, it opens a path from this T3 proxy study toward lattices, +flight-relevant materials, payload protection on other planets, and safety +hardware here on Earth (e.g., the crutch tip, as future work). + +### Close + +*A way to end the presentation clearly and elegantly* + +Return to the opening image: the next planetary lander's energy absorber — a +structure that has to survive not one impact but many — doesn't have to take +years of hand-built prototypes; with this loop, candidate designs can be +designed, printed, and tested against real impacts in weeks instead of months +or longer. End on the measured engineering result and its transfer condition, +then the bookend: every method that compresses design time moves our ventures +— on this planet and beyond — forward. + +--- + +## Changes from Draft 2 + +Applied from @sgbaird's inline review comments on PR #84 and the +[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md): + +**From the inline review comments:** + +1. **Reusability in the hook** (line 22): the tensegrity lander survives + multiple drops, unlike crushable/sacrificial alternatives. +2. **Proxy-system framing** (lines 49, 87): PLA–TPU would never fly; it is a + proxy for prototyping a self-driving-lab-style workflow — stated in a + scope note up front and re-stated right after the hook. +3. **"Expensive" de-emphasized** (line 52): the argument is now that even + heavy-duty simulation falls short of the experiments (millisecond-scale + peak shock), not that it merely costs too much. +4. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable; + complex structures and especially lattices are not. +5. **Task scoped** (line 70): T3 structure + FDM processing parameters + (temperature, print speed, …) only; the rest is explicitly future work. +6. **Simulation-gap justification flagged** (line 87): TODO for an Edison + literature search (or validation of experiments against simulation), plus + the millisecond-resolution peak-shock argument. +7. **Optional Point 4 added** (line 97): future applications, with a note it + can fold into the Conclusion if time is short. +8. **Steel-materials point** (line 104): simulations fall short for the + actual engineering materials (steel struts/cables), not just printed + PLA–TPU. +9. **Honegumi featured** (line 130): how it scaffolded the advanced BO script + in minutes, pitched so the audience sees they can use it too. +10. **"Thousands" → "hundreds"** (line 173): baseline is now a traditional + design of experiments over a high-dimensional space (hundreds), in the + main message, Need, Point 1, and Review. +11. **"Weeks not years" softened** (line 191): now "weeks instead of months + or longer" for candidate designs. + +**From the mock-audience report's priority revisions:** + +12. **Quantitative three-slide results sequence** (Priority 1): campaign + ledger → budget-matched baseline → measured feasible Pareto front, with a + fallback main-message wording if the campaign can't yet support the + efficiency claim. +13. **Anti-simulation premise replaced with a scoped cost decision** + (Priority 2): Point 1 retitled to the information-value message; + "simulation can't be trusted" removed everywhere; FEA-veteran pushback + anticipated with a backup slide. +14. **15-minute allocation table added** (Priority 3): 5 minutes on results; + implementation detail moved to backup slides; Q&A prep pointed at the + report's claims-vs-evidence table. +15. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in + the main talk, machinery in backup slides; the misleading + "qNEHVI over standard expected improvement" comparison replaced with + "noise-aware vs. non-noise-aware" plus proper budget-matched baselines. +16. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation + sequence in Point 3, so the structure isn't remembered as a generic crush + structure or lattice. +17. **"Eliminates assembly entirely" hedged** (theme + P3): now "single-build + co-fabrication / no joining of parts," with a note to list remaining + manual steps and an automation-status caveat. +18. **Objective formulation made consistent** (theme 7): objectives = SEA + + compaction efficiency, constraint = force cap, deliverable = *feasible* + Pareto front; the "SEA vs. peak force" evidence bullet corrected. +19. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the + transfer claim marked conditional, and the close ends on the measured + result before the bookend. + +## Changes from Draft 1 + +Feedback from the PR #84 review, applied in Draft 2: + +1. **Main message** re-centered on speed + real data ("robust" → the + dozens-not-thousands claim); body and message now carry the same story. +2. **"Combining real and simulated data" removed** (Review section) — the + manuscript's positioning (#75/#76) is that the physical experiment is the + source of truth, explicitly avoiding calibrated FE simulation. +3. **"Generative design" → "parametric designs"** to match the manuscript's + parametric design space and avoid an over-claim to a design-automation + audience. +4. **Objectives made explicit** (Task, Point 2, Review): maximize specific + energy absorption and compaction efficiency, cap peak transmitted force. +5. **Need sharpened into the two-sided pincer**: simulation can't be trusted + *and* the space is too big to brute-force — which makes BO + AM the + inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see + above.)* +6. **Point 1 given its own content** (the *why* behind the inefficiency) + instead of restating the Need. +7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on + the specifics (noise-aware acquisition, small batches, multi-objective). +8. **Point 3 explicitly closes the "easy assembly" loop** from the Need + (monolithic printing = no assembly). *(Draft 3 hedges the wording; see + above.)* +9. **Evidence/results slot added** before the Closing, so the Conclusion is + grounded in shown data rather than three hedged "may"s. +10. **Attention getter compressed** to 1–2 sentences of setup plus the Super + Ball Bot visual; verified video links included. +11. **Close bookends the opening** (the next lander's absorber, designed in + weeks) before the broader humankind line. +12. **Spelling**: Doumont (was "Duomont"). diff --git a/presentation/doumont-video-notes.md b/presentation/doumont-video-notes.md new file mode 100644 index 00000000..2b027cd3 --- /dev/null +++ b/presentation/doumont-video-notes.md @@ -0,0 +1,80 @@ +# Notes on Jean-luc Doumont — "Creating Effective Slides" + +Video: (assigned in #83) + +Transcribed from @me-madsen's handwritten notes +([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf), +dated 2026-07-15). + +## Overview + +A presentation is all about the right content, displayed the right way, at the +right time. + +- Verbal communication is sequential. +- Message is *interpretation* of the information. + - Ensure the audience can get the most out of what you're doing. + - Don't just give them information — help them understand it, interpret it, + and internalize it. + +## Doumont's suggested rules for a presentation + +0. Ensure you have **messages** for your audience, not just words. +1. **Adapt to your audience.** + - Think like the audience, not the speaker. +2. **Maximize signal-to-noise ratio.** + - Reduce noise (animations, laser pointers, anything that pulls attention + away from your message). +3. **Use effective redundancy.** + - Tell your message in word *and* on slide: someone deaf should understand + your messages just as well as someone blind. + - Ineffective redundancy is clutter appearing on every slide (e.g., the + date). + +## When creating slides + +- If the audience doesn't need it, why put it on the slides? +- People shouldn't have to count the items on a slide to know how many are + there. +- Don't use your slides as your "presentation notes." + - Don't show your notes to the audience. + - **A bad slide is worse than no slide at all.** +- **One message per slide** is a good rule of thumb. + - Prioritize the *so what* (or *therefore what*) of your message. +- Make it easy to interpret the information on the slide: + - 3–5 items at most. + - Align the title left. + - Do not clutter slides with repetitive or unnecessary information: logos, + affiliation, date, slide numbers. +- Doumont's suggested workflow: + 1. **Start with the title.** + - Use a full sentence in the title. + - The title is the main message of the slide. + - Make it easy to read: left-aligned, with intentional line breaks. + 2. **Do the visual next.** Optimize for viewing / taking in the message. +- Try printing the slideshow as a handout, 6 per page, to test visibility. + +## General preparation + +- Planning for audience, content, and structure matters more than materials + (i.e., slides). +- Try using one sheet of paper for presentation preparation. +- It's most important that your presentation is: + - well planned, structured, and delivered; + - **if you don't have a lot of time to prepare, don't focus on making + slides.** +- Practice the presentation a few times. Practice at least once *without* your + slideshow! +- If doing a handout, keep it separate from the slides you're preparing. +- Impress the audience with your body. + - Use gestures, not laser pointers. + - "Control" the audience with your body — keep their attention on your + presentation. + +## Impressions / what to apply to the IDETC presentation + + + +## Questions / things to discuss + + diff --git a/presentation/edison-mock-audience/artifacts/4949af58-memory.jsonl b/presentation/edison-mock-audience/artifacts/4949af58-memory.jsonl new file mode 100644 index 00000000..115fc604 --- /dev/null +++ b/presentation/edison-mock-audience/artifacts/4949af58-memory.jsonl @@ -0,0 +1,7 @@ +{"ts":"2026-07-18T17:54:32.690600+00:00","step":1,"compaction_occurred":false,"message":{"role":"system","content":"You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting — or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers — no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea — what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python↔R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn — as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state — keep them separate:\n\n- `MEMORY.md` — procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` — stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here — use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]"}} +{"ts":"2026-07-18T17:54:32.690600+00:00","step":1,"compaction_occurred":false,"message":{"role":"user","content":"\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI — this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format — ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter — ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling —\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward — execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation — reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` — no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` — it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object — use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits — e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others — and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats — reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring — that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules — SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library."}} +{"ts":"2026-07-18T17:54:32.690600+00:00","step":1,"compaction_occurred":false,"message":{"role":"user","content":"\npwd: /workspace"}} +{"ts":"2026-07-18T17:54:32.690600+00:00","step":1,"compaction_occurred":false,"message":{"role":"user","content":"\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md — the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex — the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md — the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" — design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" — senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" — mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" — new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" — computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" — design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words — noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data — in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation — Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). © 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** — transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings —\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle — thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) — NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n — Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n — NASA Video; mission-concept animation (Titan descent) — strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable–strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing — find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design–print–test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data — maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data — in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable — so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA–TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative — sweeping the design space physically — fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen — we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works — spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound — the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck — the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast — and\neliminates assembly entirely.\n\n- Monolithic PLA–TPU co-printing: rigid struts and flexible tension network\n in a single build — no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found — ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures — parametric designs proposed by a Bayesian\noptimization routine — we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails — and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes — with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly — and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" → the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) — the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" → \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force — which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1–2 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont — \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information — help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3–5 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body — keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n"}} +{"ts":"2026-07-18T17:54:32.690600+00:00","step":1,"compaction_occurred":false,"message":{"role":"assistant","content":"I’ll evaluate the outline as a delivered 15-minute talk, treating the unfinished results section as a substantive gap rather than inventing outcomes. I’ll score each persona against comprehension, credibility, relevance, and recall, then synthesize revisions for DAC-10."}} +{"ts":"2026-07-18T17:57:21.701836+00:00","step":2,"compaction_occurred":false,"message":{"role":"user","content":"\nMessage was NOT sent to user. Please call the send_message tool to communicate with the user. Use wait=True if you need a response before proceeding, or wait=False if you are providing a status update to the user."}} +{"ts":"2026-07-18T17:57:21.701836+00:00","step":2,"compaction_occurred":false,"message":{"role":"assistant","content":null,"function_call":null,"tool_calls":[{"id":"call_zSHcqZDq4Rrb984ui6lpQugL","type":"function","function":{"arguments":"{\"answer\": \"# Mock audience report: IDETC-CIE 2026 DAC-10 talk\\n\\n## Scope of this review\\n\\nThis evaluates the talk implied by the outline, not finished slides or delivery technique. The experimental campaign is still in progress, so I do **not** infer any favorable results. The empty \\u201cEvidence / results\\u201d section is treated as a major unresolved part of the talk: the outline currently explains why the workflow should be useful, but does not yet demonstrate that it is useful.\\n\\nThe central communication test is whether each listener leaves with the intended message:\\n\\n> By closing the loop between multi-material 3D printing and Bayesian optimization, we can optimize tensegrity energy absorbers directly from real impact data, in dozens of prints rather than thousands.\\n\\n---\\n\\n# P1. The skeptical Bayesian-optimization insider\\n\\n## (a) First-person reaction\\n\\nThe application is potentially interesting, and the design\\u2013print\\u2013test loop is easy to understand. The mixed continuous, integer, and categorical design space, physical failures, parallel fabrication, and multiple constrained objectives could make this a worthwhile design-automation case study.\\n\\nBut the outline keeps telling me that Bayesian optimization is sample-efficient without showing that **this implementation** is sample-efficient. \\u201cDozens, not thousands\\u201d sounds like a slogan unless I see the actual experiment count, initialization size, batch size, replication policy, convergence behavior, and a comparison against credible alternatives. q-noisy expected hypervolume improvement, or qNEHVI, is established machinery. Merely deploying it does not establish methodological novelty.\\n\\nI become skeptical at the statement that qNEHVI was selected because of heteroscedastic TPU and print noise. qNEHVI accounts for noisy observations, but the outline does not explain how the surrogate models heteroscedasticity, whether repeated specimens identify noise, or whether noise is being conflated with unmodeled process drift. Independent Gaussian processes with an unspecified observation model do not automatically resolve batch-dependent heteroscedasticity.\\n\\nMy attention drifts during the broad argument against simulation because it delays the information I came to DAC-10 to hear: the design variables, model formulation, constraint handling, acquisition implementation, budget, and benchmark. The Super Ball Bot hook is visually effective, but planetary landing may feel like application theater if the actual specimens and impact regime are far removed from that use case.\\n\\nThe strongest possible section is the results slot. In its current empty state, the talk has a method narrative but no validation.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey used standard batch noisy multi-objective Bayesian optimization to choose which multi-material printed impact specimens to test, instead of running a full physical sweep.\\u201d\\n\\n**Distortion from the intended message:** I would omit the claim that the structures were optimized in dozens of prints because I have not seen evidence for it. I would also describe qNEHVI as standard rather than as the contribution.\\n\\n## (c) Top three Q&A questions\\n\\n1. **What is the actual experimental budget?** How many initial designs, adaptive iterations, specimens per batch, and physical replicates were used, and what stopping rule defined success?\\n2. **What baselines did you beat?** Did you compare qNEHVI with Sobol or Latin-hypercube sampling, random search, scalarized Bayesian optimization, or a domain-informed sequential design under the same number of physical tests?\\n3. **How exactly is noise modeled?** Are replicate-dependent variances supplied to the Gaussian processes, is a heteroscedastic likelihood fitted, and how are TPU lot, print batch, machine drift, and specimen-level defects distinguished?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhat did you contribute beyond applying off-the-shelf qNEHVI to a new specimen family?\\u201d\\n\\n**Damage if unanswered: severe.** For a DAC review audience, this can reduce the work from a design-automation contribution to an application demonstration. A strong answer could rest on the experimental formulation, mixed-variable and failure-aware implementation, quantitative sample-efficiency evidence, and transferable operating lessons. Those contributions must be stated explicitly and tested.\\n\\n---\\n\\n# P2. The aerospace entry, descent, and landing practitioner\\n\\n## (a) First-person reaction\\n\\nThe opening lands immediately. I recognize the Super Ball Bot concept, and \\u201cthe lander is the energy absorber\\u201d is a useful framing. The three performance quantities also sound relevant: energy absorbed per mass, usable stroke, and force transmitted to the payload.\\n\\nThen the talk moves into language I do not naturally use: Gaussian-process surrogate, qNEHVI, acquisition function, hypervolume, exploration versus exploitation, and Pareto front. I can accept that the algorithm chooses the next tests, but I need a plain explanation of what information it uses and why I should trust the recommendations. If the presenter spends several minutes naming algorithms without connecting them to engineering decisions, I will lose the thread.\\n\\nMy main concern is application fidelity. A desktop drop-weight test at one fixed impact energy is not automatically evidence for entry, descent, and landing hardware. I need specimen dimensions, impact mass, velocity, energy, strain rate, boundary conditions, number of impacts, rebound behavior, temperature, vacuum sensitivity, and failure containment. PLA and TPU raise immediate environmental and qualification questions. I also want to know whether the absorber is reusable or sacrificial.\\n\\nMy attention returns when I see force\\u2013time traces, high-speed footage, and damaged specimens. It drifts if the talk stays on the optimizer rather than connecting each output to payload protection.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey have an algorithm that learns from successive drop tests and tells them which 3D-printed crush structure to make next, so they can find a lightweight low-force design with fewer prototypes.\\u201d\\n\\n**Distortion from the intended message:** I would probably call the structures \\u201ccrush structures,\\u201d losing the tensegrity distinction. I might also overgeneralize the method to planetary landers even though the current tests appear to be laboratory-scale.\\n\\n## (c) Top three Q&A questions\\n\\n1. What impact mass, velocity, energy, and strain-rate regime do the tests cover, and how do those values compare with a credible lander load case?\\n2. How do the selected designs scale with size and impact energy? Are the controlling mechanisms material-rate effects, geometric buckling, contact, or all three?\\n3. What happens under off-axis impact, repeated impacts, temperature extremes, aging, vacuum, and manufacturing defects?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhy should I believe that a PLA\\u2013TPU specimen optimized at one laboratory impact condition tells me anything about flight-scale landing protection?\\u201d\\n\\n**Damage if unanswered: severe for the planetary-landing framing, moderate for the underlying optimization study.** The presenter should clearly label planetary landing as motivation, not demonstrated readiness, and state the present technology scope. Otherwise the opening and closing overpromise relative to the reported experiment.\\n\\n---\\n\\n# P3. The additive-manufacturing and materials researcher\\n\\n## (a) First-person reaction\\n\\nThe monolithic PLA\\u2013TPU architecture is the part I care about. The core-wrapping strategy sounds more credible than simply depositing TPU against exposed PLA and assuming durable adhesion. Holding print settings fixed within a batch, weighing specimens, checking dimensions, and periodically testing a control are all sensible.\\n\\nI still need much more fabrication detail before I accept that design geometry is the main source of performance variation. PLA\\u2013TPU interfaces are sensitive to temperature, surface contact, moisture, residence time, toolpath order, contamination, and local geometry. \\u201cPrint defects\\u201d is too broad. I want failure-mode evidence showing whether energy is absorbed through intended tension-network deformation, TPU hysteresis, strut buckling, interface peeling, or accidental damage.\\n\\nThe phrase \\u201cno assembly\\u201d catches my attention, but \\u201celiminates assembly entirely\\u201d may be too strong. Does the build require support removal, trimming, conditioning, fixture insertion, post-tensioning, or manual alignment? If so, the accurate claim is \\u201csingle-build co-fabrication\\u201d or \\u201cno joining of PLA and TPU members,\\u201d not zero assembly.\\n\\nI am a Bayesian-optimization novice. I can follow the loop if the presenter says, \\u201cThe model proposes the next small batch where improvement is plausible or uncertainty is high.\\u201d I will disengage if the slide foregrounds qNEHVI notation rather than the physical decisions it enables.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey co-print rigid PLA and flexible TPU energy absorbers, test them, and use the results to choose the next geometries instead of printing a large design-of-experiments grid.\\u201d\\n\\n**Distortion from the intended message:** I would remember the fabrication loop more strongly than tensegrity or Bayesian optimization. I might interpret the improvement as mostly a material-interface result unless the structural mechanisms are shown.\\n\\n## (c) Top three Q&A questions\\n\\n1. How was PLA\\u2013TPU bonding quantified, and where did failed specimens actually fail: within TPU, within PLA, or at the interface?\\n2. How many nominally identical replicates were printed and tested, and how large were within-batch, between-batch, and printer-drift effects relative to design effects?\\n3. What manual operations occur after printing, and what exactly does \\u201cno assembly\\u201d exclude?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cHow do you know the optimizer is learning architecture rather than uncontrolled interface quality and process drift?\\u201d\\n\\n**Damage if unanswered: severe.** If fabrication variation is not measured and separated from design effects, both the surrogate and the claimed Pareto front may be unstable. Replicates, blocking or covariates, control specimens, dimensional measurements, and failure-mode documentation are needed.\\n\\n---\\n\\n# P4. The first-year graduate student\\n\\n## (a) First-person reaction\\n\\nThe opening video and the phrase \\u201cthe lander is the energy absorber\\u201d give me a concrete picture. I understand the basic problem: too many designs to print, and computer models may miss messy material behavior. I also understand the loop when it is described as \\u201cdesign, print, drop, learn, repeat.\\u201d That story carries me.\\n\\nI start getting lost when several unfamiliar terms arrive together: tensegrity-inspired, compaction efficiency, Gaussian process, surrogate, acquisition function, qNEHVI, heteroscedastic noise, Pareto front, probabilistic feasibility constraint, and exploration versus exploitation. The preview says the audience already believes optimization works, but I still need one intuitive sentence explaining what Bayesian optimization does. Skipping \\u201cBayesian optimization 101\\u201d should not mean skipping the conceptual bridge.\\n\\nI am also unsure how the structure works. Are the TPU parts stretched before impact? Are the PLA struts touching? What makes this \\u201ctensegrity-inspired\\u201d rather than a multi-material lattice? A labeled specimen image and a short deformation sequence would help much more than a verbal definition.\\n\\nMy attention drifts during the repeated case against simulation because I accepted that point the first time. It returns for printing footage, the drop test, and a clear plot with a marked \\u201cbetter\\u201d direction.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey let an artificial-intelligence program learn from 3D-printed drop tests and keep suggesting better shock absorbers, so they do not have to test every design.\\u201d\\n\\n**Distortion from the intended message:** I would probably call Bayesian optimization \\u201cAI,\\u201d omit the two-objective trade-off and force constraint, and assume each new design is simply better than the last. I might also miss what tensegrity contributes.\\n\\n## (c) Top three Q&A questions\\n\\n1. What makes one of these specimens a tensegrity structure rather than an ordinary 3D-printed lattice?\\n2. How does the computer decide which design to print next, in plain language?\\n3. Can one design maximize both energy absorption and compaction efficiency while also lowering peak force, or must those goals trade off?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cI followed the loop, but I still do not understand what is physically special about the structure or what the optimizer actually learned.\\u201d\\n\\n**Damage if unanswered: moderate.** Experts can fill in some gaps, but a talk that leaves novices with only \\u201cAI plus 3D printing\\u201d has failed to communicate its engineering mechanism. One specimen-mechanics visual and one plain-language optimization visual would largely solve this.\\n\\n---\\n\\n# P5. The finite-element-analysis veteran\\n\\n## (a) First-person reaction\\n\\nThe statement \\u201csimulation can\\u2019t be trusted for these structures\\u201d puts me on the defensive before I have seen any evidence. Interfacial slip, viscoelasticity, large deformation, buckling, contact, strain-rate dependence, and manufacturing variability are difficult, but they are not intrinsically beyond finite-element analysis. Models can be calibrated, uncertainty can be propagated, and simulation can still screen designs even if it is imperfect.\\n\\nThe abstract is more defensible than the talk outline. It says the workflow avoids **direct dependence on calibrated finite-element simulation for objective evaluation** and acknowledges future analytical or multifidelity shortcuts. The presentation should use that language. The current outline turns a project-scope choice into a universal technical claim.\\n\\nI accept the economic argument that calibrating a high-fidelity model may not be worthwhile for an early design campaign. That is a stronger position: measured tests provide authoritative objective values, while the study asks whether sequential design can use a small physical budget efficiently. I would also welcome evidence that simulation errors change design rankings or miss observed failure modes. Without such evidence, the repeated attack on simulation sounds like a straw man.\\n\\nMy attention drifts when the talk says simulation is unreliable several times without showing a simulation\\u2013experiment discrepancy. It returns when the presenter discusses physical failure modes and admits where lower-fidelity physics could later improve the loop.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey skipped finite-element modeling and used Bayesian optimization directly on physical tests because they judged model calibration too expensive for these multi-material specimens.\\u201d\\n\\n**Distortion from the intended message:** I would frame the work as bypassing simulation rather than as enabling rapid experimental optimization. If the presenter is combative about simulation, that dispute may become the only thing I remember.\\n\\n## (c) Top three Q&A questions\\n\\n1. What evidence shows that a calibrated finite-element model is insufficient for design ranking, rather than merely costly to construct?\\n2. Did you compare the physical-test-only workflow with a simple mechanics model or multifidelity surrogate that could reduce the experimental budget further?\\n3. Which observed behaviors dominate the model discrepancy: interface failure, TPU viscoelasticity, contact, buckling imperfections, or geometry errors from printing?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cYou have shown that simulation is inconvenient, not that it cannot be trusted. Why is abandoning it scientifically preferable to calibrating and validating it?\\u201d\\n\\n**Damage if unanswered: severe and avoidable.** The current absolute wording invites a technical fight that is unnecessary to the contribution. Reframing the claim as a cost-and-dependence decision would preserve the motivation without asserting that finite-element modeling is incapable.\\n\\n---\\n\\n# P6. The friendly industry generalist\\n\\n## (a) First-person reaction\\n\\nThe loop is memorable: print a candidate, test it, update the model, and print the next informative candidate. I can imagine using that pattern for brackets, seals, lattice pads, or process settings when simulation is weak and tests are expensive. \\u201cDozens rather than thousands\\u201d is exactly the kind of value proposition I remember.\\n\\nI do not need the mathematical details of qNEHVI, but I need an operational recipe. What software is used? How many initial samples are required? How automated is the handoff from optimizer to computer-aided design and slicing? How long does one cycle take? What expertise does a team need? The abstract says candidate selection is automated but slicing, specimen handling, and testing remain manual. That qualification should appear in the talk before I infer a robotic self-driving laboratory.\\n\\nThe tensegrity and planetary material is interesting, but my attention will drift if it occupies too much of a 15-minute slot. The method becomes useful to me only when the presenter explicitly generalizes it and defines when it is worth using.\\n\\nThe ending about moving humankind forward is less memorable than a concrete deployment rule. I would prefer a final slide saying: \\u201cUse this loop when tests are authoritative, each test is costly, the design space is mixed, and several performance goals conflict.\\u201d\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey showed a practical test-driven optimization loop that can find good 3D-printed designs with a few dozen prototypes instead of a giant test matrix.\\u201d\\n\\n**Distortion from the intended message:** I may overstate automation and assume the \\u201cfew dozen\\u201d claim is established across applications. I will probably forget the names qNEHVI and tensegrity, but retain the workflow.\\n\\n## (c) Top three Q&A questions\\n\\n1. What parts of the workflow are automated today, and what does an engineer still do manually between recommendation and test result?\\n2. What minimum experiment budget and software stack would my team need to try this on a different component?\\n3. When is this approach better than a conventional design of experiments, response-surface method, or a calibrated simulation?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhat concrete evidence tells me this saved enough prototypes and engineering time to justify the added optimization infrastructure?\\u201d\\n\\n**Damage if unanswered: moderate to severe.** I will still like the idea, but I will not take it back to my team. A budget-matched baseline and a simple timeline or cost comparison would convert interest into action.\\n\\n---\\n\\n# Synthesis\\n\\n## Cross-persona themes\\n\\n### 1. The causal chain is understandable, but the claimed payoff is not yet demonstrated\\n\\nAll six personas can understand the basic design\\u2013print\\u2013test\\u2013learn loop. The empty results section prevents them from deciding whether the loop actually found better designs, found them faster, or handled noise reliably. The phrase \\u201cdozens, not thousands\\u201d is currently an unsupported quantitative claim.\\n\\nAt minimum, the results need to show:\\n\\n- total number of unique designs and physical specimens;\\n- initial versus adaptively selected designs;\\n- batch size and number of iterations;\\n- replicate policy and failure count;\\n- measured uncertainty or repeatability;\\n- progress under the actual experimental budget;\\n- the final feasible Pareto set for **specific energy absorption and compaction efficiency subject to the force cap**;\\n- a budget-matched nonadaptive or simpler optimization baseline.\\n\\n### 2. \\u201cSimulation can\\u2019t be trusted\\u201d is too absolute\\n\\nP1 questions the evidence, P2 cares about application validity, and P5 directly rejects the premise. Even P6 needs to know when the loop is preferable to simulation. The abstract already contains better wording: physical measurements are used for objective evaluation \\u201cwithout relying on calibrated finite-element simulation.\\u201d\\n\\nThe defensible claim is not that simulation fails universally. It is that a sufficiently calibrated multiphysics model may be expensive relative to the available design campaign, and the authors therefore test whether physical-data-driven sequential optimization is useful without depending on such a model.\\n\\n### 3. The talk needs a two-level explanation of Bayesian optimization\\n\\nP1 does not want introductory Bayesian-optimization material; P2, P3, P4, and P5 need a plain conceptual bridge. These needs are compatible. Give the intuition in one sentence and one visual, then put the implementation specifics in a compact technical panel or backup slide.\\n\\nSuggested spoken explanation:\\n\\n> \\u201cAfter each batch, the model estimates both expected performance and uncertainty across the design space. The acquisition rule selects a small next batch that is most likely to expand the feasible trade-off frontier, while accounting for noisy tests and print failures.\\u201d\\n\\nThis is not \\u201cBayesian optimization 101.\\u201d It is the minimum explanation needed to interpret the workflow.\\n\\n### 4. The physical mechanism and the word \\u201ctensegrity\\u201d need clarification\\n\\nP2 may reduce the objects to crush structures, P3 may interpret the result as an interface study, and P4 may not distinguish the specimens from ordinary lattices. A labeled as-printed specimen plus a four-frame deformation sequence should show:\\n\\n- rigid PLA struts;\\n- continuous flexible TPU network;\\n- whether and how pre-tension exists;\\n- load path before and during impact;\\n- intended energy-dissipation mechanisms;\\n- why \\u201ctensegrity-inspired\\u201d is the precise term.\\n\\n### 5. Manufacturing variability is part of the model, not background noise\\n\\nP1 and P3 will both press this point. Periodically retesting one control is useful but may not identify design-specific heteroscedasticity, lot effects, or confounding between iteration and material batch. The talk needs to state the replication, randomization, blocking, covariate, and drift-monitoring plan. If those steps were not used, the limitation must be explicit.\\n\\n### 6. The application framing outruns the demonstrated scope\\n\\nThe planetary hook is strong, but P2 will not equate fixed-energy laboratory tests on PLA\\u2013TPU specimens with flight hardware. The closing escalates further by claiming that the next lander absorber can be \\u201cproven\\u201d in weeks. That wording should be narrowed unless the campaign includes relevant scaling and environmental evidence.\\n\\n### 7. The objective statement is not fully consistent across the outline\\n\\nThe task and Point 2 correctly describe two objectives, specific energy absorption and compaction efficiency, with a peak-force constraint. The candidate evidence list instead proposes \\u201cSEA vs. peak transmitted force,\\u201d and the abstract\\u2019s expected-outcomes section similarly describes trade-offs between SEA and peak force. Those are different optimization formulations.\\n\\nThe talk must consistently distinguish:\\n\\n- **objectives:** maximize specific energy absorption and compaction efficiency;\\n- **constraint:** peak transmitted force must not exceed a specified cap;\\n- **feasible Pareto front:** trade-offs between the two objectives among designs satisfying that cap.\\n\\nIf peak force is instead treated as a third objective, the task statement and optimization formulation must be revised accordingly.\\n\\n### 8. The closing favors inspiration over technical precision\\n\\nP6 is likely to remember \\u201cdozens, not thousands,\\u201d but P1, P2, and P5 may hear overreach in \\u201cproven,\\u201d \\u201cin weeks,\\u201d and \\u201cmoves humankind forward.\\u201d A DAC-10 close should end on the measured engineering result and transfer condition. The Super Ball Bot bookend can remain, but it should not imply flight qualification.\\n\\n---\\n\\n# Three highest-priority revisions\\n\\n## Priority 1: Build the talk around a quantitative result, not around the availability of a workflow\\n\\nReplace the placeholder with a required three-slide evidence sequence:\\n\\n1. **\\u201cThe campaign used N specimens across B adaptive batches under a fixed physical-test budget.\\u201d** \\n Show initialization, adaptive batches, replicates, failures, elapsed cycle time, and the force threshold. Do not use `N` or `B` in the actual talk; insert the observed values.\\n\\n2. **\\u201cAdaptive selection improved the feasible design set faster than [predeclared baseline].\\u201d** \\n Plot a budget-matched performance measure across physical tests, with uncertainty across repeated runs if available. If only one physical campaign exists, avoid inferential claims that require repeated campaigns and supplement with clearly labeled retrospective resampling or simulation-based algorithm checks.\\n\\n3. **\\u201cThese measured designs form the final feasible trade-off between specific energy absorption and compaction efficiency.\\u201d** \\n Show uncertainty or replicate spread, mark infeasible force-cap violations, and include specimen images or force\\u2013time traces for representative Pareto designs.\\n\\nIf the campaign cannot support those statements, revise the main message from \\u201cwe can optimize \\u2026 in dozens\\u201d to \\u201cwe are evaluating whether noisy multi-objective Bayesian optimization can reduce the physical test budget.\\u201d\\n\\n## Priority 2: Replace the anti-simulation premise with a scoped engineering decision\\n\\nReplace:\\n\\n> \\u201cSimulation can\\u2019t be trusted for these structures.\\u201d\\n\\nwith:\\n\\n> \\u201cFor these multi-material prints, obtaining objective values from a validated high-fidelity model would require substantial calibration of interface, rate, contact, and defect behavior. We therefore ask how far a limited budget of direct physical tests can take us without depending on that model.\\u201d\\n\\nThen title Point 1 with a message rather than a topic:\\n\\n> **\\u201cWhen model calibration and exhaustive testing are both costly, each physical specimen must be chosen for information value.\\u201d**\\n\\nThis keeps the pincer structure while avoiding an unnecessary universal claim.\\n\\n## Priority 3: Reallocate the 15 minutes around audience decisions\\n\\nA concrete timing plan:\\n\\n- **1.5 min:** Super Ball Bot hook, specimen, and present study scope.\\n- **2 min:** Why exhaustive physical search and high-fidelity calibration are costly.\\n- **2.5 min:** One visual of the closed loop, including a one-sentence Bayesian-optimization explanation.\\n- **2 min:** Actual design variables, objectives, force constraint, fabrication failures, and noise controls.\\n- **5 min:** measured results, baseline, uncertainty, representative specimens, and failure modes.\\n- **1 min:** limitations and transfer conditions.\\n- **1 min:** measured conclusion and bookend.\\n\\nMove kernel choices, log-space acquisitions, detailed encoding, and other implementation material to backup slides unless a result depends directly on them. This follows Doumont\\u2019s principle that each slide should communicate one interpreted message rather than expose the speaker\\u2019s notes.\\n\\n---\\n\\n# Claims needing evidence, hedging, or backup slides\\n\\n| Claim or topic | What is needed in the main talk | Prepared backup material |\\n|---|---|---|\\n| \\u201cDozens of prints, not thousands\\u201d | Actual specimen count, definition of success, and budget-matched comparator | Full campaign ledger; sensitivity to initialization and stopping rule |\\n| \\u201cSimulation can\\u2019t be trusted\\u201d | Replace with scoped calibration-cost language unless direct validation evidence exists | Simulation\\u2013experiment traces, ranking errors, or documented failure modes a tested model missed |\\n| \\u201cqNEHVI handles heteroscedastic noise\\u201d | Specify the actual observation-noise model and replication strategy | Likelihood formulation, fixed versus inferred noise, residual diagnostics, replicate variance by design or batch |\\n| qNEHVI \\u201cover standard expected improvement\\u201d | Use a technically appropriate comparison; ordinary expected improvement is not a like-for-like multi-objective batch baseline | Comparisons with random/Sobol, scalarization, qEHVI or another relevant noisy/constrained baseline |\\n| \\u201cThe Pareto front is the design deliverable\\u201d | Show the two stated objectives and force-cap feasibility consistently | Hypervolume reference point, normalization, uncertainty, constraint treatment |\\n| Peak-force cap | State its numerical value and engineering rationale | Sensitivity of selected designs to alternative cap values |\\n| Probabilistic print-feasibility constraint | Report failures and demonstrate that the classifier or constraint model is identifiable at the available sample size | Failure labels, calibration, confusion or reliability metrics, acquisition formula |\\n| \\u201cNo assembly\\u201d / \\u201celiminates assembly entirely\\u201d | List any post-print operations; use \\u201csingle-build co-fabrication\\u201d if manual steps remain | Fabrication workflow photographs and labor-time accounting |\\n| \\u201cCandidate goes to tested specimen in hours\\u201d | Median or representative print-to-result time and what remains manual | Time breakdown for design, slicing, printing, conditioning, inspection, and testing |\\n| \\u201cDramatically faster\\u201d | Define faster relative to a measured or estimated comparator | Cost and wall-clock accounting with assumptions |\\n| Transfer to other architectures | Present as a conditional hypothesis, not a demonstrated result | Applicability checklist: test cost, dimensionality, noise, throughput, and constraint structure |\\n| Planetary-landing relevance | Clearly state that this is motivation unless scale and environment are tested | Impact similitude, energy and velocity regime, environmental gaps, technology-readiness discussion |\\n| PLA\\u2013TPU interface robustness | Failure-mode and repeatability evidence | Microscopy or fracture images, process parameters, conditioning and moisture controls |\\n| \\u201cTensegrity-inspired\\u201d | Show the load path and identify which tensegrity features are retained | Geometric definition, pre-tension status, comparison with a conventional lattice/control |\\n| Independent Gaussian processes | State why cross-objective correlation is ignored and whether that choice matters | Residual correlations and sensitivity to multi-output alternatives |\\n| Mixed categorical variables | Briefly state how candidates are represented and optimized | Kernel or encoding details; treatment of invalid combinations |\\n\\n---\\n\\n# Fit to the six-person audience\\n\\n## Best served: P6, the friendly industry generalist\\n\\nThe outline has a clear problem\\u2013method\\u2013application arc, a memorable loop, and an attractive efficiency promise. P6 can understand the proposed value without needing the mathematics. The danger is that this persona may accept unsupported claims and overestimate the system\\u2019s automation and maturity.\\n\\nP4 also benefits from the story structure, but the unexplained terminology and absent physical-mechanism visual will cause substantial loss of detail.\\n\\n## Worst served: P1, the skeptical Bayesian-optimization insider\\n\\nP1 is the most likely to judge the current talk as an off-the-shelf acquisition function applied to a new artifact. The outline anticipates this concern by saying not to teach Bayesian optimization, but it does not yet supply the evidence P1 needs: novelty positioning, rigorous baseline, budget, noise identification, constraint diagnostics, and measured sample efficiency.\\n\\nP5 is nearly as poorly served because the opening motivation directly challenges finite-element modeling more strongly than the abstract supports.\\n\\n## Is that the right trade-off for DAC-10?\\n\\nNo. A DAC-10 presentation should remain accessible to P2\\u2013P6, but it cannot underserve P1. The design-automation expert is likely to shape the technical discussion and the perceived contribution. Accessibility should come from a plain-language loop and strong visuals, not from omitting methodological validation. The best target is a two-layer talk: every listener can follow the engineering decision, while experts can see the experimental budget, comparator, noise model, and constraint formulation.\\n\\n---\\n\\n# Predicted reception\\n\\n**Current outline: 5/10.** The loop and application are memorable, but without measured results, a budget-matched baseline, and narrower simulation and planetary claims, the talk reads as a polished motivation for an experiment rather than a completed DAC-10 contribution.\\n\\nIf the results establish repeatability and budgeted improvement, and the three priority revisions are made, the same structure could plausibly move into the **7\\u20138/10** range. That conditional estimate is a judgment about presentation readiness, not a prediction of unobserved experimental outcomes.\\n\\n---\\n\\n# Discretionary analytical decisions\\n\\n- Treated the unfinished evidence/results section as missing evidence rather than assuming that the planned campaign succeeds.\\n- Evaluated the artifact as a competent 15-minute delivery from the outline, not as a line edit of the outline or an assessment of slides that do not yet exist.\\n- Weighted technical credibility and evidentiary support more heavily than visual polish because the target is the DAC-10 design-optimization audience.\\n- Used the submitted abstract to resolve technical intent when the outline was ambiguous, while flagging inconsistencies rather than silently harmonizing them.\\n- Interpreted the stated optimization problem as two objectives, specific energy absorption and compaction efficiency, subject to a peak-force constraint; flagged language that instead treats peak force as an objective.\\n- Assigned qualitative damage levels of moderate, moderate-to-severe, or severe based on how directly an unanswered challenge would undermine each persona\\u2019s acceptance of the central claim.\\n- Chose P6 as the best-served persona because the present outline emphasizes an intuitive workflow and practical efficiency promise; chose P1 as the worst-served because the DAC-specific validation and novelty case is not yet developed.\\n- Scored predicted reception on the current incomplete outline, not on a hypothetical final talk with successful results.\"}","name":"submit_answer"}}]}} diff --git a/presentation/edison-mock-audience/artifacts/4949af58-notebook.ipynb b/presentation/edison-mock-audience/artifacts/4949af58-notebook.ipynb new file mode 100644 index 00000000..d1529b81 --- /dev/null +++ b/presentation/edison-mock-audience/artifacts/4949af58-notebook.ipynb @@ -0,0 +1,12 @@ +{ + "cells": [], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/presentation/edison-mock-audience/artifacts/afade0b1-memory.jsonl b/presentation/edison-mock-audience/artifacts/afade0b1-memory.jsonl new file mode 100644 index 00000000..e357a63f --- /dev/null +++ b/presentation/edison-mock-audience/artifacts/afade0b1-memory.jsonl @@ -0,0 +1,12 @@ +{"ts":"2026-07-26T02:29:14.576378+00:00","step":1,"compaction_occurred":false,"message":{"role":"system","content":"You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting — or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers — no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea — what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python↔R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn — as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state — keep them separate:\n\n- `MEMORY.md` — procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` — stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here — use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]"}} +{"ts":"2026-07-26T02:29:14.576378+00:00","step":1,"compaction_occurred":false,"message":{"role":"user","content":"\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI — this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format — ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter — ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling —\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward — execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation — reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` — no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` — it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object — use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits — e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others — and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats — reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring — that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules — SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library."}} +{"ts":"2026-07-26T02:29:14.576378+00:00","step":1,"compaction_occurred":false,"message":{"role":"user","content":"\npwd: /workspace"}} +{"ts":"2026-07-26T02:29:14.576378+00:00","step":1,"compaction_occurred":false,"message":{"role":"user","content":"\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md — a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions — treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md — the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md — the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex — the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" — a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" — design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling — and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" — senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" — multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" — new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" — 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" — design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words — noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data —\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n"}} +{"ts":"2026-07-26T02:29:14.576378+00:00","step":1,"compaction_occurred":false,"message":{"role":"assistant","content":null,"function_call":null,"tool_calls":[{"id":"call_8cnLZZRc1qR5WExVt4ZF4hEJ","type":"function","function":{"arguments":"{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}","name":"read"}},{"id":"call_93haurOMDGiH2mQFFbaMaYKn","type":"function","function":{"arguments":"{\"file_path\": \"/workspace/doumont-presentation-template.md\"}","name":"read"}},{"id":"call_tYXZgr4B8yjH0YEPyPZih9qI","type":"function","function":{"arguments":"{\"file_path\": \"/workspace/doumont-video-notes.md\"}","name":"read"}},{"id":"call_dlPcqwDfFi7a24fnE1yNh10b","type":"function","function":{"arguments":"{\"file_path\": \"/workspace/idetc-abstract.tex\"}","name":"read"}}]}} +{"ts":"2026-07-26T02:29:25.494918+00:00","step":2,"compaction_occurred":false,"message":{"role":"tool","content":" 1→# IDETC Tensegrity Slides — Draft 1 (extracted content)\n 2→\n 3→Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4→\n 5→\n 6→## Slide 1 — layout: Title Only\n 7→\n 8→**On-slide text:**\n 9→- **Title 1**: Let’s build better tensegrity structures faster\n 10→- **TextBox 4**: Marcus Madsen\n 11→Undergraduate researcher under Sterling Baird, Ph.D.\n 12→- **TextBox 6**: IDETC 2026\n 13→\n 14→**Non-text shapes / media / placeholders:**\n 15→- [PICTURE (13)] Picture 3\n 16→- [PICTURE (13)] Picture 8\n 17→\n 18→**Presenter notes:**\n 19→> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20→> \n 21→> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22→\n 23→\n 24→## Slide 2 — layout: Title Only\n 25→\n 26→**On-slide text:**\n 27→- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings—and to problems closer to home.\n 28→\n 29→**Non-text shapes / media / placeholders:**\n 30→- [MEDIA (16)] Online Media 5\n 31→\n 32→**Presenter notes:**\n 33→> Replace this video with a downloaded and trimmed version of just the fall. \n 34→> \n 35→> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle — thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander is the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit and get back up — it survives multiple drops and keeps working.\n 36→\n 37→\n 38→## Slide 3 (HIDDEN) — layout: Title Only\n 39→\n 40→**On-slide text:**\n 41→- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42→- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43→- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44→\n 45→**Presenter notes:**\n 46→> This might be better explained purely vocally while on the previous slide.\n 47→\n 48→\n 49→## Slide 4 — layout: Title Only\n 50→\n 51→**On-slide text:**\n 52→- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53→- **TextBox 3**: Iteration\n 54→- **TextBox 5**: Building\n 55→- **TextBox 7**: Testing\n 56→\n 57→**Non-text shapes / media / placeholders:**\n 58→- [MEDIA (16)] AdobeStock_207717222\n 59→- [MEDIA (16)] Online Media 2\n 60→- [PICTURE (13)] Picture 14\n 61→- [PICTURE (13)] Picture 16\n 62→- [PICTURE (13)] Picture 18\n 63→\n 64→**Presenter notes:**\n 65→> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn’t become distracting because it’s fun to watch it spin.\n 66→> \n 67→> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68→\n 69→\n 70→## Slide 5 (HIDDEN) — layout: Title Only\n 71→\n 72→**On-slide text:**\n 73→- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74→- **TextBox 14**: Baird et al. Matter (2022)\n 75→\n 76→**Non-text shapes / media / placeholders:**\n 77→- [GROUP (6)] Group 2\n 78→\n 79→**Presenter notes:**\n 80→> Consult Sterling on this slide. I don’t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it’s well enough known that it’d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81→\n 82→\n 83→## Slide 6 (HIDDEN) — layout: Title Only\n 84→\n 85→**On-slide text:**\n 86→- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87→- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88→\n 89→**Presenter notes:**\n 90→> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91→\n 92→\n 93→## Slide 7 — layout: Title Only\n 94→\n 95→**On-slide text:**\n 96→- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97→- **TextBox 5**: 6 variables\n 98→- **TextBox 7**: 2 types of input data\n 99→- **TextBox 9**: 2 objectives\n100→- **TextBox 17**: *image of blackbox*\n101→\n102→**Non-text shapes / media / placeholders:**\n103→- [LINE (9)] Connector: Curved 11\n104→- [LINE (9)] Connector: Curved 13\n105→- [LINE (9)] Connector: Curved 15\n106→\n107→**Presenter notes:**\n108→> Very rough slide as I’m not entirely sure what graphic or text to put on this, partially because I’m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109→\n110→\n111→## Slide 8 — layout: Title Only\n112→\n113→**On-slide text:**\n114→- **Title 1**: Multi-material additive manufacturing supplies that data fast — single-build co-fabrication, with no joining of parts.\n115→\n116→**Non-text shapes / media / placeholders:**\n117→- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118→- [PICTURE (13)] Picture 4\n119→\n120→**Presenter notes:**\n121→> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122→\n123→\n124→## Slide 9 — layout: Title Only\n125→\n126→**On-slide text:**\n127→- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128→- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129→- **TextBox 5**: *placeholder for slo motion of drop test*\n130→\n131→\n132→## Slide 10 — layout: Title Only\n133→\n134→**On-slide text:**\n135→- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136→- **TextBox 3**: Printing\n137→- **TextBox 5**: Testing\n138→- **TextBox 7**: BO feed\n139→\n140→**Non-text shapes / media / placeholders:**\n141→- [PICTURE (13)] Picture 14\n142→- [PICTURE (13)] Picture 16\n143→- [PICTURE (13)] Picture 18\n144→- [PICTURE (13)] Graphic 8\n145→\n146→**Presenter notes:**\n147→> I think it might be better if we replaced “printing” with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148→\n149→\n150→## Slide 11 (HIDDEN) — layout: Title Only\n151→\n152→**On-slide text:**\n153→- **Title 1**: Reserved for results\n154→\n155→**Presenter notes:**\n156→> Would be helpful if we could show that this process works in this case.\n157→\n158→\n159→## Slide 12 — layout: Title Only\n160→\n161→**On-slide text:**\n162→- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163→- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164→- **TextBox 3**: *image to show lattice structure\n165→\n166→\n167→## Slide 13 — layout: Title Only\n168→\n169→**Non-text shapes / media / placeholders:**\n170→- [PICTURE (13)] Picture 3\n171→\n172→**Presenter notes:**\n173→> Slide for questions at the end\n174→\n175→\n176→## Slide 14 — layout: Title Slide","name":"read","tool_call_id":"call_8cnLZZRc1qR5WExVt4ZF4hEJ"}} +{"ts":"2026-07-26T02:29:25.494918+00:00","step":2,"compaction_occurred":false,"message":{"role":"tool","content":" 1→# IDETC Presentation — Doumont Presentation Structure Template\n 2→\n 3→Transcribed from Jean-luc Doumont's presentation-structure template\n 4→([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5→adapted from *Trees, maps, and theorems* (Principiae, 2009). © 2009 by Principiae.\n 6→All rights reserved. Can be downloaded from .\n 7→\n 8→> To design your presentation, write down your ideas for each component below.\n 9→> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10→\n 11→**Draft 3** — applies the inline review comments from @sgbaird on PR #84 and the\n 12→priority revisions from the\n 13→[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14→Summaries of what changed are at the bottom of this file\n 15→([Draft 2 → 3](#changes-from-draft-2), [Draft 1 → 2](#changes-from-draft-1)).\n 16→\n 17→**Scope note (frame everything below with this):** the study presented is the\n 18→optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19→processing parameters** (e.g., nozzle temperature, print speed). PLA–TPU is a\n 20→**proxy system** — not flight material — used to prototype a self-driving-lab\n 21→style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22→The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23→beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24→\n 25→## Opening\n 26→\n 27→### Attention getter\n 28→\n 29→*A way to lead the audience to the need efficiently*\n 30→\n 31→Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32→landings — and to problems closer to home.\n 33→\n 34→One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35→clip) do the work:\n 36→\n 37→> Landing a payload on another planet means surviving an impact in conditions\n 38→> where parachutes and retrorockets struggle — thin atmospheres, rough\n 39→> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40→> structure: the lander *is* the energy absorber. And unlike crushable\n 41→> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42→> hit **and get back up** — it survives multiple drops and keeps working.\n 43→\n 44→Candidate Super Ball Bot videos to show (verified links):\n 45→\n 46→- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) — NASA Video\n 47→ (official channel); the canonical overview.\n 48→- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49→ — Wall Street Journal; prototype drop footage.\n 50→- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51→ — NASA Video; mission-concept animation (Titan descent) — strong hook material.\n 52→\n 53→Immediately after the hook, state the present study scope in one sentence\n 54→(per the scope note above): *we study a printed PLA–TPU T3 prism as a proxy\n 55→system for prototyping an autonomous tensegrity-optimization workflow* — so\n 56→the planetary framing never outruns the demonstrated scope.\n 57→\n 58→\n 60→\n 61→### Need\n 62→\n 63→*A difference between actual and desired situations*\n 64→\n 65→**Actual situation** (a two-sided pincer — stated as a scoped engineering\n 66→judgment, not an absolute):\n 67→\n 68→- For these structures, obtaining trustworthy objective values from a\n 69→ validated high-fidelity model would require substantial calibration of\n 70→ interface, rate, contact, and defect behavior — and even heavy-duty\n 71→ simulation falls short of the experiments we are running: peak transmitted\n 72→ shock is a millisecond-scale transient that is notoriously difficult to\n 73→ resolve numerically. This holds even for idealized cable–strut tensegrities\n 74→ in their actual engineering materials (steel or similar), not just for\n 75→ printed PLA–TPU.\n 76→ \n 79→- Exhaustive physical search fails on cost: a traditional design of\n 80→ experiments over this relatively high-dimensional space (geometry + FDM\n 81→ process parameters) would demand hundreds of specimens, each costing print\n 82→ and test time.\n 83→- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84→ more complex structures — especially tensegrity *lattices* — become\n 85→ extremely difficult and ultimately infeasible to hand-build.\n 86→\n 87→**Desired situation:**\n 88→\n 89→- Trustworthy performance data without hand assembly (easy creation of\n 90→ tensegrity structures, scalable beyond a single prism).\n 91→- Quick iteration and testing — find good designs in dozens of experiments,\n 92→ not the hundreds a traditional design of experiments would require.\n 93→\n 94→### Task\n 95→\n 96→*What I decided/was asked to do to address the need*\n 97→\n 98→Build a closed-loop design–print–test workflow that optimizes a **T3-prism\n 99→tensegrity-inspired energy absorber together with its FDM processing\n100→parameters** (e.g., temperature, print speed) directly from real, measured\n101→impact data — maximizing energy absorbed per gram (specific energy\n102→absorption) and compaction efficiency, subject to a cap on the peak force\n103→transmitted to the payload. Extensions beyond the T3 prism are future work.\n104→\n105→### Main message\n106→\n107→*The one sentence I want my audience to remember*\n108→\n109→By closing the loop between multi-material 3D printing and Bayesian\n110→optimization, we can optimize tensegrity-inspired energy absorbers directly\n111→from real impact data — in dozens of prints, not the hundreds a traditional\n112→design of experiments would demand.\n113→\n114→\n118→\n119→### Preview\n120→\n121→*A map of the body (ideally three points, max. five)*\n122→\n123→1. When model calibration and exhaustive testing are both costly, each\n124→ physical specimen must be chosen for its information value — so the\n125→ measured experiment is our source of truth.\n126→2. Bayesian optimization makes physical experimentation affordable: it handles\n127→ noisy measurements, small batches, and the constrained multi-objective\n128→ trade-off — and Honegumi let us scaffold it in minutes.\n129→3. Multi-material additive manufacturing closes the loop: single-build PLA–TPU\n130→ co-fabrication turns each proposed design into test data in hours, with no\n131→ joining of parts.\n132→4. *(Optional fourth point, if time allows)* The same closed loop points at\n133→ what comes next: tensegrity lattices, flight-relevant materials, and\n134→ safety hardware on Earth.\n135→\n136→## Body\n137→\n138→### Point 1\n139→\n140→When model calibration and exhaustive testing are both costly, each physical\n141→specimen must be chosen for its information value.\n142→\n143→- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144→ these structures, a sufficiently calibrated multiphysics model is expensive\n145→ to build relative to the design campaign itself — and the hardest quantity,\n146→ peak transmitted shock, is a millisecond-scale transient that even\n147→ heavy-duty simulation struggles to resolve. The gap persists in the actual\n148→ engineering materials a real lander would use (steel struts and cables),\n149→ so this is not merely an artifact of printed PLA–TPU.\n150→- The alternative — sweeping the design space physically — fails on cost: a\n151→ traditional design of experiments over geometry plus FDM process parameters\n152→ would take hundreds of specimens, each needing print + test time.\n153→- Therefore: treat the physical experiment as the authoritative source of\n154→ objective values, and be ruthless about *which* experiments to run.\n155→- Anticipate the FEA-veteran pushback (\"inconvenient ≠ untrustworthy\"): frame\n156→ it as a cost-and-dependence decision, and keep a backup slide with the\n157→ literature (or our own validation) on simulation–experiment discrepancy for\n158→ impact transients.\n159→\n160→### Transition\n161→\n162→If every data point must be a real experiment, we need a method that extracts\n163→the most from every specimen — we need to make the process radically more\n164→sample-efficient.\n165→\n166→### Point 2\n167→\n168→Bayesian optimization makes physical experimentation affordable, even with\n169→limited, noisy data.\n170→\n171→- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172→ visual in the main talk; implementation detail goes to backup slides.\n173→ Spoken version:\n174→ > \"After each batch, the model estimates both expected performance and\n175→ > uncertainty across the design space. The acquisition rule selects a small\n176→ > next batch that is most likely to expand the feasible trade-off frontier,\n177→ > while accounting for noisy tests and print failures.\"\n178→- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179→ motivate a noise-aware acquisition function (qNEHVI) rather than\n180→ non-noise-aware alternatives. (Backup slide: how observation noise is\n181→ actually modeled — replicates, noise identification — and budget-matched\n182→ baselines such as Sobol/random sampling.)\n183→- Small parallel batches: the surrogate proposes the next batch of specimens\n184→ to print, balancing exploration and exploitation.\n185→- Constrained multi-objective by construction: **objectives** = maximize\n186→ specific energy absorption and compaction efficiency; **constraint** = peak\n187→ transmitted force must stay under a cap; the deliverable is the *feasible*\n188→ Pareto front between the two objectives among force-cap-satisfying designs.\n189→ (Keep this formulation consistent everywhere — do not present \"SEA vs. peak\n190→ force\" as the trade-off.)\n191→- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192→ this advanced task (noisy, batched, constrained, multi-objective) in\n193→ minutes using [Honegumi](https://honegumi.readthedocs.io/) — the audience\n194→ can do the same for their own problems; show the code-template picker\n195→ briefly so they see how low the barrier is.\n196→\n197→### Transition\n198→\n199→The optimizer is no longer the bottleneck — the limit is now providing real\n200→data quickly enough.\n201→\n202→### Point 3\n203→\n204→Multi-material additive manufacturing supplies that data fast — single-build\n205→co-fabrication, with no joining of parts.\n206→\n207→- Single-build PLA–TPU co-fabrication: rigid struts and flexible tension\n208→ network in one print — no joining or hand assembly of members, which closes\n209→ the \"easy creation\" gap from the Need and is what makes future lattices\n210→ feasible at all. (Be precise: if support removal, trimming, or conditioning\n211→ remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212→ assembly.\")\n213→- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214→ as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215→ pre-tension status) plus a short deformation sequence — so the EDL engineer\n216→ doesn't file it as a \"crush structure\" and the grad student can tell it from\n217→ an ordinary lattice.\n218→- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219→ tested specimen (quasi-static compression + instrumented drop-weight impact)\n220→ in hours. (Backup slide: representative print-to-result time breakdown and\n221→ which steps remain manual — candidate selection is automated; slicing,\n222→ handling, and testing are not yet.)\n223→- Now we have the data we need to complete the loop.\n224→\n225→### Point 4 (optional — future applications)\n226→\n227→*Include if timing allows; otherwise fold into the Conclusion.*\n228→\n229→The same closed loop transfers wherever tests are authoritative, each test is\n230→costly, the design space is mixed, and several performance goals conflict:\n231→\n232→- Tensegrity *lattices* and more complex architectures — exactly where hand\n233→ assembly becomes infeasible and single-build co-fabrication pays off.\n234→- Migration from the PLA–TPU proxy toward flight-relevant materials and\n235→ impact regimes.\n236→- Safety hardware on Earth (e.g., the tensegrity crutch tip — future work).\n237→- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238→ handling, testing) in the loop.\n239→\n240→### Evidence / results\n241→\n242→*What we found — ground the conclusion in shown data*\n243→\n244→Build the talk around a quantitative three-slide sequence (per the\n245→mock-audience report; insert observed values once the campaign completes):\n246→\n247→1. **Campaign ledger** — \"The campaign used N specimens across B adaptive\n248→ batches under a fixed physical-test budget\": initialization vs. adaptive\n249→ batches, replicates, print failures, cycle time, and the numerical value\n250→ (and rationale) of the force cap.\n251→2. **Budget-matched baseline** — \"Adaptive selection improved the feasible\n252→ design set faster than [predeclared baseline]\": performance vs. number of\n253→ physical tests against Sobol/random sampling at the same budget, with\n254→ uncertainty where available.\n255→3. **Measured feasible Pareto front** — specific energy absorption vs.\n256→ compaction efficiency among designs satisfying the force cap, with\n257→ replicate spread, marked infeasible designs, and specimen photos or\n258→ force–time traces for representative Pareto designs.\n259→\n260→Supporting raw-data candidates: drop-test force–time traces, Sobol /\n261→first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262→\n263→### Suggested 15-minute allocation\n264→\n265→*(From the mock-audience report — results get the largest block.)*\n266→\n267→| Time | Content |\n268→|---|---|\n269→| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270→| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271→| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272→| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273→| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274→| 1 min | Limitations and transfer conditions |\n275→| 1 min | Measured conclusion + bookend |\n276→\n277→Move kernel choices, encodings, and other implementation detail to backup\n278→slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279→[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280→\n281→## Closing\n282→\n283→### Review\n284→\n285→*A recap of the body, leading into the conclusion*\n286→\n287→Using real, measured impact data from additively manufactured\n288→tensegrity-inspired structures — parametric designs proposed by a Bayesian\n289→optimization routine — we rapidly iterate toward better energy absorbers.\n290→This feedback loop makes tensegrity optimization dramatically faster: dozens\n291→of prints instead of the hundreds a traditional design of experiments over\n292→this design space would require.\n293→\n294→### Conclusion\n295→\n296→*What the above means to the audience in the end*\n297→\n298→Because the loop runs on physical measurements, it is useful precisely where\n299→model calibration is not worth its cost — and the same closed-loop pattern\n300→should transfer (a conditional claim, pending evidence) to other additively\n301→manufactured architectures whose performance is dominated by\n302→hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303→*use this loop when tests are authoritative, each test is costly, the design\n304→space is mixed, and several performance goals conflict.* For tensegrity\n305→specifically, it opens a path from this T3 proxy study toward lattices,\n306→flight-relevant materials, payload protection on other planets, and safety\n307→hardware here on Earth (e.g., the crutch tip, as future work).\n308→\n309→### Close\n310→\n311→*A way to end the presentation clearly and elegantly*\n312→\n313→Return to the opening image: the next planetary lander's energy absorber — a\n314→structure that has to survive not one impact but many — doesn't have to take\n315→years of hand-built prototypes; with this loop, candidate designs can be\n316→designed, printed, and tested against real impacts in weeks instead of months\n317→or longer. End on the measured engineering result and its transfer condition,\n318→then the bookend: every method that compresses design time moves our ventures\n319→— on this planet and beyond — forward.\n320→\n321→---\n322→\n323→## Changes from Draft 2\n324→\n325→Applied from @sgbaird's inline review comments on PR #84 and the\n326→[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327→\n328→**From the inline review comments:**\n329→\n330→1. **Reusability in the hook** (line 22): the tensegrity lander survives\n331→ multiple drops, unlike crushable/sacrificial alternatives.\n332→2. **Proxy-system framing** (lines 49, 87): PLA–TPU would never fly; it is a\n333→ proxy for prototyping a self-driving-lab-style workflow — stated in a\n334→ scope note up front and re-stated right after the hook.\n335→3. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336→ heavy-duty simulation falls short of the experiments (millisecond-scale\n337→ peak shock), not that it merely costs too much.\n338→4. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339→ complex structures and especially lattices are not.\n340→5. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341→ (temperature, print speed, …) only; the rest is explicitly future work.\n342→6. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343→ literature search (or validation of experiments against simulation), plus\n344→ the millisecond-resolution peak-shock argument.\n345→7. **Optional Point 4 added** (line 97): future applications, with a note it\n346→ can fold into the Conclusion if time is short.\n347→8. **Steel-materials point** (line 104): simulations fall short for the\n348→ actual engineering materials (steel struts/cables), not just printed\n349→ PLA–TPU.\n350→9. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351→ in minutes, pitched so the audience sees they can use it too.\n352→10. **\"Thousands\" → \"hundreds\"** (line 173): baseline is now a traditional\n353→ design of experiments over a high-dimensional space (hundreds), in the\n354→ main message, Need, Point 1, and Review.\n355→11. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356→ or longer\" for candidate designs.\n357→\n358→**From the mock-audience report's priority revisions:**\n359→\n360→12. **Quantitative three-slide results sequence** (Priority 1): campaign\n361→ ledger → budget-matched baseline → measured feasible Pareto front, with a\n362→ fallback main-message wording if the campaign can't yet support the\n363→ efficiency claim.\n364→13. **Anti-simulation premise replaced with a scoped cost decision**\n365→ (Priority 2): Point 1 retitled to the information-value message;\n366→ \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367→ anticipated with a backup slide.\n368→14. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369→ implementation detail moved to backup slides; Q&A prep pointed at the\n370→ report's claims-vs-evidence table.\n371→15. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372→ the main talk, machinery in backup slides; the misleading\n373→ \"qNEHVI over standard expected improvement\" comparison replaced with\n374→ \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375→16. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376→ sequence in Point 3, so the structure isn't remembered as a generic crush\n377→ structure or lattice.\n378→17. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379→ co-fabrication / no joining of parts,\" with a note to list remaining\n380→ manual steps and an automation-status caveat.\n381→18. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382→ compaction efficiency, constraint = force cap, deliverable = *feasible*\n383→ Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384→19. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385→ transfer claim marked conditional, and the close ends on the measured\n386→ result before the bookend.\n387→\n388→## Changes from Draft 1\n389→\n390→Feedback from the PR #84 review, applied in Draft 2:\n391→\n392→1. **Main message** re-centered on speed + real data (\"robust\" → the\n393→ dozens-not-thousands claim); body and message now carry the same story.\n394→2. **\"Combining real and simulated data\" removed** (Review section) — the\n395→ manuscript's positioning (#75/#76) is that the physical experiment is the\n396→ source of truth, explicitly avoiding calibrated FE simulation.\n397→3. **\"Generative design\" → \"parametric designs\"** to match the manuscript's\n398→ parametric design space and avoid an over-claim to a design-automation\n399→ audience.\n400→4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401→ energy absorption and compaction efficiency, cap peak transmitted force.\n402→5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403→ *and* the space is too big to brute-force — which makes BO + AM the\n404→ inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405→ above.)*\n406→6. **Point 1 given its own content** (the *why* behind the inefficiency)\n407→ instead of restating the Need.\n408→7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409→ the specifics (noise-aware acquisition, small batches, multi-objective).\n410→8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411→ (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412→ above.)*\n413→9. **Evidence/results slot added** before the Closing, so the Conclusion is\n414→ grounded in shown data rather than three hedged \"may\"s.\n415→10. **Attention getter compressed** to 1–2 sentences of setup plus the Super\n416→ Ball Bot visual; verified video links included.\n417→11. **Close bookends the opening** (the next lander's absorber, designed in\n418→ weeks) before the broader humankind line.\n419→12. **Spelling**: Doumont (was \"Duomont\").","name":"read","tool_call_id":"call_93haurOMDGiH2mQFFbaMaYKn"}} +{"ts":"2026-07-26T02:29:25.494918+00:00","step":2,"compaction_occurred":false,"message":{"role":"tool","content":" 1→# Notes on Jean-luc Doumont — \"Creating Effective Slides\"\n 2→\n 3→Video: (assigned in #83)\n 4→\n 5→Transcribed from @me-madsen's handwritten notes\n 6→([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7→dated 2026-07-15).\n 8→\n 9→## Overview\n10→\n11→A presentation is all about the right content, displayed the right way, at the\n12→right time.\n13→\n14→- Verbal communication is sequential.\n15→- Message is *interpretation* of the information.\n16→ - Ensure the audience can get the most out of what you're doing.\n17→ - Don't just give them information — help them understand it, interpret it,\n18→ and internalize it.\n19→\n20→## Doumont's suggested rules for a presentation\n21→\n22→0. Ensure you have **messages** for your audience, not just words.\n23→1. **Adapt to your audience.**\n24→ - Think like the audience, not the speaker.\n25→2. **Maximize signal-to-noise ratio.**\n26→ - Reduce noise (animations, laser pointers, anything that pulls attention\n27→ away from your message).\n28→3. **Use effective redundancy.**\n29→ - Tell your message in word *and* on slide: someone deaf should understand\n30→ your messages just as well as someone blind.\n31→ - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32→ date).\n33→\n34→## When creating slides\n35→\n36→- If the audience doesn't need it, why put it on the slides?\n37→- People shouldn't have to count the items on a slide to know how many are\n38→ there.\n39→- Don't use your slides as your \"presentation notes.\"\n40→ - Don't show your notes to the audience.\n41→ - **A bad slide is worse than no slide at all.**\n42→- **One message per slide** is a good rule of thumb.\n43→ - Prioritize the *so what* (or *therefore what*) of your message.\n44→- Make it easy to interpret the information on the slide:\n45→ - 3–5 items at most.\n46→ - Align the title left.\n47→ - Do not clutter slides with repetitive or unnecessary information: logos,\n48→ affiliation, date, slide numbers.\n49→- Doumont's suggested workflow:\n50→ 1. **Start with the title.**\n51→ - Use a full sentence in the title.\n52→ - The title is the main message of the slide.\n53→ - Make it easy to read: left-aligned, with intentional line breaks.\n54→ 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55→- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56→\n57→## General preparation\n58→\n59→- Planning for audience, content, and structure matters more than materials\n60→ (i.e., slides).\n61→- Try using one sheet of paper for presentation preparation.\n62→- It's most important that your presentation is:\n63→ - well planned, structured, and delivered;\n64→ - **if you don't have a lot of time to prepare, don't focus on making\n65→ slides.**\n66→- Practice the presentation a few times. Practice at least once *without* your\n67→ slideshow!\n68→- If doing a handout, keep it separate from the slides you're preparing.\n69→- Impress the audience with your body.\n70→ - Use gestures, not laser pointers.\n71→ - \"Control\" the audience with your body — keep their attention on your\n72→ presentation.\n73→\n74→## Impressions / what to apply to the IDETC presentation\n75→\n76→\n77→\n78→## Questions / things to discuss\n79→\n80→","name":"read","tool_call_id":"call_tYXZgr4B8yjH0YEPyPZih9qI"}} +{"ts":"2026-07-26T02:29:25.494918+00:00","step":2,"compaction_occurred":false,"message":{"role":"tool","content":" 1→\\documentclass[10pt]{article}\n 2→\n 3→% --- Packages ---\n 4→\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5→\\usepackage[hidelinks]{hyperref}\n 6→\\usepackage{parskip}\n 7→\\usepackage{graphicx}\n 8→\\usepackage{caption}\n 9→\\usepackage{titlesec}\n 10→\\usepackage{wrapfig}\n 11→\\usepackage[numbers,sort&compress]{natbib}\n 12→\n 13→% --- Formatting ---\n 14→\\setlength{\\parskip}{0.2em}\n 15→\\setlength{\\parindent}{0em}\n 16→\\titleformat*{\\section}{\\large\\bfseries}\n 17→\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18→\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19→\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20→\\setlength{\\emergencystretch}{2em}\n 21→\\setlength{\\intextsep}{0.2em}\n 22→\\bibliographystyle{abbrvnat}\n 23→\n 24→\\begin{document}\n 25→\n 26→\\begin{center}\n 27→{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28→3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29→\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30→\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31→\\end{center}\n 32→\n 33→\\vspace{0.4em}\n 34→\n 35→\\section*{Abstract}\n 36→Tensegrity-inspired architectures---rigid struts suspended within a\n 37→continuous flexible network---can exhibit tunable nonlinear\n 38→force--displacement responses and favorable energy absorption per unit\n 39→mass, motivating their use in lightweight energy-absorbing and protective\n 40→structures\n 41→\\citep{skelton2009tensegrity,pajunen2019design}.\n 42→Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43→struts and flexible (TPU) elements in a single build\n 44→\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45→space---strut geometry, tension-element cross-section, connectivity\n 46→topology, and unit-cell tiling---is too large to explore by trial and\n 47→error. We present a closed-loop experimental campaign that uses\n 48→Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49→to drive a design--print--test workflow operating directly on physical\n 50→measurements, without relying on calibrated finite-element simulation\n 51→for objective evaluation.\n 52→\n 53→\\section{Introduction and Motivation}\n 54→\n 55→\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56→\\centering\n 57→\\vspace{-0.4em}\n 58→\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59→\\captionsetup{font=small}\n 60→\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61→Candidate tensegrity-inspired unit cells are instantiated from a\n 62→parameter vector, fabricated by multi-material FDM, and tested under\n 63→quasi-static compression and drop-weight impact. Measured peak force,\n 64→specific energy absorption (SEA), and compaction efficiency update a\n 65→Gaussian-process surrogate that proposes the next batch of designs.}\n 66→\\label{fig:overview}\n 67→\\vspace{-0.4em}\n 68→\\end{wrapfigure}\n 69→\n 70→Tensegrity structures combine compression-only members with a\n 71→pre-tensioned tension network to achieve stiffness without rigid\n 72→joinery~\\citep{skelton2009tensegrity}. Their\n 73→nonlinear, often load-limiting force--displacement response makes them\n 74→attractive for impact mitigation, packaging, and wearable protective\n 75→gear~\\citep{pajunen2019design}. Idealized\n 76→cable--strut tensegrities are difficult to manufacture at scale, but\n 77→\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78→3D printing reproduce key tensegrity behaviors---including\n 79→post-buckling stability and load-limiting force--displacement\n 80→response~\\citep{pajunen2019design}---motivating our extension to\n 81→multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82→elements. PLA provides rigid struts; TPU provides flexible,\n 83→rate-dependent tension elements whose viscoelasticity is expected to\n 84→contribute hysteretic energy dissipation under impact loading,\n 85→consistent with the energy-absorbing behavior reported for rigid--TPU\n 86→multi-material prints~\\citep{khatri2024energy}.\n 87→\n 88→The challenge is that small geometric and topological changes can\n 89→produce large changes in the measured response, and high-fidelity\n 90→simulation of FDM specimens is computationally expensive and often\n 91→struggles to capture physical defects such as interfacial slip and\n 92→TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93→trial-and-error sweeps over the joint design space are also infeasible:\n 94→even modest parameter resolutions yield thousands of candidates, each\n 95→requiring print and test time. We instead treat the physical\n 96→experiment as the source of truth and use BO to select which specimens\n 97→are most informative to fabricate next---an approach used in related\n 98→closed-loop materials and structures\n 99→campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100→Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101→set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102→designs using parallel, noisy multi-objective BO driven by measured\n103→$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104→\n105→\\section{Approach}\n106→\\subsection{Closed-Loop Workflow}\n107→Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108→proceeds in four steps: (i)~a parameterized unit-cell design is\n109→instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110→is sliced and printed on a multi-material FDM system; (iii)~it is\n111→tested under quasi-static compression and instrumented drop-weight\n112→impact; and (iv)~the resulting performance metrics update a\n113→Gaussian-process (GP) surrogate, which a batch acquisition function\n114→queries to recommend the next set of candidates. Surrogate updating\n115→and experiment selection are automated; specimen handling, slicing,\n116→and test setup remain manual.\n117→\n118→\\subsection{Design Space}\n119→We parameterize a family of unit cells using a core-wrapping\n120→architecture in which rigid PLA struts are enclosed by continuous TPU\n121→skins; the wrapping rationale follows multi-material FDM strategies in\n122→which rigid panels are wrapped by stretchable soft material to prevent\n123→delamination and improve interfacial bonding at the rigid--flexible\n124→interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125→comprises continuous geometric variables (strut diameter and length,\n126→tension-element width and thickness), an integer strut count per unit\n127→cell, and two categorical variables (connectivity topology, unit-cell\n128→tiling pattern). Categorical choices are encoded explicitly rather\n129→than embedded in a continuous metric, and bounds on the continuous\n130→variables are chosen to remain within the printer's resolution and\n131→overhang limits and to keep specimens in a common form factor for the\n132→test fixtures.\n133→\n134→\\subsection{Surrogate and Acquisition}\n135→The GP surrogate is trained on three measured performance metrics:\n136→peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137→(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138→objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139→likelihood with priors to avoid pathological length scales at small $n$.\n140→Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141→interfacial defects motivates our use of q-noisy expected hypervolume\n142→improvement (qNEHVI), which explicitly accounts for observation noise\n143→in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144→expected-improvement variants. Print failures are treated as a\n145→probabilistic feasibility constraint: the acquisition score for each\n146→candidate is its qNEHVI improvement weighted by the GP-estimated\n147→probability of successful fabrication, following recent constrained\n148→multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149→Where applicable, we use numerically stable log-space variants of\n150→improvement-based acquisitions to mitigate vanishing-gradient\n151→pathologies as data and constraints\n152→accumulate~\\citep{ament2023logei}.\n153→\n154→\\section{Methods}\n155→\\subsection{Fabrication}\n156→Specimens are fabricated on a multi-material FDM system capable of\n157→co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158→Print parameters (temperature, line width, infill, retraction) are\n159→held fixed within a batch to isolate the effect of the design\n160→variables. Each specimen is weighed and dimensionally inspected prior\n161→to testing so that mass and geometric deviations are available as\n162→covariates.\n163→\n164→\\subsection{Mechanical Testing}\n165→Quasi-static compression is performed on a screw-driven load frame at\n166→a fixed nominal strain rate, with force--displacement recorded to\n167→densification. Drop-weight impact testing uses an instrumented tup at\n168→a fixed impact energy; transmitted force is captured at high sample\n169→rate, and specimens are imaged before and after impact to document\n170→failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171→unit mass, and compaction efficiency $\\eta$, defined consistently\n172→across the campaign. A control specimen from an earlier batch is\n173→retested periodically to monitor TPU batch-to-batch and printer drift.\n174→\n175→\\section{Expected Outcomes and Discussion}\n176→We will report how BO-selected designs evolve across iterations, how\n177→GP surrogate predictions and uncertainties change as data accumulate,\n178→and which Pareto-efficient trade-offs emerge between SEA and peak\n179→transmitted force within the explored design space. We will also\n180→discuss practical lessons for operating the closed-loop workflow,\n181→including handling of print failures, batch-to-batch variability in\n182→TPU response, the influence of the rigid--flexible interface on\n183→energy absorption, and the exploration--exploitation balance of the\n184→acquisition function under realistic experimental noise. We expect\n185→the campaign to clarify where physical experimentation is genuinely\n186→required versus where simpler analytical or multi-fidelity surrogate\n187→shortcuts~\\citep{mo2023accelerated} could be\n188→substituted in future work.\n189→\n190→\\section{Conclusion}\n191→This work demonstrates a partially automated, experiment-driven\n192→closed-loop pipeline for designing multi-material 3D-printed\n193→tensegrity-inspired energy absorbers, with surrogate updating and\n194→candidate selection automated and fabrication and testing kept manual.\n195→By updating a GP surrogate directly from physical measurements, the\n196→workflow avoids direct dependence on calibrated finite-element\n197→simulation for objective evaluation while still providing principled,\n198→multi-objective design recommendations. The same closed-loop pattern is\n199→expected to transfer to other multi-material additively manufactured\n200→architectures whose performance is dominated by hard-to-simulate\n201→effects.\n202→\n203→{\\tiny\n204→\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205→\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206→% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207→% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208→\\providecommand{\\doi}[1]{}\n209→\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210→\\bibliography{references}\n211→}\n212→\n213→\\end{document}","name":"read","tool_call_id":"call_dlPcqwDfFi7a24fnE1yNh10b"}} +{"ts":"2026-07-26T02:29:25.494918+00:00","step":2,"compaction_occurred":false,"message":{"role":"assistant","content":null,"function_call":null,"tool_calls":[{"id":"call_Dr3LokFRC7vtM1o4UzyNoEFW","type":"function","function":{"arguments":"{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\", \"offset\": 174, \"limit\": 100}","name":"read"}}]}} +{"ts":"2026-07-26T02:31:54.056575+00:00","step":3,"compaction_occurred":false,"message":{"role":"tool","content":"175→\n176→## Slide 14 — layout: Title Slide","name":"read","tool_call_id":"call_Dr3LokFRC7vtM1o4UzyNoEFW"}} +{"ts":"2026-07-26T02:31:54.056575+00:00","step":3,"compaction_occurred":false,"message":{"role":"assistant","content":null,"function_call":null,"tool_calls":[{"id":"call_8In1tJq32AD2tTyHX3tmIlLf","type":"function","function":{"arguments":"{\"answer\": \"# Mock audience report: IDETC-CIE 2026 slide deck, Draft 1\\n\\n## Executive read\\n\\nThe visible deck has a clear visual spine: **motivation \\u2192 slow iteration \\u2192 Bayesian optimization \\u2192 multi-material printing \\u2192 impact testing \\u2192 closed loop \\u2192 future applications**. The strongest section is slides 8\\u201310, where fabrication, testing, and adaptive selection finally appear as one workflow.\\n\\nBut Draft 1 does not yet deliver the technical case promised by the agreed outline. The visible deck omits four things a DAC-10 audience will need:\\n\\n1. **Honest scope:** no visible slide says that the PLA\\u2013TPU T3 prism is a proxy system rather than flight hardware.\\n2. **A precise optimization problem:** the six variables, two objectives, force constraint, noise treatment, and fabrication failures are not intelligible on slide 7.\\n3. **Quantitative evidence:** the sole results slot is hidden and empty, yet slides 10 and 12 already claim acceleration and transfer.\\n4. **A measured conclusion:** the talk jumps from the workflow to speculative applications without showing a campaign ledger, comparison against a budget-matched baseline, or feasible Pareto front.\\n\\nThe slide titles generally follow Doumont\\u2019s full-sentence-message rule, but several messages outrun the evidence. The deck also departs substantially from the Draft 3 timing plan, which reserves **5 of 15 minutes for results**. As built, results receive zero visible slides.\\n\\n---\\n\\n# P0. The program manager in the audience\\n\\n## (a) My reaction in the room\\n\\nSlide 1 tells me the team wants to work faster, but it does not tell me what is being optimized or what has actually been built. The title sounds aspirational rather than traceable to a project deliverable. I immediately want the formal title from the abstract or a shorter version of it.\\n\\nThe Super Ball Bot footage on slide 2 is an effective hook. I understand why reusable impact attenuation matters. But the slide creates a scope risk: I am looking at planetary landing hardware while the actual work is a printed PLA\\u2013TPU T3 proxy. Because that distinction is not stated immediately, I may spend the rest of the talk evaluating the work against flight-readiness criteria the project never intended to meet.\\n\\nSlide 4 gives me the business problem, but not a baseline. \\u201cSlow and resource intensive\\u201d needs a number: hours per print-test cycle, number of factors, or the candidate count under a declared design of experiments. Without that, I cannot assess whether the proposed loop attacks the dominant schedule driver.\\n\\nSlide 7 is where I expect the project definition. Instead, \\u201c6 variables,\\u201d \\u201c2 types of input data,\\u201d and \\u201c2 objectives\\u201d read like inventory labels. I cannot reconstruct the statement of work from them. I need to see what is automated, what remains manual, what the force cap is, and what constitutes a successful campaign.\\n\\nSlides 8 and 9 make the project tangible. A labeled specimen and an actual drop test would build confidence. Yet \\u201csingle-build\\u201d and \\u201cno joining\\u201d need a process qualification caveat, and I need the measurement chain to be explicit. The abstract refers to an instrumented tup and force\\u2013time data; slide 9 says accelerometers. Those may be compatible, but the deck presently leaves the relationship unclear.\\n\\nSlide 10 is the right management view of the system, but \\u201csignificantly accelerate\\u201d is a result claim placed on a workflow diagram. At this point I ask, \\u201cCompared with what, by how much, and over how many specimens?\\u201d Then the deck skips the hidden results slide and goes directly to future applications. That is the point where my confidence drops sharply. The project is asking me to extrapolate before closing the evidence loop.\\n\\nSlide 12 is too early and too broad. A crutch tip and lattice can be legitimate next phases, but only after the team shows what the present campaign established and what technical risks remain. The blank questions slide gives me no durable take-home message.\\n\\n### PM stage-risk register\\n\\n| Risk | Likelihood | Consequence | Mitigation |\\n|---|---:|---:|---|\\n| Results are incomplete by conference lock | Medium to high | Critical: central claim becomes unsupported | Set a dated campaign freeze; predeclare a fallback message and minimum publishable result package |\\n| Embedded Super Ball Bot or drop-test video fails | Medium | Moderate: weak opening or broken pacing | Use local, trimmed files; retain a still-frame fallback on every video slide |\\n| Talk overruns because several videos and explanations are improvised | High | High: results or conclusion gets cut | Script videos to 10\\u201315 seconds total each; rehearse to 13:30; assign 5 minutes to results |\\n| Scope is mistaken for flight-hardware validation | High | High: credibility damage in Q&A | Put \\u201cPLA\\u2013TPU T3 proxy; not flight hardware\\u201d on slide 2 or the next slide |\\n| Acceleration claim lacks a budget-matched comparator | High | Critical for DAC review audience | Show Sobol/random or another predeclared baseline at the same physical-test budget |\\n| Measurement chain is challenged | Medium | High | Add a methods schematic and backup slide on calibration, sampling, filtering, and metric extraction |\\n| \\u201cNo joining\\u201d or \\u201cnear autonomous\\u201d is read as full automation | Medium | Moderate | List manual steps explicitly: slicing, handling, setup, and testing |\\n| Animations distract or fail to communicate without narration | Medium | Moderate | Prefer one short clip plus static annotated frames; follow Doumont\\u2019s signal-to-noise rule |\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey are building a loop that prints PLA\\u2013TPU tensegrity-like absorbers, drop-tests them, and lets Bayesian optimization choose the next print.\\u201d\\n\\nThat retains the basic workflow but loses the intended **dozens rather than hundreds** claim because the deck never demonstrates it. I would also probably describe the work as \\u201clander research,\\u201d which is a distortion caused by the missing proxy-system statement.\\n\\n## (c) My top three Q&A questions\\n\\n1. What is the dated plan to complete the campaign, baseline comparison, and repeat testing before the conference slide freeze?\\n2. Which exact operations are automated today, which remain manual, and what is the measured end-to-end cycle time per adaptive batch?\\n3. What evidence will justify \\u201cdozens rather than hundreds,\\u201d and what will you claim if the budget-matched baseline does not show an advantage?\\n\\n## (d) My most pointed objection\\n\\n**You are presenting a claimed acceleration without showing a completed campaign, a comparator, or even a visible results slide.**\\n\\nUnanswered, this is **critical**. I would not fund a scale-up phase from this deck. I might fund completion of the present campaign if the team showed a disciplined schedule, predefined success criteria, and a credible fallback.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 11.** It must become a real, scheduled results package, preferably three slides: campaign ledger, budget-matched baseline, and feasible Pareto front.\\n- **Hidden-slide decision:** **Unhide slide 11**, but not in its current empty form. The talk cannot make its principal claim without it.\\n\\n### PM slide-level punch list, ordered by schedule criticality\\n\\n**Must be fixed first**\\n\\n1. **Slide 11/results sequence:** assign owners and dates for data freeze, quality control, baseline computation, replicate analysis, and final plots. Define fallback wording now.\\n2. **Slide 2:** add the explicit scope line, \\u201cThis study uses a printed PLA\\u2013TPU T3 prism as a proxy for developing the workflow; it is not flight hardware.\\u201d\\n3. **Slide 7:** replace category counts with the actual optimization problem, test budget, batch size, objectives, constraint, and noise/failure treatment.\\n4. **Slide 10:** remove \\u201csignificantly\\u201d until slide 11 demonstrates a quantitative improvement; show automation boundaries and the loop\\u2019s measured cycle time.\\n5. **Slides 2 and 9:** download and trim media, test on the conference laptop, and put a static fallback image on each slide.\\n\\n**Then fix**\\n\\n6. **Slide 4:** quantify the current cost or candidate count and identify the bottleneck.\\n7. **Slide 8:** label PLA, TPU, interface, and any post-processing; qualify \\u201cno joining.\\u201d\\n8. **Slide 9:** reconcile accelerometers with the instrumented-tup description and show how force, energy absorption, and compaction efficiency are obtained.\\n9. **Slide 12:** convert the application collage into a conditional next-phase roadmap with gates.\\n10. **Slide 13:** end on the measured result and deployment rule, not a blank Q&A screen.\\n\\n**Nice to have**\\n\\n11. Replace decorative or looping media on slide 4 with one static process image unless it communicates a measured delay.\\n12. Add backup slides on Bayesian optimization implementation, finite-element-analysis positioning, repeatability, force-cap rationale, and print failures.\\n\\n---\\n\\n# P1. The skeptical Bayesian-optimization insider\\n\\n## (a) My reaction in the room\\n\\nThe opening is competent but generic for a design-automation conference. Slides 1\\u20134 set up an expensive physical experiment, which is a valid application class for Bayesian optimization (BO). I become interested when the presenter reaches slide 7, because that is where methodological seriousness should appear.\\n\\nInstead, slide 7 says only \\u201c6 variables,\\u201d \\u201c2 types of input data,\\u201d and \\u201c2 objectives.\\u201d I do not learn the variables, their types, how categorical choices are encoded, whether process parameters are optimized, how noisy observations enter the Gaussian processes, how replicates identify noise, how failures are modeled, or how the force cap enters the acquisition. The title says BO handles noisy data, but the visual does not substantiate that message.\\n\\nThere is also an unresolved specification conflict across the supplied materials. The agreed outline says the study jointly optimizes T3 geometry and FDM processing parameters. The abstract\\u2019s design vector lists four continuous geometric variables, one integer, and two categorical variables, apparently **seven variables**, while its fabrication section says print parameters are held fixed within a batch. The slide says **six variables**. A DAC audience will notice if the spoken details do not reconcile these versions.\\n\\nSlides 8\\u201310 explain why physical feedback is useful, but they do not establish why qNEHVI is the right algorithm or whether adaptive selection helped. The visible deck never names qNEHVI, the feasible Pareto front, the peak-force constraint, batch size, initialization, or test budget. Hiding slide 5 is reasonable if it is only a generic \\u201cwhat is BO?\\u201d slide copied from another talk. But slide 7 must then carry the application-specific methodological content.\\n\\nWhen slide 10 says the workflow \\u201csignificantly accelerate[s]\\u201d optimization, I wait for a performance-versus-budget curve. It never arrives. Slide 12 then generalizes to other applications. My default conclusion is exactly the skeptical one posed in the prompt: this appears to be off-the-shelf qNEHVI attached to an interesting physical apparatus, with no demonstrated algorithmic or empirical advantage yet.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cIt was an experimental self-driving-lab-style application of batch multi-objective BO to printed impact absorbers, but the talk did not show whether BO beat space-filling sampling.\\u201d\\n\\nThat differs from the intended message because I would not repeat \\u201cdozens, not hundreds\\u201d without a budget-matched baseline.\\n\\n## (c) My top three Q&A questions\\n\\n1. What are the exact decision variables and encodings, and why do the deck, scope statement, and abstract appear to disagree on whether there are six or seven variables and whether FDM parameters are optimized?\\n2. How is observation noise estimated: fixed noise, learned heteroscedastic noise, technical replicates, or repeated control specimens? What is the replicate allocation?\\n3. Against what budget-matched baseline do you evaluate qNEHVI, and what metric do you report: feasible hypervolume, best feasible objective values, or attainment probability versus physical tests?\\n\\n## (d) My most pointed objection\\n\\n**Nothing visible distinguishes a validated BO contribution from an untested application of standard qNEHVI.**\\n\\nUnanswered, this is **critical for DAC-10 reception**. Application novelty can be enough, but only if the physical campaign is rigorous and the adaptive method is compared fairly under the same test budget.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 7.** Replace the black-box inventory with a visual of posterior mean plus uncertainty \\u2192 constrained batch acquisition \\u2192 next physical tests, accompanied by the exact objectives and force constraint.\\n- **Hidden-slide decision:** **Keep slide 5 hidden** as a standalone generic BO tutorial. Merge one plain-language BO sentence into slide 7 and put qNEHVI details in backup.\\n\\n---\\n\\n# P2. The aerospace practitioner\\n\\n## (a) My reaction in the room\\n\\nThe Super Ball Bot hook works because I know the lineage. Reusable landing structures are interesting. But the title\\u2019s claim that tensegrity provides \\u201crobust, reusable solutions\\u201d is broader than what this deck demonstrates. A lab drop of a polymer proxy is not evidence of robustness in thermal extremes, vacuum, dust, radiation, repeated impacts, or off-axis terrain contact.\\n\\nThe deck never immediately tells me that the actual specimen is **tensegrity-inspired**, lacks ideal compression-only and tension-only membership, and is not intended to fly. That makes slide 2 vulnerable to looking like borrowed aerospace relevance.\\n\\nSlide 4 is plausible but too broad. Tensegrity design can be difficult, but aerospace development is not made slow only by design-space search. Verification, environmental qualification, uncertainty, packaging, guidance, and system integration matter. A faster specimen loop is useful, but it is one subsystem tool.\\n\\nI am not helped much by slide 7. I need BO translated into engineering behavior: it chooses the next small batch based on predicted performance and uncertainty while rejecting designs expected to violate a payload-force limit. The slide should name that limit and show the actual trade-off.\\n\\nSlides 8 and 9 are the first ones that answer \\u201cwhat did you build and how did you hit it?\\u201d A labeled specimen, deformation sequence, drop mass, speed or energy, orientation, and force measurement would hold my attention. Two simultaneous looping videos may not. I also want to know whether the structure rebounds, survives repeated drops, and retains its response, because the opening emphasizes reuse.\\n\\nSlide 10 is understandable. Slide 12 loses me because it jumps from a T3 polymer coupon to lattices and crutch tips without stating transfer conditions or technology gates.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey use an optimizer to choose which 3D-printed soft\\u2013rigid impact absorber to drop-test next, with the eventual motivation of reusable landing structures.\\u201d\\n\\nThat is fairly close, but I would deliberately downgrade \\u201ctensegrity\\u201d to \\u201ctensegrity-inspired absorber\\u201d and treat planetary landing as motivation only.\\n\\n## (c) My top three Q&A questions\\n\\n1. What makes the printed T3 specimen mechanically tensegrity-inspired rather than an ordinary compliant crush structure, and is it actually prestressed?\\n2. What impact energy, mass, orientation, and transmitted-force cap are used, and how do they relate to a payload-protection requirement?\\n3. How does performance change over repeated impacts, and what failure modes or permanent set limit the claimed reusability?\\n\\n## (d) My most pointed objection\\n\\n**The opening borrows flight relevance and reusability, but the visible deck never establishes the proxy boundary or shows repeated-impact evidence.**\\n\\nUnanswered, this is **highly damaging** to credibility with aerospace practitioners, though it does not invalidate the optimization workflow itself.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 2.** Keep the hook, but place a photograph of the actual T3 specimen beside the Super Ball Bot and state \\u201cmotivation, not demonstrated flight readiness.\\u201d\\n- **Hidden-slide decision:** **Keep slide 3 hidden.** A generic elbow-friction GIF would distract and oversimplify. Merge a precise tensegrity-mechanism annotation into slide 8 instead.\\n\\n---\\n\\n# P3. The additive-manufacturing/materials researcher\\n\\n## (a) My reaction in the room\\n\\nSlides 1\\u20134 give me a recognizable motivation for rapid experimental iteration. My interest rises on slide 8 because multi-material fused deposition modeling is where the claimed loop can succeed or fail.\\n\\nThe message \\u201csingle-build co-fabrication, with no joining of parts\\u201d is attractive, but it needs exact boundaries. Is there support removal, trimming, conditioning, manual tensioning, or fixture insertion? Does \\u201csingle build\\u201d mean one tool-change sequence on one machine, or truly co-deposited interfaces throughout the architecture? Are PLA and thermoplastic polyurethane (TPU) mechanically interlocked, chemically bonded, or merely in contact?\\n\\nThe abstract provides more useful information than the slide: a core-wrapping architecture is intended to resist delamination, specimens are weighed and dimensionally inspected, and a control is retested to monitor drift. Those are exactly the details the talk should surface. Slide 8\\u2019s planned printing video will not replace a labeled cross-section or interface image.\\n\\nSlide 9 raises repeatability questions. TPU is rate-, temperature-, moisture-, and history-dependent. \\u201cLimited, noisy data\\u201d on slide 7 acknowledges noise but does not show the control plan. I need batch identity, drying/conditioning, build position, print orientation, machine state, interface failures, and replicates.\\n\\nThe deck also appears inconsistent about process parameters. The agreed scope says FDM processing parameters are optimized with structure, but the abstract says temperature, line width, infill, and retraction are held fixed within a batch. This could mean they vary between batches or are controlled rather than optimized, but the deck must say which.\\n\\nI like the physical loop on slide 10. I do not yet accept that multi-material printing \\u201csupplies data fast\\u201d until cycle time and failure rate are shown.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey co-print PLA and TPU impact specimens and use each mechanical test to guide the next design batch.\\u201d\\n\\nI would probably omit \\u201cno joining\\u201d and \\u201cdozens rather than hundreds\\u201d until I saw interface qualification, failure accounting, and campaign data.\\n\\n## (c) My top three Q&A questions\\n\\n1. How is the PLA\\u2013TPU interface designed and qualified, and what fraction of specimens fail by delamination or print defects?\\n2. Which material, machine, conditioning, and build-position variables are controlled, measured as covariates, or optimized?\\n3. How many true replicates and repeated control specimens are used to separate BO improvement from printer drift and TPU batch variation?\\n\\n## (d) My most pointed objection\\n\\n**The deck treats co-fabrication as an enabling fact without showing interface integrity, repeatability, or the remaining manual processing steps.**\\n\\nUnanswered, this is **highly damaging** because print variation is part of both the physical mechanism and the statistical noise model.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 8.** Replace or supplement the printing clip with an annotated specimen and interface detail: PLA struts, continuous TPU network, deposition/wrapping strategy, prestress status, post-processing, and observed failure modes.\\n- **Hidden-slide decision:** **Keep slide 3 hidden.** Its proposed generic GIFs do not answer the materials questions. Put the relevant deformation mechanism on slide 8.\\n\\n---\\n\\n# P4. The first-year graduate student\\n\\n## (a) My reaction in the room\\n\\nSlide 1 sounds positive, and slide 2 gives me a memorable reason to care. I understand that the lander survives an impact because the structure itself deforms. But I do not yet know what \\u201ctensegrity\\u201d means, and the hidden slide 3 would not necessarily fix that: an elbow GIF and a squishing GIF could leave me with an inaccurate friction-versus-elasticity story.\\n\\nSlide 4 is easy to follow. The three words \\u201cIteration, Building, Testing\\u201d form a simple problem statement, although the images need to make their relationship obvious.\\n\\nI get lost on slide 7. Six variables of what? What are the two input-data types? Are the two objectives \\u201cabsorb energy\\u201d and \\u201creduce force,\\u201d or something else? I do not know what a black box means in this setting. The title says Bayesian optimization works with noisy data, but I have not been shown how it decides anything.\\n\\nSlide 8 helps because I can see printing. Still, I need labels that distinguish the rigid and flexible parts and one sentence explaining why the object is \\u201ctensegrity-inspired.\\u201d Slide 9 is intuitive if the clips are synchronized and annotated. I would understand even better if the slide drew the metric extraction directly on a force\\u2013time or force\\u2013displacement trace.\\n\\nSlide 10 is the clearest slide in the deck. Printing \\u2192 testing \\u2192 optimizer \\u2192 next print is the idea I will remember. But it arrives after the technical terms rather than before them. I would benefit from seeing a version of this loop before slide 7 and then revisiting it after the methods.\\n\\nSlide 12 feels like a new presentation. I have not seen whether the current system worked, so the crutch tip and lattice do not feel earned. Then the talk ends on a blank question slide rather than telling me the answer.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey repeatedly print and drop-test flexible 3D structures, and a machine-learning model decides what to print next.\\u201d\\n\\nMy retelling loses the two objectives, the force constraint, and the \\u201cdozens rather than hundreds\\u201d quantitative point. I might incorrectly call the method generic machine learning rather than Bayesian optimization.\\n\\n## (c) My top three Q&A questions\\n\\n1. What exactly is tensegrity, and which parts of your printed specimen are in tension and compression?\\n2. What are the six things the optimizer can change, and what are the two scores it is trying to improve?\\n3. How does the optimizer know whether to try something uncertain rather than the design it currently thinks is best?\\n\\n## (d) My most pointed objection\\n\\n**The deck names the ingredients before giving me a plain-language map of how they work together.**\\n\\nUnanswered, this is **moderately damaging**. I retain the loop but lose the actual research question and technical contribution.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 7.** It needs one concrete candidate, predicted performance plus uncertainty, and the next selected batch, not abstract counts around a black box.\\n- **Hidden-slide decision:** **Unhide neither slide 3 nor slide 5 as written.** If forced to choose one, **keep slide 5 hidden** and integrate its one-sentence concept into a redesigned slide 7. Generic explanation would add time without restoring the missing optimization details.\\n\\n---\\n\\n# P5. The finite-element-analysis veteran\\n\\n## (a) My reaction in the room\\n\\nThe opening is fine until slide 4 implies that physical iteration is the central route around slow design. I start waiting for the argument about simulation. Because slide 6 is hidden, the deck never states the agreed, defensible position: a sufficiently calibrated model may cost more than it saves for this campaign, particularly when interfaces, defects, rate dependence, contact, and millisecond shock transients dominate.\\n\\nThat omission avoids an overt attack on finite-element analysis (FEA), which is good, but it also creates a logical hole. Why is physical BO the chosen source of objective values? Why not a hybrid strategy, reduced-order model, analytical screening, or multi-fidelity optimization? The deck merely proceeds from \\u201cdesign is slow\\u201d to \\u201cBO makes experimentation affordable.\\u201d\\n\\nI object to any suggestion that experimental data are automatically \\u201creal\\u201d and simulations are not. Measurements also involve sensor bandwidth, filtering, fixture dynamics, uncertainty, and model-based transformations. Slide 9\\u2019s \\u201caccelerometers gather real data\\u201d wording is particularly vulnerable. A force estimate derived from acceleration still rests on calibration and assumptions.\\n\\nSlide 10 is a useful experimental workflow. It should be presented as a scoped engineering choice, not a universal replacement for simulation. Slide 12\\u2019s transfer claim needs the condition from the outline: use this pattern where tests are authoritative, costly, the design space is mixed, and goals conflict.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey bypass a calibrated finite-element model and optimize directly against impact tests because the printed interfaces and transient response are difficult to model economically.\\u201d\\n\\nThat is close to the intended experiment-first rationale, but I had to infer it; the visible slides do not say it clearly.\\n\\n## (c) My top three Q&A questions\\n\\n1. What evidence shows that calibrating a useful simulation would cost more than it saves for this campaign, rather than merely being inconvenient?\\n2. How do you verify the measured peak transmitted force, including sensor bandwidth, fixture dynamics, filtering, and uncertainty?\\n3. Why exclude analytical or low-fidelity screening from a multi-fidelity BO strategy, especially for infeasible geometries and gross structural trends?\\n\\n## (d) My most pointed objection\\n\\n**The deck uses experiment-first optimization without stating a scoped cost-and-validity argument or acknowledging where simulation could still add value.**\\n\\nUnanswered, this is **highly damaging in Q&A**. It makes the methodology look ideologically anti-simulation rather than economically reasoned.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 4.** It should become the two-sided decision: exhaustive physical search is too costly, while campaign-grade calibration for the hardest responses is also costly; therefore choose physical tests adaptively.\\n- **Hidden-slide decision:** **Merge slide 6 into slide 4**, rather than unhide it as written. Keep a fuller evidence-backed simulation/experiment comparison as backup.\\n\\n---\\n\\n# P6. The friendly industry generalist\\n\\n## (a) My reaction in the room\\n\\nThe opening video is memorable, and I like the practical goal of shortening build\\u2013test iteration. Slide 4 is easy to understand. My attention slips on slide 7 because the counts and black-box symbol do not tell a story. I do not need the name qNEHVI, but I need to know that the software balances trying promising designs with learning from uncertain ones and respects a force limit.\\n\\nSlides 8\\u201310 are the strongest run. If the presenter shows one labeled specimen, one clean drop clip, and one loop diagram, I can see how I might use the pattern in another product-development problem. I want a cycle-time number and an indication of what software or human work is required.\\n\\nSlide 12 dilutes the message by offering several destinations before proving the present result. The ending is weak. A blank Q&A slide discards the chance to leave the audience with the workflow, the best specimen, and a measured improvement.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cPrint it, break or drop it, feed the measurements back, and let the optimizer pick the next batch instead of running a huge test matrix.\\u201d\\n\\nThat captures the intended practical pattern, but I would probably overgeneralize it beyond the specific conditions under which it is useful.\\n\\n## (c) My top three Q&A questions\\n\\n1. How many prints and calendar days did it take to reach a useful design compared with your previous workflow?\\n2. What parts of this system could another engineering team reuse without building the same drop tower or writing a custom optimizer?\\n3. What happens when a print fails or a test produces an outlier?\\n\\n## (d) My most pointed objection\\n\\n**I understand the loop, but the deck never gives me the one number or result that proves it was worth building.**\\n\\nUnanswered, this is **moderately to highly damaging**. I would remember the concept but not have a reason to adopt or sponsor it.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 13.** Replace the blank Q&A screen with the best measured result, the closed-loop diagram, and one deployment rule; retain a small \\u201cQuestions?\\u201d label.\\n- **Hidden-slide decision:** **Unhide slide 11** once populated. Results are more valuable than another explanatory slide.\\n\\n---\\n\\n# Synthesis\\n\\n## Cross-persona themes\\n\\n1. **The scope boundary is missing.** The Super Ball Bot is a good hook, but every persona risks interpreting a PLA\\u2013TPU proxy as a claim about flight hardware.\\n2. **Slide 7 is the weakest visible technical slide.** It serves neither experts nor novices. Counts such as \\u201c6 variables\\u201d and \\u201c2 objectives\\u201d are information, not a message.\\n3. **The main claim currently lacks evidence.** \\u201cSignificantly accelerate,\\u201d \\u201csupplies data fast,\\u201d and \\u201cdozens, not hundreds\\u201d require a campaign ledger and a budget-matched comparison.\\n4. **The results omission breaks the story.** The deck follows the outline through the workflow, then skips the outline\\u2019s largest time block and moves directly to future work.\\n5. **The physical system needs more definition.** The audience needs a labeled specimen, a clear explanation of \\u201ctensegrity-inspired,\\u201d interface design, remaining manual steps, and the measurement chain.\\n6. **Noise must be operational rather than rhetorical.** Saying the data are noisy is insufficient. Replicates, periodic controls, drift, failures, and uncertainty should be shown.\\n7. **Experiment-first needs a scoped rationale.** The deck should not dismiss FEA, but it should explain why adaptive physical testing is the economical source of objective values for this campaign.\\n8. **The close is not a conclusion.** Future applications and a blank Q&A slide do not substitute for the measured finding and its conditions of transfer.\\n9. **Media are a communication and schedule risk.** Short clips can work, but multiple looping videos violate the signal-to-noise principle and create failure modes.\\n10. **The project specification needs reconciliation.** The supplied materials differ on six versus seven variables, whether FDM process parameters are optimized, and whether peak force is a constraint or part of the Pareto trade-off.\\n\\n## Five highest-priority slide-level revisions\\n\\n### 1. Slide 11: replace one empty slot with a three-slide quantitative results sequence\\n\\nUse full-sentence titles tied to observed data:\\n\\n- **11A:** \\u201cThe campaign used **N** specimens in **B** adaptive batches, including **R** replicates and **F** failed prints.\\u201d\\n- **11B:** \\u201cAt the same physical-test budget, adaptive selection reached **[measured feasible-performance result]** faster than **[predeclared Sobol/random baseline]**.\\u201d\\n- **11C:** \\u201cThe measured feasible Pareto front trades specific energy absorption against compaction efficiency while respecting a **[value and unit]** peak-force cap.\\u201d\\n\\nShow uncertainty, replicate spread, infeasible points, and representative specimen photographs or traces. If the campaign cannot support the baseline claim, change the talk\\u2019s main message to: **\\u201cWe evaluate whether noise-aware multi-objective Bayesian optimization can reduce the physical-test budget.\\u201d**\\n\\n### 2. Slide 7: state the actual constrained optimization problem\\n\\nReplace the current black-box/count design with:\\n\\n- the named decision variables and their types;\\n- a small batch entering the printer/test loop;\\n- objectives: maximize specific energy absorption and compaction efficiency;\\n- constraint: peak transmitted force below a stated cap;\\n- qNEHVI\\u2019s role in one plain sentence: it chooses a small next batch likely to expand the feasible trade-off frontier while accounting for uncertainty and noisy tests;\\n- the declared test budget, initialization size, and batch size.\\n\\nResolve six versus seven variables and whether processing parameters are decision variables before finalizing this slide.\\n\\n### 3. Slide 2: put the proxy boundary beside the hook\\n\\nKeep the Super Ball Bot clip, but add an image of the actual specimen and the visible sentence:\\n\\n> **\\u201cHere, a printed PLA\\u2013TPU T3 prism is a proxy for developing the workflow, not flight hardware.\\u201d**\\n\\nChange or qualify \\u201crobust, reusable solutions\\u201d unless the present campaign includes repeat-impact evidence.\\n\\n### 4. Slide 4: turn the vague need into the two-sided engineering decision\\n\\nRetitle it:\\n\\n> **\\u201cBecause exhaustive testing and campaign-grade model calibration are both costly, each physical specimen must be chosen for its information value.\\u201d**\\n\\nAdd one quantified physical-search estimate and one scoped calibration challenge. Do not say simulation is untrustworthy. State that FEA and lower-fidelity models remain possible screening or future multi-fidelity tools.\\n\\n### 5. Slides 10 and 13: separate the demonstrated loop from the conclusion\\n\\n- **Slide 10:** retitle to **\\u201cThe closed loop turns each measured test into the next small batch of specimens.\\u201d** Remove \\u201csignificantly accelerate\\u201d until the results demonstrate it. Mark automated and manual stages.\\n- **Slide 13:** retain the closed-loop thumbnail, add the best quantitative result, and state the deployment rule: **use the loop when tests are authoritative, each test is costly, the design space is mixed, and performance goals conflict.** Add \\u201cQuestions?\\u201d without clearing the conclusion from the screen.\\n\\n## Hidden-slide adjudication\\n\\n| Hidden slide | Recommendation | Rationale |\\n|---|---|---|\\n| **3: tensegrity usefulness/mechanism** | **Merge into slide 8** | The generic elbow and squishing GIFs add noise and risk an inaccurate mechanism; an annotated actual specimen and deformation sequence are stronger. |\\n| **5: generic BO explanation** | **Merge into slide 7; keep original as backup or delete** | DAC experts do not need a generic advocacy slide, but novices need one plain-language sentence and a concrete acquisition visual. |\\n| **6: specimen information value/experiment-first** | **Merge into slide 4; keep evidence-rich version as backup** | The argument is structurally necessary, but the current title is vague and the visual does not support the scoped calibration-versus-campaign-cost decision. |\\n| **11: results** | **Unhide and expand to three slides** | Results are the evidentiary center of the talk and were allocated five minutes in the agreed outline. An empty or hidden results slot makes the main claim untenable. |\\n\\n## Visible-slide claims needing evidence, hedging, or backup\\n\\n| Slide | Claim | Required treatment |\\n|---|---|---|\\n| **1** | \\u201cBuild better tensegrity structures faster\\u201d | Define \\u201cbetter\\u201d with the two objectives and force constraint; support \\u201cfaster\\u201d with the results comparison. |\\n| **2** | Tensegrity is robust and reusable for planetary landings | Cite the Super Ball Bot source; distinguish concept evidence from this study; provide repeat-impact evidence or hedge reusability. |\\n| **2** | Parachutes and retrorockets struggle in the stated conditions | Cite an authoritative aerospace source if retained in narration; avoid implying universal inadequacy. |\\n| **4** | Current tensegrity design is slow and resource intensive | Quantify candidate count, print/test time, hand-assembly burden, or calibration effort. |\\n| **7** | BO makes experiments affordable with limited, noisy data | Show campaign budget, noise characterization, replicates, baseline, and performance versus tests. Prepare qNEHVI and Gaussian-process details in backup. |\\n| **7** | Six variables, two input-data types, two objectives | Reconcile with the apparent seven-variable abstract specification and identify every quantity. Clarify \\u201cinput-data types.\\u201d |\\n| **8** | Multi-material AM supplies data fast | Report print-to-result cycle time and failure rate. |\\n| **8** | Single-build co-fabrication with no joining | Show interface and post-processing; use \\u201cno joining of PLA and TPU members\\u201d if other manual operations remain. |\\n| **9** | Accelerometers gather the relevant impact data | Show sensor locations and the derivation of transmitted force, specific energy absorption, and compaction efficiency. Reconcile this wording with the abstract\\u2019s instrumented-tup description. |\\n| **10** | The loop significantly accelerates optimization | Remove \\u201csignificantly\\u201d unless a defined baseline and uncertainty support it. Statistical significance should not be implied casually. |\\n| **12** | Workflow will transfer to lattices and other applications | Present as conditional future work with transfer criteria, not an achieved result. Attribute any external crutch-tip image and claim. |\\n\\n### Prepared backup slides\\n\\n1. Full design-variable table, bounds, encoding, and fixed versus optimized process parameters.\\n2. qNEHVI implementation, Gaussian-process assumptions, constraint treatment, batch size, and acquisition settings.\\n3. Noise plan: replicates, periodic control, heteroscedasticity, drift, outlier policy, and uncertainty propagation.\\n4. Budget-matched baseline definition and evaluation metric.\\n5. Force-cap rationale and full objective definitions.\\n6. Sensor chain, calibration, sample rate, filtering, fixture dynamics, and metric extraction.\\n7. PLA\\u2013TPU interface, conditioning, build layout, dimensional/mass deviations, and print-failure modes.\\n8. Scoped FEA rationale with citations or internal validation; possible analytical or multi-fidelity extensions.\\n9. Automation map and cycle-time breakdown.\\n10. Repeated-impact behavior if reusability remains in the opening.\\n\\n## Merged, ordered presenter TODO list\\n\\n### Must fix before the talk\\n\\n1. **Freeze the scientific specification.** Reconcile the number and type of decision variables, whether FDM parameters are optimized or controlled, the two objectives, the force constraint, treatment of print failures, and the measurement chain. Use one formulation across slides, abstract, paper, and Q&A.\\n2. **Set a results completion plan.** Establish dates and owners for campaign completion, repeat tests, data quality review, baseline analysis, and slide freeze. Predeclare the fallback claim if the baseline does not support \\u201cdozens rather than hundreds.\\u201d\\n3. **Build the three-slide results sequence.** Campaign ledger \\u2192 budget-matched baseline \\u2192 measured feasible Pareto front. Allocate approximately five minutes, as the agreed outline requires.\\n4. **Add the proxy statement to slide 2.** State plainly that the PLA\\u2013TPU T3 specimen is a workflow proxy and not flight hardware.\\n5. **Redesign slide 7.** Show the exact constrained multi-objective problem, test budget, small batches, uncertainty, noisy observations, and the role of qNEHVI.\\n6. **Remove unsupported result language.** Until supported, delete \\u201csignificantly\\u201d from slide 10 and avoid \\u201cdozens rather than hundreds\\u201d in the title, narration, or close.\\n7. **Clarify experiment-first positioning.** Merge the information-value argument into slide 4 and prepare an evidence-backed FEA backup slide.\\n8. **Make the experiment auditable.** On slide 9 or backup, reconcile accelerometers and the instrumented tup; show how all three performance metrics are calculated and how uncertainty is handled.\\n9. **De-risk every video.** Store local trimmed files, disable distracting loops, test them on presentation hardware, and include static fallback frames.\\n10. **Rehearse to 13:30\\u201314:00.** Protect the results and conclusion from being cut. Practice once without slides, consistent with Doumont\\u2019s preparation advice.\\n\\n### Should fix\\n\\n11. **Replace slide 8\\u2019s generic print emphasis with mechanism and materials evidence.** Label PLA, TPU, interface strategy, prestress status, deformation, post-processing, and failure modes.\\n12. **Quantify slide 4.** Give at least one campaign-relevant cost, duration, or candidate-count estimate.\\n13. **Show automation boundaries on slide 10.** Candidate selection and surrogate updating may be automated; slicing, handling, test setup, and testing remain manual according to the abstract.\\n14. **Convert slide 12 to a gated roadmap.** Separate demonstrated T3-proxy results from future lattices, flight materials, and terrestrial applications.\\n15. **Turn slide 13 into the conclusion.** Leave the audience with the best result and transfer rule while taking questions.\\n16. **Prepare technical backup slides.** Prioritize baseline fairness, noise, force-cap rationale, print failures, materials repeatability, and FEA positioning.\\n\\n### Polish\\n\\n17. Replace the slide 1 title with a specific message, such as **\\u201cPhysical Bayesian optimization searches multi-material tensegrity absorbers one small test batch at a time.\\u201d**\\n18. Left-align titles and use intentional line breaks; check every slide at six-per-page scale.\\n19. Use one purposeful visual per slide. Avoid decorative loading or elbow animations.\\n20. Standardize terminology: \\u201cBayesian optimization,\\u201d not \\u201cBays Opt\\u201d; \\u201ctensegrity-inspired\\u201d where appropriate; define specific energy absorption once.\\n21. Add citations directly beside borrowed footage, images, and quantitative claims.\\n\\n## Who the deck serves best and worst\\n\\n- **Best served:** **P6, the friendly industry generalist.** The visible sequence is visually intuitive, practical, and easy to reduce to \\u201cprint \\u2192 test \\u2192 learn \\u2192 repeat.\\u201d\\n- **Worst served:** **P1, the skeptical BO insider.** The current deck omits nearly every item needed to judge a DAC-10 contribution: exact problem formulation, budget, baseline, noise model, constraint handling, and empirical adaptive advantage.\\n\\nThat is **not the right trade-off for DAC-10**. The deck should remain accessible to P4 and P6, but its technical center must satisfy P1 without turning into a BO lecture. One rigorous optimization slide, three quantitative results slides, and targeted backup material would achieve that balance.\\n\\n## Predicted reception\\n\\n- **As is: 4/10.** The physical workflow and application are memorable, but the absent scope statement, empty results section, vague BO slide, and unsupported acceleration claim leave the DAC contribution unproven.\\n- **After the must-fixes: 8/10.** A scoped proxy claim, reconciled methods, fair budget-matched comparison, feasible Pareto front, and measured conclusion would turn the same visual story into a credible design-optimization application talk.\\n\\n## Discretionary analytical decisions\\n\\n- Evaluated only the visible slide order for the simulated in-room reactions, while using hidden slides and presenter notes to judge revision options.\\n- Treated planned placeholder visuals as if implemented according to the presenter\\u2019s notes, but judged the selected visual concept and associated stage risk rather than absent graphic quality.\\n- Prioritized the Draft 3 outline over conflicting Draft 1 wording because the prompt identifies it as the agreed story arc, scope framing, results plan, and timing plan.\\n- Rated objections qualitatively as moderate, high, or critical rather than assigning unsupported numerical probabilities.\\n- Recommended three results slides rather than preserving one reserved slot because the agreed outline specifies three distinct evidentiary products and allocates five minutes to results.\\n- Recommended merging hidden slides 3, 5, and 6 rather than simply unhiding them because their necessary ideas can be conveyed with higher signal-to-noise on existing slides.\\n- Treated the six-versus-seven-variable and optimized-versus-fixed-process-parameter differences as unresolved specification conflicts requiring reconciliation, rather than inferring which document is correct.\\n- Assessed claims against the supplied deck extraction, Draft 3 outline, Doumont notes, and submitted abstract only; no independent validation of cited literature or unseen experimental data was attempted.\"}","name":"submit_answer"}}]}} diff --git a/presentation/edison-mock-audience/artifacts/afade0b1-notebook.ipynb b/presentation/edison-mock-audience/artifacts/afade0b1-notebook.ipynb new file mode 100644 index 00000000..d1529b81 --- /dev/null +++ b/presentation/edison-mock-audience/artifacts/afade0b1-notebook.ipynb @@ -0,0 +1,12 @@ +{ + "cells": [], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/presentation/edison-mock-audience/failed-task-91cadd05-response.json b/presentation/edison-mock-audience/failed-task-91cadd05-response.json new file mode 100644 index 00000000..7201be7f --- /dev/null +++ b/presentation/edison-mock-audience/failed-task-91cadd05-response.json @@ -0,0 +1,32 @@ +{ + "status": "fail", + "query": "You are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nAttached files:\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n", + "user": null, + "created_at": "2026-07-18T17:37:41.515679Z", + "job_name": "job-futurehouse-data-analysis-crow-high", + "share_status": "private", + "permitted_accessors": { + "users": [], + "organizations": [] + }, + "build_owner": "FutureHouse", + "environment_name": null, + "agent_name": null, + "task_id": "91cadd05-fe5f-4ccb-9528-18987f5cd26c", + "project_id": null, + "agent_state": null, + "environment_frame": null, + "metadata": null, + "deployment_config": null, + "failure_reason": null, + "task": "You are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nAttached files:\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n", + "started_at": "2026-07-18T17:37:43.927205Z", + "crow": "job-futurehouse-data-analysis-crow-high", + "notification_enabled": false, + "notification_type": null, + "continued_trajectory_id": null, + "is_trajectory_owner": true, + "gcloud_operation_name": "sandbox://91cadd05-fe5f-4ccb-9528-18987f5cd26c/sandbox-claim-899e7a23/job-futurehouse-data-analysis-crow-high-agent-warmpool-b5h6z", + "task_summary": "Simulate Six Conference Audience Personas Evaluating Bayesian Optimization For 3D Printed Energy Absorbers", + "id": "91cadd05-fe5f-4ccb-9528-18987f5cd26c" +} \ No newline at end of file diff --git a/presentation/edison-mock-audience/fetch_task.py b/presentation/edison-mock-audience/fetch_task.py new file mode 100644 index 00000000..0add12bb --- /dev/null +++ b/presentation/edison-mock-audience/fetch_task.py @@ -0,0 +1,71 @@ +"""Fetch a completed Edison analysis task and save all artifacts. + +Usage: python fetch_task.py +Requires EDISON_PLATFORM_API_KEY (or EDISON_API_KEY) in the environment. +""" + +import json +import os +import sys +from pathlib import Path + +from edison_client import EdisonClient + +OUTDIR = Path(__file__).resolve().parent + + +def hunt(node): + found = [] + if isinstance(node, dict): + for k, v in node.items(): + if ( + k in ("answer", "formatted_answer", "final_answer") + and isinstance(v, str) + and len(v) > 200 + ): + found.append(v) + found.extend(hunt(v)) + elif isinstance(node, list): + for v in node: + found.extend(hunt(v)) + return found + + +def main() -> None: + task_id = sys.argv[1] + api_key = os.environ.get("EDISON_PLATFORM_API_KEY") or os.environ["EDISON_API_KEY"] + client = EdisonClient(api_key=api_key) + + status = str(client.get_task(task_id).status).lower() + print(f"status: {status}", flush=True) + if not any(s in status for s in ("success", "fail", "cancel", "error")): + sys.exit(3) # still running + + verbose = client.get_task(task_id, verbose=True, history=True) + payload = verbose.model_dump(mode="json") + (OUTDIR / "task-response.json").write_text( + json.dumps(payload, indent=2, default=str) + ) + + candidates = hunt(payload) + if candidates: + answer = max(candidates, key=len) + (OUTDIR / "mock-audience-feedback.md").write_text(answer) + print(f"answer saved ({len(answer)} chars)", flush=True) + else: + print("no answer field found; inspect task-response.json", flush=True) + + try: + files = client.list_files(task_id) + (OUTDIR / "trajectory-files.json").write_text( + json.dumps(files, indent=2, default=str) + ) + print(f"trajectory files: {files}", flush=True) + except Exception as exc: + print(f"list_files failed: {exc}", flush=True) + + print("done", flush=True) + + +if __name__ == "__main__": + main() diff --git a/presentation/edison-mock-audience/mock-audience-feedback.md b/presentation/edison-mock-audience/mock-audience-feedback.md new file mode 100644 index 00000000..f0604c88 --- /dev/null +++ b/presentation/edison-mock-audience/mock-audience-feedback.md @@ -0,0 +1,388 @@ +# Mock audience report: IDETC-CIE 2026 DAC-10 talk + +## Scope of this review + +This evaluates the talk implied by the outline, not finished slides or delivery technique. The experimental campaign is still in progress, so I do **not** infer any favorable results. The empty “Evidence / results” section is treated as a major unresolved part of the talk: the outline currently explains why the workflow should be useful, but does not yet demonstrate that it is useful. + +The central communication test is whether each listener leaves with the intended message: + +> By closing the loop between multi-material 3D printing and Bayesian optimization, we can optimize tensegrity energy absorbers directly from real impact data, in dozens of prints rather than thousands. + +--- + +# P1. The skeptical Bayesian-optimization insider + +## (a) First-person reaction + +The application is potentially interesting, and the design–print–test loop is easy to understand. The mixed continuous, integer, and categorical design space, physical failures, parallel fabrication, and multiple constrained objectives could make this a worthwhile design-automation case study. + +But the outline keeps telling me that Bayesian optimization is sample-efficient without showing that **this implementation** is sample-efficient. “Dozens, not thousands” sounds like a slogan unless I see the actual experiment count, initialization size, batch size, replication policy, convergence behavior, and a comparison against credible alternatives. q-noisy expected hypervolume improvement, or qNEHVI, is established machinery. Merely deploying it does not establish methodological novelty. + +I become skeptical at the statement that qNEHVI was selected because of heteroscedastic TPU and print noise. qNEHVI accounts for noisy observations, but the outline does not explain how the surrogate models heteroscedasticity, whether repeated specimens identify noise, or whether noise is being conflated with unmodeled process drift. Independent Gaussian processes with an unspecified observation model do not automatically resolve batch-dependent heteroscedasticity. + +My attention drifts during the broad argument against simulation because it delays the information I came to DAC-10 to hear: the design variables, model formulation, constraint handling, acquisition implementation, budget, and benchmark. The Super Ball Bot hook is visually effective, but planetary landing may feel like application theater if the actual specimens and impact regime are far removed from that use case. + +The strongest possible section is the results slot. In its current empty state, the talk has a method narrative but no validation. + +## (b) What I would repeat the next day + +> “They used standard batch noisy multi-objective Bayesian optimization to choose which multi-material printed impact specimens to test, instead of running a full physical sweep.” + +**Distortion from the intended message:** I would omit the claim that the structures were optimized in dozens of prints because I have not seen evidence for it. I would also describe qNEHVI as standard rather than as the contribution. + +## (c) Top three Q&A questions + +1. **What is the actual experimental budget?** How many initial designs, adaptive iterations, specimens per batch, and physical replicates were used, and what stopping rule defined success? +2. **What baselines did you beat?** Did you compare qNEHVI with Sobol or Latin-hypercube sampling, random search, scalarized Bayesian optimization, or a domain-informed sequential design under the same number of physical tests? +3. **How exactly is noise modeled?** Are replicate-dependent variances supplied to the Gaussian processes, is a heteroscedastic likelihood fitted, and how are TPU lot, print batch, machine drift, and specimen-level defects distinguished? + +## (d) Most pointed challenge + +> “What did you contribute beyond applying off-the-shelf qNEHVI to a new specimen family?” + +**Damage if unanswered: severe.** For a DAC review audience, this can reduce the work from a design-automation contribution to an application demonstration. A strong answer could rest on the experimental formulation, mixed-variable and failure-aware implementation, quantitative sample-efficiency evidence, and transferable operating lessons. Those contributions must be stated explicitly and tested. + +--- + +# P2. The aerospace entry, descent, and landing practitioner + +## (a) First-person reaction + +The opening lands immediately. I recognize the Super Ball Bot concept, and “the lander is the energy absorber” is a useful framing. The three performance quantities also sound relevant: energy absorbed per mass, usable stroke, and force transmitted to the payload. + +Then the talk moves into language I do not naturally use: Gaussian-process surrogate, qNEHVI, acquisition function, hypervolume, exploration versus exploitation, and Pareto front. I can accept that the algorithm chooses the next tests, but I need a plain explanation of what information it uses and why I should trust the recommendations. If the presenter spends several minutes naming algorithms without connecting them to engineering decisions, I will lose the thread. + +My main concern is application fidelity. A desktop drop-weight test at one fixed impact energy is not automatically evidence for entry, descent, and landing hardware. I need specimen dimensions, impact mass, velocity, energy, strain rate, boundary conditions, number of impacts, rebound behavior, temperature, vacuum sensitivity, and failure containment. PLA and TPU raise immediate environmental and qualification questions. I also want to know whether the absorber is reusable or sacrificial. + +My attention returns when I see force–time traces, high-speed footage, and damaged specimens. It drifts if the talk stays on the optimizer rather than connecting each output to payload protection. + +## (b) What I would repeat the next day + +> “They have an algorithm that learns from successive drop tests and tells them which 3D-printed crush structure to make next, so they can find a lightweight low-force design with fewer prototypes.” + +**Distortion from the intended message:** I would probably call the structures “crush structures,” losing the tensegrity distinction. I might also overgeneralize the method to planetary landers even though the current tests appear to be laboratory-scale. + +## (c) Top three Q&A questions + +1. What impact mass, velocity, energy, and strain-rate regime do the tests cover, and how do those values compare with a credible lander load case? +2. How do the selected designs scale with size and impact energy? Are the controlling mechanisms material-rate effects, geometric buckling, contact, or all three? +3. What happens under off-axis impact, repeated impacts, temperature extremes, aging, vacuum, and manufacturing defects? + +## (d) Most pointed challenge + +> “Why should I believe that a PLA–TPU specimen optimized at one laboratory impact condition tells me anything about flight-scale landing protection?” + +**Damage if unanswered: severe for the planetary-landing framing, moderate for the underlying optimization study.** The presenter should clearly label planetary landing as motivation, not demonstrated readiness, and state the present technology scope. Otherwise the opening and closing overpromise relative to the reported experiment. + +--- + +# P3. The additive-manufacturing and materials researcher + +## (a) First-person reaction + +The monolithic PLA–TPU architecture is the part I care about. The core-wrapping strategy sounds more credible than simply depositing TPU against exposed PLA and assuming durable adhesion. Holding print settings fixed within a batch, weighing specimens, checking dimensions, and periodically testing a control are all sensible. + +I still need much more fabrication detail before I accept that design geometry is the main source of performance variation. PLA–TPU interfaces are sensitive to temperature, surface contact, moisture, residence time, toolpath order, contamination, and local geometry. “Print defects” is too broad. I want failure-mode evidence showing whether energy is absorbed through intended tension-network deformation, TPU hysteresis, strut buckling, interface peeling, or accidental damage. + +The phrase “no assembly” catches my attention, but “eliminates assembly entirely” may be too strong. Does the build require support removal, trimming, conditioning, fixture insertion, post-tensioning, or manual alignment? If so, the accurate claim is “single-build co-fabrication” or “no joining of PLA and TPU members,” not zero assembly. + +I am a Bayesian-optimization novice. I can follow the loop if the presenter says, “The model proposes the next small batch where improvement is plausible or uncertainty is high.” I will disengage if the slide foregrounds qNEHVI notation rather than the physical decisions it enables. + +## (b) What I would repeat the next day + +> “They co-print rigid PLA and flexible TPU energy absorbers, test them, and use the results to choose the next geometries instead of printing a large design-of-experiments grid.” + +**Distortion from the intended message:** I would remember the fabrication loop more strongly than tensegrity or Bayesian optimization. I might interpret the improvement as mostly a material-interface result unless the structural mechanisms are shown. + +## (c) Top three Q&A questions + +1. How was PLA–TPU bonding quantified, and where did failed specimens actually fail: within TPU, within PLA, or at the interface? +2. How many nominally identical replicates were printed and tested, and how large were within-batch, between-batch, and printer-drift effects relative to design effects? +3. What manual operations occur after printing, and what exactly does “no assembly” exclude? + +## (d) Most pointed challenge + +> “How do you know the optimizer is learning architecture rather than uncontrolled interface quality and process drift?” + +**Damage if unanswered: severe.** If fabrication variation is not measured and separated from design effects, both the surrogate and the claimed Pareto front may be unstable. Replicates, blocking or covariates, control specimens, dimensional measurements, and failure-mode documentation are needed. + +--- + +# P4. The first-year graduate student + +## (a) First-person reaction + +The opening video and the phrase “the lander is the energy absorber” give me a concrete picture. I understand the basic problem: too many designs to print, and computer models may miss messy material behavior. I also understand the loop when it is described as “design, print, drop, learn, repeat.” That story carries me. + +I start getting lost when several unfamiliar terms arrive together: tensegrity-inspired, compaction efficiency, Gaussian process, surrogate, acquisition function, qNEHVI, heteroscedastic noise, Pareto front, probabilistic feasibility constraint, and exploration versus exploitation. The preview says the audience already believes optimization works, but I still need one intuitive sentence explaining what Bayesian optimization does. Skipping “Bayesian optimization 101” should not mean skipping the conceptual bridge. + +I am also unsure how the structure works. Are the TPU parts stretched before impact? Are the PLA struts touching? What makes this “tensegrity-inspired” rather than a multi-material lattice? A labeled specimen image and a short deformation sequence would help much more than a verbal definition. + +My attention drifts during the repeated case against simulation because I accepted that point the first time. It returns for printing footage, the drop test, and a clear plot with a marked “better” direction. + +## (b) What I would repeat the next day + +> “They let an artificial-intelligence program learn from 3D-printed drop tests and keep suggesting better shock absorbers, so they do not have to test every design.” + +**Distortion from the intended message:** I would probably call Bayesian optimization “AI,” omit the two-objective trade-off and force constraint, and assume each new design is simply better than the last. I might also miss what tensegrity contributes. + +## (c) Top three Q&A questions + +1. What makes one of these specimens a tensegrity structure rather than an ordinary 3D-printed lattice? +2. How does the computer decide which design to print next, in plain language? +3. Can one design maximize both energy absorption and compaction efficiency while also lowering peak force, or must those goals trade off? + +## (d) Most pointed challenge + +> “I followed the loop, but I still do not understand what is physically special about the structure or what the optimizer actually learned.” + +**Damage if unanswered: moderate.** Experts can fill in some gaps, but a talk that leaves novices with only “AI plus 3D printing” has failed to communicate its engineering mechanism. One specimen-mechanics visual and one plain-language optimization visual would largely solve this. + +--- + +# P5. The finite-element-analysis veteran + +## (a) First-person reaction + +The statement “simulation can’t be trusted for these structures” puts me on the defensive before I have seen any evidence. Interfacial slip, viscoelasticity, large deformation, buckling, contact, strain-rate dependence, and manufacturing variability are difficult, but they are not intrinsically beyond finite-element analysis. Models can be calibrated, uncertainty can be propagated, and simulation can still screen designs even if it is imperfect. + +The abstract is more defensible than the talk outline. It says the workflow avoids **direct dependence on calibrated finite-element simulation for objective evaluation** and acknowledges future analytical or multifidelity shortcuts. The presentation should use that language. The current outline turns a project-scope choice into a universal technical claim. + +I accept the economic argument that calibrating a high-fidelity model may not be worthwhile for an early design campaign. That is a stronger position: measured tests provide authoritative objective values, while the study asks whether sequential design can use a small physical budget efficiently. I would also welcome evidence that simulation errors change design rankings or miss observed failure modes. Without such evidence, the repeated attack on simulation sounds like a straw man. + +My attention drifts when the talk says simulation is unreliable several times without showing a simulation–experiment discrepancy. It returns when the presenter discusses physical failure modes and admits where lower-fidelity physics could later improve the loop. + +## (b) What I would repeat the next day + +> “They skipped finite-element modeling and used Bayesian optimization directly on physical tests because they judged model calibration too expensive for these multi-material specimens.” + +**Distortion from the intended message:** I would frame the work as bypassing simulation rather than as enabling rapid experimental optimization. If the presenter is combative about simulation, that dispute may become the only thing I remember. + +## (c) Top three Q&A questions + +1. What evidence shows that a calibrated finite-element model is insufficient for design ranking, rather than merely costly to construct? +2. Did you compare the physical-test-only workflow with a simple mechanics model or multifidelity surrogate that could reduce the experimental budget further? +3. Which observed behaviors dominate the model discrepancy: interface failure, TPU viscoelasticity, contact, buckling imperfections, or geometry errors from printing? + +## (d) Most pointed challenge + +> “You have shown that simulation is inconvenient, not that it cannot be trusted. Why is abandoning it scientifically preferable to calibrating and validating it?” + +**Damage if unanswered: severe and avoidable.** The current absolute wording invites a technical fight that is unnecessary to the contribution. Reframing the claim as a cost-and-dependence decision would preserve the motivation without asserting that finite-element modeling is incapable. + +--- + +# P6. The friendly industry generalist + +## (a) First-person reaction + +The loop is memorable: print a candidate, test it, update the model, and print the next informative candidate. I can imagine using that pattern for brackets, seals, lattice pads, or process settings when simulation is weak and tests are expensive. “Dozens rather than thousands” is exactly the kind of value proposition I remember. + +I do not need the mathematical details of qNEHVI, but I need an operational recipe. What software is used? How many initial samples are required? How automated is the handoff from optimizer to computer-aided design and slicing? How long does one cycle take? What expertise does a team need? The abstract says candidate selection is automated but slicing, specimen handling, and testing remain manual. That qualification should appear in the talk before I infer a robotic self-driving laboratory. + +The tensegrity and planetary material is interesting, but my attention will drift if it occupies too much of a 15-minute slot. The method becomes useful to me only when the presenter explicitly generalizes it and defines when it is worth using. + +The ending about moving humankind forward is less memorable than a concrete deployment rule. I would prefer a final slide saying: “Use this loop when tests are authoritative, each test is costly, the design space is mixed, and several performance goals conflict.” + +## (b) What I would repeat the next day + +> “They showed a practical test-driven optimization loop that can find good 3D-printed designs with a few dozen prototypes instead of a giant test matrix.” + +**Distortion from the intended message:** I may overstate automation and assume the “few dozen” claim is established across applications. I will probably forget the names qNEHVI and tensegrity, but retain the workflow. + +## (c) Top three Q&A questions + +1. What parts of the workflow are automated today, and what does an engineer still do manually between recommendation and test result? +2. What minimum experiment budget and software stack would my team need to try this on a different component? +3. When is this approach better than a conventional design of experiments, response-surface method, or a calibrated simulation? + +## (d) Most pointed challenge + +> “What concrete evidence tells me this saved enough prototypes and engineering time to justify the added optimization infrastructure?” + +**Damage if unanswered: moderate to severe.** I will still like the idea, but I will not take it back to my team. A budget-matched baseline and a simple timeline or cost comparison would convert interest into action. + +--- + +# Synthesis + +## Cross-persona themes + +### 1. The causal chain is understandable, but the claimed payoff is not yet demonstrated + +All six personas can understand the basic design–print–test–learn loop. The empty results section prevents them from deciding whether the loop actually found better designs, found them faster, or handled noise reliably. The phrase “dozens, not thousands” is currently an unsupported quantitative claim. + +At minimum, the results need to show: + +- total number of unique designs and physical specimens; +- initial versus adaptively selected designs; +- batch size and number of iterations; +- replicate policy and failure count; +- measured uncertainty or repeatability; +- progress under the actual experimental budget; +- the final feasible Pareto set for **specific energy absorption and compaction efficiency subject to the force cap**; +- a budget-matched nonadaptive or simpler optimization baseline. + +### 2. “Simulation can’t be trusted” is too absolute + +P1 questions the evidence, P2 cares about application validity, and P5 directly rejects the premise. Even P6 needs to know when the loop is preferable to simulation. The abstract already contains better wording: physical measurements are used for objective evaluation “without relying on calibrated finite-element simulation.” + +The defensible claim is not that simulation fails universally. It is that a sufficiently calibrated multiphysics model may be expensive relative to the available design campaign, and the authors therefore test whether physical-data-driven sequential optimization is useful without depending on such a model. + +### 3. The talk needs a two-level explanation of Bayesian optimization + +P1 does not want introductory Bayesian-optimization material; P2, P3, P4, and P5 need a plain conceptual bridge. These needs are compatible. Give the intuition in one sentence and one visual, then put the implementation specifics in a compact technical panel or backup slide. + +Suggested spoken explanation: + +> “After each batch, the model estimates both expected performance and uncertainty across the design space. The acquisition rule selects a small next batch that is most likely to expand the feasible trade-off frontier, while accounting for noisy tests and print failures.” + +This is not “Bayesian optimization 101.” It is the minimum explanation needed to interpret the workflow. + +### 4. The physical mechanism and the word “tensegrity” need clarification + +P2 may reduce the objects to crush structures, P3 may interpret the result as an interface study, and P4 may not distinguish the specimens from ordinary lattices. A labeled as-printed specimen plus a four-frame deformation sequence should show: + +- rigid PLA struts; +- continuous flexible TPU network; +- whether and how pre-tension exists; +- load path before and during impact; +- intended energy-dissipation mechanisms; +- why “tensegrity-inspired” is the precise term. + +### 5. Manufacturing variability is part of the model, not background noise + +P1 and P3 will both press this point. Periodically retesting one control is useful but may not identify design-specific heteroscedasticity, lot effects, or confounding between iteration and material batch. The talk needs to state the replication, randomization, blocking, covariate, and drift-monitoring plan. If those steps were not used, the limitation must be explicit. + +### 6. The application framing outruns the demonstrated scope + +The planetary hook is strong, but P2 will not equate fixed-energy laboratory tests on PLA–TPU specimens with flight hardware. The closing escalates further by claiming that the next lander absorber can be “proven” in weeks. That wording should be narrowed unless the campaign includes relevant scaling and environmental evidence. + +### 7. The objective statement is not fully consistent across the outline + +The task and Point 2 correctly describe two objectives, specific energy absorption and compaction efficiency, with a peak-force constraint. The candidate evidence list instead proposes “SEA vs. peak transmitted force,” and the abstract’s expected-outcomes section similarly describes trade-offs between SEA and peak force. Those are different optimization formulations. + +The talk must consistently distinguish: + +- **objectives:** maximize specific energy absorption and compaction efficiency; +- **constraint:** peak transmitted force must not exceed a specified cap; +- **feasible Pareto front:** trade-offs between the two objectives among designs satisfying that cap. + +If peak force is instead treated as a third objective, the task statement and optimization formulation must be revised accordingly. + +### 8. The closing favors inspiration over technical precision + +P6 is likely to remember “dozens, not thousands,” but P1, P2, and P5 may hear overreach in “proven,” “in weeks,” and “moves humankind forward.” A DAC-10 close should end on the measured engineering result and transfer condition. The Super Ball Bot bookend can remain, but it should not imply flight qualification. + +--- + +# Three highest-priority revisions + +## Priority 1: Build the talk around a quantitative result, not around the availability of a workflow + +Replace the placeholder with a required three-slide evidence sequence: + +1. **“The campaign used N specimens across B adaptive batches under a fixed physical-test budget.”** + Show initialization, adaptive batches, replicates, failures, elapsed cycle time, and the force threshold. Do not use `N` or `B` in the actual talk; insert the observed values. + +2. **“Adaptive selection improved the feasible design set faster than [predeclared baseline].”** + Plot a budget-matched performance measure across physical tests, with uncertainty across repeated runs if available. If only one physical campaign exists, avoid inferential claims that require repeated campaigns and supplement with clearly labeled retrospective resampling or simulation-based algorithm checks. + +3. **“These measured designs form the final feasible trade-off between specific energy absorption and compaction efficiency.”** + Show uncertainty or replicate spread, mark infeasible force-cap violations, and include specimen images or force–time traces for representative Pareto designs. + +If the campaign cannot support those statements, revise the main message from “we can optimize … in dozens” to “we are evaluating whether noisy multi-objective Bayesian optimization can reduce the physical test budget.” + +## Priority 2: Replace the anti-simulation premise with a scoped engineering decision + +Replace: + +> “Simulation can’t be trusted for these structures.” + +with: + +> “For these multi-material prints, obtaining objective values from a validated high-fidelity model would require substantial calibration of interface, rate, contact, and defect behavior. We therefore ask how far a limited budget of direct physical tests can take us without depending on that model.” + +Then title Point 1 with a message rather than a topic: + +> **“When model calibration and exhaustive testing are both costly, each physical specimen must be chosen for information value.”** + +This keeps the pincer structure while avoiding an unnecessary universal claim. + +## Priority 3: Reallocate the 15 minutes around audience decisions + +A concrete timing plan: + +- **1.5 min:** Super Ball Bot hook, specimen, and present study scope. +- **2 min:** Why exhaustive physical search and high-fidelity calibration are costly. +- **2.5 min:** One visual of the closed loop, including a one-sentence Bayesian-optimization explanation. +- **2 min:** Actual design variables, objectives, force constraint, fabrication failures, and noise controls. +- **5 min:** measured results, baseline, uncertainty, representative specimens, and failure modes. +- **1 min:** limitations and transfer conditions. +- **1 min:** measured conclusion and bookend. + +Move kernel choices, log-space acquisitions, detailed encoding, and other implementation material to backup slides unless a result depends directly on them. This follows Doumont’s principle that each slide should communicate one interpreted message rather than expose the speaker’s notes. + +--- + +# Claims needing evidence, hedging, or backup slides + +| Claim or topic | What is needed in the main talk | Prepared backup material | +|---|---|---| +| “Dozens of prints, not thousands” | Actual specimen count, definition of success, and budget-matched comparator | Full campaign ledger; sensitivity to initialization and stopping rule | +| “Simulation can’t be trusted” | Replace with scoped calibration-cost language unless direct validation evidence exists | Simulation–experiment traces, ranking errors, or documented failure modes a tested model missed | +| “qNEHVI handles heteroscedastic noise” | Specify the actual observation-noise model and replication strategy | Likelihood formulation, fixed versus inferred noise, residual diagnostics, replicate variance by design or batch | +| qNEHVI “over standard expected improvement” | Use a technically appropriate comparison; ordinary expected improvement is not a like-for-like multi-objective batch baseline | Comparisons with random/Sobol, scalarization, qEHVI or another relevant noisy/constrained baseline | +| “The Pareto front is the design deliverable” | Show the two stated objectives and force-cap feasibility consistently | Hypervolume reference point, normalization, uncertainty, constraint treatment | +| Peak-force cap | State its numerical value and engineering rationale | Sensitivity of selected designs to alternative cap values | +| Probabilistic print-feasibility constraint | Report failures and demonstrate that the classifier or constraint model is identifiable at the available sample size | Failure labels, calibration, confusion or reliability metrics, acquisition formula | +| “No assembly” / “eliminates assembly entirely” | List any post-print operations; use “single-build co-fabrication” if manual steps remain | Fabrication workflow photographs and labor-time accounting | +| “Candidate goes to tested specimen in hours” | Median or representative print-to-result time and what remains manual | Time breakdown for design, slicing, printing, conditioning, inspection, and testing | +| “Dramatically faster” | Define faster relative to a measured or estimated comparator | Cost and wall-clock accounting with assumptions | +| Transfer to other architectures | Present as a conditional hypothesis, not a demonstrated result | Applicability checklist: test cost, dimensionality, noise, throughput, and constraint structure | +| Planetary-landing relevance | Clearly state that this is motivation unless scale and environment are tested | Impact similitude, energy and velocity regime, environmental gaps, technology-readiness discussion | +| PLA–TPU interface robustness | Failure-mode and repeatability evidence | Microscopy or fracture images, process parameters, conditioning and moisture controls | +| “Tensegrity-inspired” | Show the load path and identify which tensegrity features are retained | Geometric definition, pre-tension status, comparison with a conventional lattice/control | +| Independent Gaussian processes | State why cross-objective correlation is ignored and whether that choice matters | Residual correlations and sensitivity to multi-output alternatives | +| Mixed categorical variables | Briefly state how candidates are represented and optimized | Kernel or encoding details; treatment of invalid combinations | + +--- + +# Fit to the six-person audience + +## Best served: P6, the friendly industry generalist + +The outline has a clear problem–method–application arc, a memorable loop, and an attractive efficiency promise. P6 can understand the proposed value without needing the mathematics. The danger is that this persona may accept unsupported claims and overestimate the system’s automation and maturity. + +P4 also benefits from the story structure, but the unexplained terminology and absent physical-mechanism visual will cause substantial loss of detail. + +## Worst served: P1, the skeptical Bayesian-optimization insider + +P1 is the most likely to judge the current talk as an off-the-shelf acquisition function applied to a new artifact. The outline anticipates this concern by saying not to teach Bayesian optimization, but it does not yet supply the evidence P1 needs: novelty positioning, rigorous baseline, budget, noise identification, constraint diagnostics, and measured sample efficiency. + +P5 is nearly as poorly served because the opening motivation directly challenges finite-element modeling more strongly than the abstract supports. + +## Is that the right trade-off for DAC-10? + +No. A DAC-10 presentation should remain accessible to P2–P6, but it cannot underserve P1. The design-automation expert is likely to shape the technical discussion and the perceived contribution. Accessibility should come from a plain-language loop and strong visuals, not from omitting methodological validation. The best target is a two-layer talk: every listener can follow the engineering decision, while experts can see the experimental budget, comparator, noise model, and constraint formulation. + +--- + +# Predicted reception + +**Current outline: 5/10.** The loop and application are memorable, but without measured results, a budget-matched baseline, and narrower simulation and planetary claims, the talk reads as a polished motivation for an experiment rather than a completed DAC-10 contribution. + +If the results establish repeatability and budgeted improvement, and the three priority revisions are made, the same structure could plausibly move into the **7–8/10** range. That conditional estimate is a judgment about presentation readiness, not a prediction of unobserved experimental outcomes. + +--- + +# Discretionary analytical decisions + +- Treated the unfinished evidence/results section as missing evidence rather than assuming that the planned campaign succeeds. +- Evaluated the artifact as a competent 15-minute delivery from the outline, not as a line edit of the outline or an assessment of slides that do not yet exist. +- Weighted technical credibility and evidentiary support more heavily than visual polish because the target is the DAC-10 design-optimization audience. +- Used the submitted abstract to resolve technical intent when the outline was ambiguous, while flagging inconsistencies rather than silently harmonizing them. +- Interpreted the stated optimization problem as two objectives, specific energy absorption and compaction efficiency, subject to a peak-force constraint; flagged language that instead treats peak force as an objective. +- Assigned qualitative damage levels of moderate, moderate-to-severe, or severe based on how directly an unanswered challenge would undermine each persona’s acceptance of the central claim. +- Chose P6 as the best-served persona because the present outline emphasizes an intuitive workflow and practical efficiency promise; chose P1 as the worst-served because the DAC-specific validation and novelty case is not yet developed. +- Scored predicted reception on the current incomplete outline, not on a hypothetical final talk with successful results. \ No newline at end of file diff --git a/presentation/edison-mock-audience/resubmit_inline.py b/presentation/edison-mock-audience/resubmit_inline.py new file mode 100644 index 00000000..a12a61f0 --- /dev/null +++ b/presentation/edison-mock-audience/resubmit_inline.py @@ -0,0 +1,47 @@ +"""Resubmit the mock-audience analysis with documents inlined in the query. + +Two prior submissions with file uploads failed with no failure_reason and a +null environment_frame (sandbox died before the agent ran), so this variant +embeds the three small documents directly in the query text instead of +attaching them. + +Usage: python resubmit_inline.py +Requires EDISON_PLATFORM_API_KEY (or EDISON_API_KEY) in the environment. +""" + +import os +import re +import sys +from pathlib import Path + +from edison_client import EdisonClient, JobNames +from edison_client.models import TaskRequest + +HERE = Path(__file__).resolve().parent +REPO = HERE.parents[1] +sys.path.insert(0, str(REPO / "presentation")) +from edison_mock_audience_job import QUERY # noqa: E402 + +DOCS = { + "doumont-presentation-template.md": REPO + / "presentation" + / "doumont-presentation-template.md", + "idetc-abstract.tex": REPO / "idetc-abstract.tex", + "doumont-video-notes.md": REPO / "presentation" / "doumont-video-notes.md", +} + +query = re.sub(r"Attached files:", "Documents (inlined below):", QUERY) +parts = [query, "\n\n---\n\nDOCUMENTS\n"] +for name, path in DOCS.items(): + parts.append(f"\n===== BEGIN {name} =====\n") + parts.append(path.read_text()) + parts.append(f"\n===== END {name} =====\n") +full_query = "".join(parts) + +api_key = os.environ.get("EDISON_PLATFORM_API_KEY") or os.environ["EDISON_API_KEY"] +client = EdisonClient(api_key=api_key) +task_data = TaskRequest(name=JobNames.ANALYSIS, query=full_query) +task_ids = client.create_task(task_data) +task_id = task_ids[0] if isinstance(task_ids, (list, tuple)) else task_ids +(HERE / "task-id.txt").write_text(str(task_id) + "\n") +print(f"submitted task {task_id}", flush=True) diff --git a/presentation/edison-mock-audience/slide-deck-draft1-extracted.md b/presentation/edison-mock-audience/slide-deck-draft1-extracted.md new file mode 100644 index 00000000..3550ba13 --- /dev/null +++ b/presentation/edison-mock-audience/slide-deck-draft1-extracted.md @@ -0,0 +1,176 @@ +# IDETC Tensegrity Slides — Draft 1 (extracted content) + +Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes. + + +## Slide 1 — layout: Title Only + +**On-slide text:** +- **Title 1**: Let’s build better tensegrity structures faster +- **TextBox 4**: Marcus Madsen +Undergraduate researcher under Sterling Baird, Ph.D. +- **TextBox 6**: IDETC 2026 + +**Non-text shapes / media / placeholders:** +- [PICTURE (13)] Picture 3 +- [PICTURE (13)] Picture 8 + +**Presenter notes:** +> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly. +> +> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all. + + +## Slide 2 — layout: Title Only + +**On-slide text:** +- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings—and to problems closer to home. + +**Non-text shapes / media / placeholders:** +- [MEDIA (16)] Online Media 5 + +**Presenter notes:** +> Replace this video with a downloaded and trimmed version of just the fall. +> +> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle — thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander is the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit and get back up — it survives multiple drops and keeps working. + + +## Slide 3 (HIDDEN) — layout: Title Only + +**On-slide text:** +- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments) +- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction* +- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress* + +**Presenter notes:** +> This might be better explained purely vocally while on the previous slide. + + +## Slide 4 — layout: Title Only + +**On-slide text:** +- **Title 1**: Currently, tensegrity design is slow and resource intensive. +- **TextBox 3**: Iteration +- **TextBox 5**: Building +- **TextBox 7**: Testing + +**Non-text shapes / media / placeholders:** +- [MEDIA (16)] AdobeStock_207717222 +- [MEDIA (16)] Online Media 2 +- [PICTURE (13)] Picture 14 +- [PICTURE (13)] Picture 16 +- [PICTURE (13)] Picture 18 + +**Presenter notes:** +> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn’t become distracting because it’s fun to watch it spin. +> +> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology. + + +## Slide 5 (HIDDEN) — layout: Title Only + +**On-slide text:** +- **Title 1**: Bayesian Optimization makes physical experimentation affordable. +- **TextBox 14**: Baird et al. Matter (2022) + +**Non-text shapes / media / placeholders:** +- [GROUP (6)] Group 2 + +**Presenter notes:** +> Consult Sterling on this slide. I don’t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it’s well enough known that it’d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is. + + +## Slide 6 (HIDDEN) — layout: Title Only + +**On-slide text:** +- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose. +- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.) + +**Presenter notes:** +> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space. + + +## Slide 7 — layout: Title Only + +**On-slide text:** +- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data. +- **TextBox 5**: 6 variables +- **TextBox 7**: 2 types of input data +- **TextBox 9**: 2 objectives +- **TextBox 17**: *image of blackbox* + +**Non-text shapes / media / placeholders:** +- [LINE (9)] Connector: Curved 11 +- [LINE (9)] Connector: Curved 13 +- [LINE (9)] Connector: Curved 15 + +**Presenter notes:** +> Very rough slide as I’m not entirely sure what graphic or text to put on this, partially because I’m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this + + +## Slide 8 — layout: Title Only + +**On-slide text:** +- **Title 1**: Multi-material additive manufacturing supplies that data fast — single-build co-fabrication, with no joining of parts. + +**Non-text shapes / media / placeholders:** +- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1) +- [PICTURE (13)] Picture 4 + +**Presenter notes:** +> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members. + + +## Slide 9 — layout: Title Only + +**On-slide text:** +- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens. +- **TextBox 3**: *placeholder for looped gif of drop test at 60 in* +- **TextBox 5**: *placeholder for slo motion of drop test* + + +## Slide 10 — layout: Title Only + +**On-slide text:** +- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures: +- **TextBox 3**: Printing +- **TextBox 5**: Testing +- **TextBox 7**: BO feed + +**Non-text shapes / media / placeholders:** +- [PICTURE (13)] Picture 14 +- [PICTURE (13)] Picture 16 +- [PICTURE (13)] Picture 18 +- [PICTURE (13)] Graphic 8 + +**Presenter notes:** +> I think it might be better if we replaced “printing” with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox. + + +## Slide 11 (HIDDEN) — layout: Title Only + +**On-slide text:** +- **Title 1**: Reserved for results + +**Presenter notes:** +> Would be helpful if we could show that this process works in this case. + + +## Slide 12 — layout: Title Only + +**On-slide text:** +- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity. +- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed* +- **TextBox 3**: *image to show lattice structure + + +## Slide 13 — layout: Title Only + +**Non-text shapes / media / placeholders:** +- [PICTURE (13)] Picture 3 + +**Presenter notes:** +> Slide for questions at the end + + +## Slide 14 — layout: Title Slide diff --git a/presentation/edison-mock-audience/slide-deck-mock-audience-feedback.md b/presentation/edison-mock-audience/slide-deck-mock-audience-feedback.md new file mode 100644 index 00000000..bfaf23d9 --- /dev/null +++ b/presentation/edison-mock-audience/slide-deck-mock-audience-feedback.md @@ -0,0 +1,483 @@ +# Mock audience report: IDETC-CIE 2026 slide deck, Draft 1 + +## Executive read + +The visible deck has a clear visual spine: **motivation → slow iteration → Bayesian optimization → multi-material printing → impact testing → closed loop → future applications**. The strongest section is slides 8–10, where fabrication, testing, and adaptive selection finally appear as one workflow. + +But Draft 1 does not yet deliver the technical case promised by the agreed outline. The visible deck omits four things a DAC-10 audience will need: + +1. **Honest scope:** no visible slide says that the PLA–TPU T3 prism is a proxy system rather than flight hardware. +2. **A precise optimization problem:** the six variables, two objectives, force constraint, noise treatment, and fabrication failures are not intelligible on slide 7. +3. **Quantitative evidence:** the sole results slot is hidden and empty, yet slides 10 and 12 already claim acceleration and transfer. +4. **A measured conclusion:** the talk jumps from the workflow to speculative applications without showing a campaign ledger, comparison against a budget-matched baseline, or feasible Pareto front. + +The slide titles generally follow Doumont’s full-sentence-message rule, but several messages outrun the evidence. The deck also departs substantially from the Draft 3 timing plan, which reserves **5 of 15 minutes for results**. As built, results receive zero visible slides. + +--- + +# P0. The program manager in the audience + +## (a) My reaction in the room + +Slide 1 tells me the team wants to work faster, but it does not tell me what is being optimized or what has actually been built. The title sounds aspirational rather than traceable to a project deliverable. I immediately want the formal title from the abstract or a shorter version of it. + +The Super Ball Bot footage on slide 2 is an effective hook. I understand why reusable impact attenuation matters. But the slide creates a scope risk: I am looking at planetary landing hardware while the actual work is a printed PLA–TPU T3 proxy. Because that distinction is not stated immediately, I may spend the rest of the talk evaluating the work against flight-readiness criteria the project never intended to meet. + +Slide 4 gives me the business problem, but not a baseline. “Slow and resource intensive” needs a number: hours per print-test cycle, number of factors, or the candidate count under a declared design of experiments. Without that, I cannot assess whether the proposed loop attacks the dominant schedule driver. + +Slide 7 is where I expect the project definition. Instead, “6 variables,” “2 types of input data,” and “2 objectives” read like inventory labels. I cannot reconstruct the statement of work from them. I need to see what is automated, what remains manual, what the force cap is, and what constitutes a successful campaign. + +Slides 8 and 9 make the project tangible. A labeled specimen and an actual drop test would build confidence. Yet “single-build” and “no joining” need a process qualification caveat, and I need the measurement chain to be explicit. The abstract refers to an instrumented tup and force–time data; slide 9 says accelerometers. Those may be compatible, but the deck presently leaves the relationship unclear. + +Slide 10 is the right management view of the system, but “significantly accelerate” is a result claim placed on a workflow diagram. At this point I ask, “Compared with what, by how much, and over how many specimens?” Then the deck skips the hidden results slide and goes directly to future applications. That is the point where my confidence drops sharply. The project is asking me to extrapolate before closing the evidence loop. + +Slide 12 is too early and too broad. A crutch tip and lattice can be legitimate next phases, but only after the team shows what the present campaign established and what technical risks remain. The blank questions slide gives me no durable take-home message. + +### PM stage-risk register + +| Risk | Likelihood | Consequence | Mitigation | +|---|---:|---:|---| +| Results are incomplete by conference lock | Medium to high | Critical: central claim becomes unsupported | Set a dated campaign freeze; predeclare a fallback message and minimum publishable result package | +| Embedded Super Ball Bot or drop-test video fails | Medium | Moderate: weak opening or broken pacing | Use local, trimmed files; retain a still-frame fallback on every video slide | +| Talk overruns because several videos and explanations are improvised | High | High: results or conclusion gets cut | Script videos to 10–15 seconds total each; rehearse to 13:30; assign 5 minutes to results | +| Scope is mistaken for flight-hardware validation | High | High: credibility damage in Q&A | Put “PLA–TPU T3 proxy; not flight hardware” on slide 2 or the next slide | +| Acceleration claim lacks a budget-matched comparator | High | Critical for DAC review audience | Show Sobol/random or another predeclared baseline at the same physical-test budget | +| Measurement chain is challenged | Medium | High | Add a methods schematic and backup slide on calibration, sampling, filtering, and metric extraction | +| “No joining” or “near autonomous” is read as full automation | Medium | Moderate | List manual steps explicitly: slicing, handling, setup, and testing | +| Animations distract or fail to communicate without narration | Medium | Moderate | Prefer one short clip plus static annotated frames; follow Doumont’s signal-to-noise rule | + +## (b) What I would tell a colleague the next day + +> “They are building a loop that prints PLA–TPU tensegrity-like absorbers, drop-tests them, and lets Bayesian optimization choose the next print.” + +That retains the basic workflow but loses the intended **dozens rather than hundreds** claim because the deck never demonstrates it. I would also probably describe the work as “lander research,” which is a distortion caused by the missing proxy-system statement. + +## (c) My top three Q&A questions + +1. What is the dated plan to complete the campaign, baseline comparison, and repeat testing before the conference slide freeze? +2. Which exact operations are automated today, which remain manual, and what is the measured end-to-end cycle time per adaptive batch? +3. What evidence will justify “dozens rather than hundreds,” and what will you claim if the budget-matched baseline does not show an advantage? + +## (d) My most pointed objection + +**You are presenting a claimed acceleration without showing a completed campaign, a comparator, or even a visible results slide.** + +Unanswered, this is **critical**. I would not fund a scale-up phase from this deck. I might fund completion of the present campaign if the team showed a disciplined schedule, predefined success criteria, and a credible fallback. + +## (e) Slide-deck verdict + +- **Slide most needing work:** **Slide 11.** It must become a real, scheduled results package, preferably three slides: campaign ledger, budget-matched baseline, and feasible Pareto front. +- **Hidden-slide decision:** **Unhide slide 11**, but not in its current empty form. The talk cannot make its principal claim without it. + +### PM slide-level punch list, ordered by schedule criticality + +**Must be fixed first** + +1. **Slide 11/results sequence:** assign owners and dates for data freeze, quality control, baseline computation, replicate analysis, and final plots. Define fallback wording now. +2. **Slide 2:** add the explicit scope line, “This study uses a printed PLA–TPU T3 prism as a proxy for developing the workflow; it is not flight hardware.” +3. **Slide 7:** replace category counts with the actual optimization problem, test budget, batch size, objectives, constraint, and noise/failure treatment. +4. **Slide 10:** remove “significantly” until slide 11 demonstrates a quantitative improvement; show automation boundaries and the loop’s measured cycle time. +5. **Slides 2 and 9:** download and trim media, test on the conference laptop, and put a static fallback image on each slide. + +**Then fix** + +6. **Slide 4:** quantify the current cost or candidate count and identify the bottleneck. +7. **Slide 8:** label PLA, TPU, interface, and any post-processing; qualify “no joining.” +8. **Slide 9:** reconcile accelerometers with the instrumented-tup description and show how force, energy absorption, and compaction efficiency are obtained. +9. **Slide 12:** convert the application collage into a conditional next-phase roadmap with gates. +10. **Slide 13:** end on the measured result and deployment rule, not a blank Q&A screen. + +**Nice to have** + +11. Replace decorative or looping media on slide 4 with one static process image unless it communicates a measured delay. +12. Add backup slides on Bayesian optimization implementation, finite-element-analysis positioning, repeatability, force-cap rationale, and print failures. + +--- + +# P1. The skeptical Bayesian-optimization insider + +## (a) My reaction in the room + +The opening is competent but generic for a design-automation conference. Slides 1–4 set up an expensive physical experiment, which is a valid application class for Bayesian optimization (BO). I become interested when the presenter reaches slide 7, because that is where methodological seriousness should appear. + +Instead, slide 7 says only “6 variables,” “2 types of input data,” and “2 objectives.” I do not learn the variables, their types, how categorical choices are encoded, whether process parameters are optimized, how noisy observations enter the Gaussian processes, how replicates identify noise, how failures are modeled, or how the force cap enters the acquisition. The title says BO handles noisy data, but the visual does not substantiate that message. + +There is also an unresolved specification conflict across the supplied materials. The agreed outline says the study jointly optimizes T3 geometry and FDM processing parameters. The abstract’s design vector lists four continuous geometric variables, one integer, and two categorical variables, apparently **seven variables**, while its fabrication section says print parameters are held fixed within a batch. The slide says **six variables**. A DAC audience will notice if the spoken details do not reconcile these versions. + +Slides 8–10 explain why physical feedback is useful, but they do not establish why qNEHVI is the right algorithm or whether adaptive selection helped. The visible deck never names qNEHVI, the feasible Pareto front, the peak-force constraint, batch size, initialization, or test budget. Hiding slide 5 is reasonable if it is only a generic “what is BO?” slide copied from another talk. But slide 7 must then carry the application-specific methodological content. + +When slide 10 says the workflow “significantly accelerate[s]” optimization, I wait for a performance-versus-budget curve. It never arrives. Slide 12 then generalizes to other applications. My default conclusion is exactly the skeptical one posed in the prompt: this appears to be off-the-shelf qNEHVI attached to an interesting physical apparatus, with no demonstrated algorithmic or empirical advantage yet. + +## (b) What I would tell a colleague the next day + +> “It was an experimental self-driving-lab-style application of batch multi-objective BO to printed impact absorbers, but the talk did not show whether BO beat space-filling sampling.” + +That differs from the intended message because I would not repeat “dozens, not hundreds” without a budget-matched baseline. + +## (c) My top three Q&A questions + +1. What are the exact decision variables and encodings, and why do the deck, scope statement, and abstract appear to disagree on whether there are six or seven variables and whether FDM parameters are optimized? +2. How is observation noise estimated: fixed noise, learned heteroscedastic noise, technical replicates, or repeated control specimens? What is the replicate allocation? +3. Against what budget-matched baseline do you evaluate qNEHVI, and what metric do you report: feasible hypervolume, best feasible objective values, or attainment probability versus physical tests? + +## (d) My most pointed objection + +**Nothing visible distinguishes a validated BO contribution from an untested application of standard qNEHVI.** + +Unanswered, this is **critical for DAC-10 reception**. Application novelty can be enough, but only if the physical campaign is rigorous and the adaptive method is compared fairly under the same test budget. + +## (e) Slide-deck verdict + +- **Slide most needing work:** **Slide 7.** Replace the black-box inventory with a visual of posterior mean plus uncertainty → constrained batch acquisition → next physical tests, accompanied by the exact objectives and force constraint. +- **Hidden-slide decision:** **Keep slide 5 hidden** as a standalone generic BO tutorial. Merge one plain-language BO sentence into slide 7 and put qNEHVI details in backup. + +--- + +# P2. The aerospace practitioner + +## (a) My reaction in the room + +The Super Ball Bot hook works because I know the lineage. Reusable landing structures are interesting. But the title’s claim that tensegrity provides “robust, reusable solutions” is broader than what this deck demonstrates. A lab drop of a polymer proxy is not evidence of robustness in thermal extremes, vacuum, dust, radiation, repeated impacts, or off-axis terrain contact. + +The deck never immediately tells me that the actual specimen is **tensegrity-inspired**, lacks ideal compression-only and tension-only membership, and is not intended to fly. That makes slide 2 vulnerable to looking like borrowed aerospace relevance. + +Slide 4 is plausible but too broad. Tensegrity design can be difficult, but aerospace development is not made slow only by design-space search. Verification, environmental qualification, uncertainty, packaging, guidance, and system integration matter. A faster specimen loop is useful, but it is one subsystem tool. + +I am not helped much by slide 7. I need BO translated into engineering behavior: it chooses the next small batch based on predicted performance and uncertainty while rejecting designs expected to violate a payload-force limit. The slide should name that limit and show the actual trade-off. + +Slides 8 and 9 are the first ones that answer “what did you build and how did you hit it?” A labeled specimen, deformation sequence, drop mass, speed or energy, orientation, and force measurement would hold my attention. Two simultaneous looping videos may not. I also want to know whether the structure rebounds, survives repeated drops, and retains its response, because the opening emphasizes reuse. + +Slide 10 is understandable. Slide 12 loses me because it jumps from a T3 polymer coupon to lattices and crutch tips without stating transfer conditions or technology gates. + +## (b) What I would tell a colleague the next day + +> “They use an optimizer to choose which 3D-printed soft–rigid impact absorber to drop-test next, with the eventual motivation of reusable landing structures.” + +That is fairly close, but I would deliberately downgrade “tensegrity” to “tensegrity-inspired absorber” and treat planetary landing as motivation only. + +## (c) My top three Q&A questions + +1. What makes the printed T3 specimen mechanically tensegrity-inspired rather than an ordinary compliant crush structure, and is it actually prestressed? +2. What impact energy, mass, orientation, and transmitted-force cap are used, and how do they relate to a payload-protection requirement? +3. How does performance change over repeated impacts, and what failure modes or permanent set limit the claimed reusability? + +## (d) My most pointed objection + +**The opening borrows flight relevance and reusability, but the visible deck never establishes the proxy boundary or shows repeated-impact evidence.** + +Unanswered, this is **highly damaging** to credibility with aerospace practitioners, though it does not invalidate the optimization workflow itself. + +## (e) Slide-deck verdict + +- **Slide most needing work:** **Slide 2.** Keep the hook, but place a photograph of the actual T3 specimen beside the Super Ball Bot and state “motivation, not demonstrated flight readiness.” +- **Hidden-slide decision:** **Keep slide 3 hidden.** A generic elbow-friction GIF would distract and oversimplify. Merge a precise tensegrity-mechanism annotation into slide 8 instead. + +--- + +# P3. The additive-manufacturing/materials researcher + +## (a) My reaction in the room + +Slides 1–4 give me a recognizable motivation for rapid experimental iteration. My interest rises on slide 8 because multi-material fused deposition modeling is where the claimed loop can succeed or fail. + +The message “single-build co-fabrication, with no joining of parts” is attractive, but it needs exact boundaries. Is there support removal, trimming, conditioning, manual tensioning, or fixture insertion? Does “single build” mean one tool-change sequence on one machine, or truly co-deposited interfaces throughout the architecture? Are PLA and thermoplastic polyurethane (TPU) mechanically interlocked, chemically bonded, or merely in contact? + +The abstract provides more useful information than the slide: a core-wrapping architecture is intended to resist delamination, specimens are weighed and dimensionally inspected, and a control is retested to monitor drift. Those are exactly the details the talk should surface. Slide 8’s planned printing video will not replace a labeled cross-section or interface image. + +Slide 9 raises repeatability questions. TPU is rate-, temperature-, moisture-, and history-dependent. “Limited, noisy data” on slide 7 acknowledges noise but does not show the control plan. I need batch identity, drying/conditioning, build position, print orientation, machine state, interface failures, and replicates. + +The deck also appears inconsistent about process parameters. The agreed scope says FDM processing parameters are optimized with structure, but the abstract says temperature, line width, infill, and retraction are held fixed within a batch. This could mean they vary between batches or are controlled rather than optimized, but the deck must say which. + +I like the physical loop on slide 10. I do not yet accept that multi-material printing “supplies data fast” until cycle time and failure rate are shown. + +## (b) What I would tell a colleague the next day + +> “They co-print PLA and TPU impact specimens and use each mechanical test to guide the next design batch.” + +I would probably omit “no joining” and “dozens rather than hundreds” until I saw interface qualification, failure accounting, and campaign data. + +## (c) My top three Q&A questions + +1. How is the PLA–TPU interface designed and qualified, and what fraction of specimens fail by delamination or print defects? +2. Which material, machine, conditioning, and build-position variables are controlled, measured as covariates, or optimized? +3. How many true replicates and repeated control specimens are used to separate BO improvement from printer drift and TPU batch variation? + +## (d) My most pointed objection + +**The deck treats co-fabrication as an enabling fact without showing interface integrity, repeatability, or the remaining manual processing steps.** + +Unanswered, this is **highly damaging** because print variation is part of both the physical mechanism and the statistical noise model. + +## (e) Slide-deck verdict + +- **Slide most needing work:** **Slide 8.** Replace or supplement the printing clip with an annotated specimen and interface detail: PLA struts, continuous TPU network, deposition/wrapping strategy, prestress status, post-processing, and observed failure modes. +- **Hidden-slide decision:** **Keep slide 3 hidden.** Its proposed generic GIFs do not answer the materials questions. Put the relevant deformation mechanism on slide 8. + +--- + +# P4. The first-year graduate student + +## (a) My reaction in the room + +Slide 1 sounds positive, and slide 2 gives me a memorable reason to care. I understand that the lander survives an impact because the structure itself deforms. But I do not yet know what “tensegrity” means, and the hidden slide 3 would not necessarily fix that: an elbow GIF and a squishing GIF could leave me with an inaccurate friction-versus-elasticity story. + +Slide 4 is easy to follow. The three words “Iteration, Building, Testing” form a simple problem statement, although the images need to make their relationship obvious. + +I get lost on slide 7. Six variables of what? What are the two input-data types? Are the two objectives “absorb energy” and “reduce force,” or something else? I do not know what a black box means in this setting. The title says Bayesian optimization works with noisy data, but I have not been shown how it decides anything. + +Slide 8 helps because I can see printing. Still, I need labels that distinguish the rigid and flexible parts and one sentence explaining why the object is “tensegrity-inspired.” Slide 9 is intuitive if the clips are synchronized and annotated. I would understand even better if the slide drew the metric extraction directly on a force–time or force–displacement trace. + +Slide 10 is the clearest slide in the deck. Printing → testing → optimizer → next print is the idea I will remember. But it arrives after the technical terms rather than before them. I would benefit from seeing a version of this loop before slide 7 and then revisiting it after the methods. + +Slide 12 feels like a new presentation. I have not seen whether the current system worked, so the crutch tip and lattice do not feel earned. Then the talk ends on a blank question slide rather than telling me the answer. + +## (b) What I would tell a colleague the next day + +> “They repeatedly print and drop-test flexible 3D structures, and a machine-learning model decides what to print next.” + +My retelling loses the two objectives, the force constraint, and the “dozens rather than hundreds” quantitative point. I might incorrectly call the method generic machine learning rather than Bayesian optimization. + +## (c) My top three Q&A questions + +1. What exactly is tensegrity, and which parts of your printed specimen are in tension and compression? +2. What are the six things the optimizer can change, and what are the two scores it is trying to improve? +3. How does the optimizer know whether to try something uncertain rather than the design it currently thinks is best? + +## (d) My most pointed objection + +**The deck names the ingredients before giving me a plain-language map of how they work together.** + +Unanswered, this is **moderately damaging**. I retain the loop but lose the actual research question and technical contribution. + +## (e) Slide-deck verdict + +- **Slide most needing work:** **Slide 7.** It needs one concrete candidate, predicted performance plus uncertainty, and the next selected batch, not abstract counts around a black box. +- **Hidden-slide decision:** **Unhide neither slide 3 nor slide 5 as written.** If forced to choose one, **keep slide 5 hidden** and integrate its one-sentence concept into a redesigned slide 7. Generic explanation would add time without restoring the missing optimization details. + +--- + +# P5. The finite-element-analysis veteran + +## (a) My reaction in the room + +The opening is fine until slide 4 implies that physical iteration is the central route around slow design. I start waiting for the argument about simulation. Because slide 6 is hidden, the deck never states the agreed, defensible position: a sufficiently calibrated model may cost more than it saves for this campaign, particularly when interfaces, defects, rate dependence, contact, and millisecond shock transients dominate. + +That omission avoids an overt attack on finite-element analysis (FEA), which is good, but it also creates a logical hole. Why is physical BO the chosen source of objective values? Why not a hybrid strategy, reduced-order model, analytical screening, or multi-fidelity optimization? The deck merely proceeds from “design is slow” to “BO makes experimentation affordable.” + +I object to any suggestion that experimental data are automatically “real” and simulations are not. Measurements also involve sensor bandwidth, filtering, fixture dynamics, uncertainty, and model-based transformations. Slide 9’s “accelerometers gather real data” wording is particularly vulnerable. A force estimate derived from acceleration still rests on calibration and assumptions. + +Slide 10 is a useful experimental workflow. It should be presented as a scoped engineering choice, not a universal replacement for simulation. Slide 12’s transfer claim needs the condition from the outline: use this pattern where tests are authoritative, costly, the design space is mixed, and goals conflict. + +## (b) What I would tell a colleague the next day + +> “They bypass a calibrated finite-element model and optimize directly against impact tests because the printed interfaces and transient response are difficult to model economically.” + +That is close to the intended experiment-first rationale, but I had to infer it; the visible slides do not say it clearly. + +## (c) My top three Q&A questions + +1. What evidence shows that calibrating a useful simulation would cost more than it saves for this campaign, rather than merely being inconvenient? +2. How do you verify the measured peak transmitted force, including sensor bandwidth, fixture dynamics, filtering, and uncertainty? +3. Why exclude analytical or low-fidelity screening from a multi-fidelity BO strategy, especially for infeasible geometries and gross structural trends? + +## (d) My most pointed objection + +**The deck uses experiment-first optimization without stating a scoped cost-and-validity argument or acknowledging where simulation could still add value.** + +Unanswered, this is **highly damaging in Q&A**. It makes the methodology look ideologically anti-simulation rather than economically reasoned. + +## (e) Slide-deck verdict + +- **Slide most needing work:** **Slide 4.** It should become the two-sided decision: exhaustive physical search is too costly, while campaign-grade calibration for the hardest responses is also costly; therefore choose physical tests adaptively. +- **Hidden-slide decision:** **Merge slide 6 into slide 4**, rather than unhide it as written. Keep a fuller evidence-backed simulation/experiment comparison as backup. + +--- + +# P6. The friendly industry generalist + +## (a) My reaction in the room + +The opening video is memorable, and I like the practical goal of shortening build–test iteration. Slide 4 is easy to understand. My attention slips on slide 7 because the counts and black-box symbol do not tell a story. I do not need the name qNEHVI, but I need to know that the software balances trying promising designs with learning from uncertain ones and respects a force limit. + +Slides 8–10 are the strongest run. If the presenter shows one labeled specimen, one clean drop clip, and one loop diagram, I can see how I might use the pattern in another product-development problem. I want a cycle-time number and an indication of what software or human work is required. + +Slide 12 dilutes the message by offering several destinations before proving the present result. The ending is weak. A blank Q&A slide discards the chance to leave the audience with the workflow, the best specimen, and a measured improvement. + +## (b) What I would tell a colleague the next day + +> “Print it, break or drop it, feed the measurements back, and let the optimizer pick the next batch instead of running a huge test matrix.” + +That captures the intended practical pattern, but I would probably overgeneralize it beyond the specific conditions under which it is useful. + +## (c) My top three Q&A questions + +1. How many prints and calendar days did it take to reach a useful design compared with your previous workflow? +2. What parts of this system could another engineering team reuse without building the same drop tower or writing a custom optimizer? +3. What happens when a print fails or a test produces an outlier? + +## (d) My most pointed objection + +**I understand the loop, but the deck never gives me the one number or result that proves it was worth building.** + +Unanswered, this is **moderately to highly damaging**. I would remember the concept but not have a reason to adopt or sponsor it. + +## (e) Slide-deck verdict + +- **Slide most needing work:** **Slide 13.** Replace the blank Q&A screen with the best measured result, the closed-loop diagram, and one deployment rule; retain a small “Questions?” label. +- **Hidden-slide decision:** **Unhide slide 11** once populated. Results are more valuable than another explanatory slide. + +--- + +# Synthesis + +## Cross-persona themes + +1. **The scope boundary is missing.** The Super Ball Bot is a good hook, but every persona risks interpreting a PLA–TPU proxy as a claim about flight hardware. +2. **Slide 7 is the weakest visible technical slide.** It serves neither experts nor novices. Counts such as “6 variables” and “2 objectives” are information, not a message. +3. **The main claim currently lacks evidence.** “Significantly accelerate,” “supplies data fast,” and “dozens, not hundreds” require a campaign ledger and a budget-matched comparison. +4. **The results omission breaks the story.** The deck follows the outline through the workflow, then skips the outline’s largest time block and moves directly to future work. +5. **The physical system needs more definition.** The audience needs a labeled specimen, a clear explanation of “tensegrity-inspired,” interface design, remaining manual steps, and the measurement chain. +6. **Noise must be operational rather than rhetorical.** Saying the data are noisy is insufficient. Replicates, periodic controls, drift, failures, and uncertainty should be shown. +7. **Experiment-first needs a scoped rationale.** The deck should not dismiss FEA, but it should explain why adaptive physical testing is the economical source of objective values for this campaign. +8. **The close is not a conclusion.** Future applications and a blank Q&A slide do not substitute for the measured finding and its conditions of transfer. +9. **Media are a communication and schedule risk.** Short clips can work, but multiple looping videos violate the signal-to-noise principle and create failure modes. +10. **The project specification needs reconciliation.** The supplied materials differ on six versus seven variables, whether FDM process parameters are optimized, and whether peak force is a constraint or part of the Pareto trade-off. + +## Five highest-priority slide-level revisions + +### 1. Slide 11: replace one empty slot with a three-slide quantitative results sequence + +Use full-sentence titles tied to observed data: + +- **11A:** “The campaign used **N** specimens in **B** adaptive batches, including **R** replicates and **F** failed prints.” +- **11B:** “At the same physical-test budget, adaptive selection reached **[measured feasible-performance result]** faster than **[predeclared Sobol/random baseline]**.” +- **11C:** “The measured feasible Pareto front trades specific energy absorption against compaction efficiency while respecting a **[value and unit]** peak-force cap.” + +Show uncertainty, replicate spread, infeasible points, and representative specimen photographs or traces. If the campaign cannot support the baseline claim, change the talk’s main message to: **“We evaluate whether noise-aware multi-objective Bayesian optimization can reduce the physical-test budget.”** + +### 2. Slide 7: state the actual constrained optimization problem + +Replace the current black-box/count design with: + +- the named decision variables and their types; +- a small batch entering the printer/test loop; +- objectives: maximize specific energy absorption and compaction efficiency; +- constraint: peak transmitted force below a stated cap; +- qNEHVI’s role in one plain sentence: it chooses a small next batch likely to expand the feasible trade-off frontier while accounting for uncertainty and noisy tests; +- the declared test budget, initialization size, and batch size. + +Resolve six versus seven variables and whether processing parameters are decision variables before finalizing this slide. + +### 3. Slide 2: put the proxy boundary beside the hook + +Keep the Super Ball Bot clip, but add an image of the actual specimen and the visible sentence: + +> **“Here, a printed PLA–TPU T3 prism is a proxy for developing the workflow, not flight hardware.”** + +Change or qualify “robust, reusable solutions” unless the present campaign includes repeat-impact evidence. + +### 4. Slide 4: turn the vague need into the two-sided engineering decision + +Retitle it: + +> **“Because exhaustive testing and campaign-grade model calibration are both costly, each physical specimen must be chosen for its information value.”** + +Add one quantified physical-search estimate and one scoped calibration challenge. Do not say simulation is untrustworthy. State that FEA and lower-fidelity models remain possible screening or future multi-fidelity tools. + +### 5. Slides 10 and 13: separate the demonstrated loop from the conclusion + +- **Slide 10:** retitle to **“The closed loop turns each measured test into the next small batch of specimens.”** Remove “significantly accelerate” until the results demonstrate it. Mark automated and manual stages. +- **Slide 13:** retain the closed-loop thumbnail, add the best quantitative result, and state the deployment rule: **use the loop when tests are authoritative, each test is costly, the design space is mixed, and performance goals conflict.** Add “Questions?” without clearing the conclusion from the screen. + +## Hidden-slide adjudication + +| Hidden slide | Recommendation | Rationale | +|---|---|---| +| **3: tensegrity usefulness/mechanism** | **Merge into slide 8** | The generic elbow and squishing GIFs add noise and risk an inaccurate mechanism; an annotated actual specimen and deformation sequence are stronger. | +| **5: generic BO explanation** | **Merge into slide 7; keep original as backup or delete** | DAC experts do not need a generic advocacy slide, but novices need one plain-language sentence and a concrete acquisition visual. | +| **6: specimen information value/experiment-first** | **Merge into slide 4; keep evidence-rich version as backup** | The argument is structurally necessary, but the current title is vague and the visual does not support the scoped calibration-versus-campaign-cost decision. | +| **11: results** | **Unhide and expand to three slides** | Results are the evidentiary center of the talk and were allocated five minutes in the agreed outline. An empty or hidden results slot makes the main claim untenable. | + +## Visible-slide claims needing evidence, hedging, or backup + +| Slide | Claim | Required treatment | +|---|---|---| +| **1** | “Build better tensegrity structures faster” | Define “better” with the two objectives and force constraint; support “faster” with the results comparison. | +| **2** | Tensegrity is robust and reusable for planetary landings | Cite the Super Ball Bot source; distinguish concept evidence from this study; provide repeat-impact evidence or hedge reusability. | +| **2** | Parachutes and retrorockets struggle in the stated conditions | Cite an authoritative aerospace source if retained in narration; avoid implying universal inadequacy. | +| **4** | Current tensegrity design is slow and resource intensive | Quantify candidate count, print/test time, hand-assembly burden, or calibration effort. | +| **7** | BO makes experiments affordable with limited, noisy data | Show campaign budget, noise characterization, replicates, baseline, and performance versus tests. Prepare qNEHVI and Gaussian-process details in backup. | +| **7** | Six variables, two input-data types, two objectives | Reconcile with the apparent seven-variable abstract specification and identify every quantity. Clarify “input-data types.” | +| **8** | Multi-material AM supplies data fast | Report print-to-result cycle time and failure rate. | +| **8** | Single-build co-fabrication with no joining | Show interface and post-processing; use “no joining of PLA and TPU members” if other manual operations remain. | +| **9** | Accelerometers gather the relevant impact data | Show sensor locations and the derivation of transmitted force, specific energy absorption, and compaction efficiency. Reconcile this wording with the abstract’s instrumented-tup description. | +| **10** | The loop significantly accelerates optimization | Remove “significantly” unless a defined baseline and uncertainty support it. Statistical significance should not be implied casually. | +| **12** | Workflow will transfer to lattices and other applications | Present as conditional future work with transfer criteria, not an achieved result. Attribute any external crutch-tip image and claim. | + +### Prepared backup slides + +1. Full design-variable table, bounds, encoding, and fixed versus optimized process parameters. +2. qNEHVI implementation, Gaussian-process assumptions, constraint treatment, batch size, and acquisition settings. +3. Noise plan: replicates, periodic control, heteroscedasticity, drift, outlier policy, and uncertainty propagation. +4. Budget-matched baseline definition and evaluation metric. +5. Force-cap rationale and full objective definitions. +6. Sensor chain, calibration, sample rate, filtering, fixture dynamics, and metric extraction. +7. PLA–TPU interface, conditioning, build layout, dimensional/mass deviations, and print-failure modes. +8. Scoped FEA rationale with citations or internal validation; possible analytical or multi-fidelity extensions. +9. Automation map and cycle-time breakdown. +10. Repeated-impact behavior if reusability remains in the opening. + +## Merged, ordered presenter TODO list + +### Must fix before the talk + +1. **Freeze the scientific specification.** Reconcile the number and type of decision variables, whether FDM parameters are optimized or controlled, the two objectives, the force constraint, treatment of print failures, and the measurement chain. Use one formulation across slides, abstract, paper, and Q&A. +2. **Set a results completion plan.** Establish dates and owners for campaign completion, repeat tests, data quality review, baseline analysis, and slide freeze. Predeclare the fallback claim if the baseline does not support “dozens rather than hundreds.” +3. **Build the three-slide results sequence.** Campaign ledger → budget-matched baseline → measured feasible Pareto front. Allocate approximately five minutes, as the agreed outline requires. +4. **Add the proxy statement to slide 2.** State plainly that the PLA–TPU T3 specimen is a workflow proxy and not flight hardware. +5. **Redesign slide 7.** Show the exact constrained multi-objective problem, test budget, small batches, uncertainty, noisy observations, and the role of qNEHVI. +6. **Remove unsupported result language.** Until supported, delete “significantly” from slide 10 and avoid “dozens rather than hundreds” in the title, narration, or close. +7. **Clarify experiment-first positioning.** Merge the information-value argument into slide 4 and prepare an evidence-backed FEA backup slide. +8. **Make the experiment auditable.** On slide 9 or backup, reconcile accelerometers and the instrumented tup; show how all three performance metrics are calculated and how uncertainty is handled. +9. **De-risk every video.** Store local trimmed files, disable distracting loops, test them on presentation hardware, and include static fallback frames. +10. **Rehearse to 13:30–14:00.** Protect the results and conclusion from being cut. Practice once without slides, consistent with Doumont’s preparation advice. + +### Should fix + +11. **Replace slide 8’s generic print emphasis with mechanism and materials evidence.** Label PLA, TPU, interface strategy, prestress status, deformation, post-processing, and failure modes. +12. **Quantify slide 4.** Give at least one campaign-relevant cost, duration, or candidate-count estimate. +13. **Show automation boundaries on slide 10.** Candidate selection and surrogate updating may be automated; slicing, handling, test setup, and testing remain manual according to the abstract. +14. **Convert slide 12 to a gated roadmap.** Separate demonstrated T3-proxy results from future lattices, flight materials, and terrestrial applications. +15. **Turn slide 13 into the conclusion.** Leave the audience with the best result and transfer rule while taking questions. +16. **Prepare technical backup slides.** Prioritize baseline fairness, noise, force-cap rationale, print failures, materials repeatability, and FEA positioning. + +### Polish + +17. Replace the slide 1 title with a specific message, such as **“Physical Bayesian optimization searches multi-material tensegrity absorbers one small test batch at a time.”** +18. Left-align titles and use intentional line breaks; check every slide at six-per-page scale. +19. Use one purposeful visual per slide. Avoid decorative loading or elbow animations. +20. Standardize terminology: “Bayesian optimization,” not “Bays Opt”; “tensegrity-inspired” where appropriate; define specific energy absorption once. +21. Add citations directly beside borrowed footage, images, and quantitative claims. + +## Who the deck serves best and worst + +- **Best served:** **P6, the friendly industry generalist.** The visible sequence is visually intuitive, practical, and easy to reduce to “print → test → learn → repeat.” +- **Worst served:** **P1, the skeptical BO insider.** The current deck omits nearly every item needed to judge a DAC-10 contribution: exact problem formulation, budget, baseline, noise model, constraint handling, and empirical adaptive advantage. + +That is **not the right trade-off for DAC-10**. The deck should remain accessible to P4 and P6, but its technical center must satisfy P1 without turning into a BO lecture. One rigorous optimization slide, three quantitative results slides, and targeted backup material would achieve that balance. + +## Predicted reception + +- **As is: 4/10.** The physical workflow and application are memorable, but the absent scope statement, empty results section, vague BO slide, and unsupported acceleration claim leave the DAC contribution unproven. +- **After the must-fixes: 8/10.** A scoped proxy claim, reconciled methods, fair budget-matched comparison, feasible Pareto front, and measured conclusion would turn the same visual story into a credible design-optimization application talk. + +## Discretionary analytical decisions + +- Evaluated only the visible slide order for the simulated in-room reactions, while using hidden slides and presenter notes to judge revision options. +- Treated planned placeholder visuals as if implemented according to the presenter’s notes, but judged the selected visual concept and associated stage risk rather than absent graphic quality. +- Prioritized the Draft 3 outline over conflicting Draft 1 wording because the prompt identifies it as the agreed story arc, scope framing, results plan, and timing plan. +- Rated objections qualitatively as moderate, high, or critical rather than assigning unsupported numerical probabilities. +- Recommended three results slides rather than preserving one reserved slot because the agreed outline specifies three distinct evidentiary products and allocates five minutes to results. +- Recommended merging hidden slides 3, 5, and 6 rather than simply unhiding them because their necessary ideas can be conveyed with higher signal-to-noise on existing slides. +- Treated the six-versus-seven-variable and optimized-versus-fixed-process-parameter differences as unresolved specification conflicts requiring reconciliation, rather than inferring which document is correct. +- Assessed claims against the supplied deck extraction, Draft 3 outline, Doumont notes, and submitted abstract only; no independent validation of cited literature or unseen experimental data was attempted. \ No newline at end of file diff --git a/presentation/edison-mock-audience/slide-review-task-id.txt b/presentation/edison-mock-audience/slide-review-task-id.txt new file mode 100644 index 00000000..90ab66b3 --- /dev/null +++ b/presentation/edison-mock-audience/slide-review-task-id.txt @@ -0,0 +1 @@ +afade0b1-f49e-4031-a86d-cb5ec4652536 diff --git a/presentation/edison-mock-audience/slide-review-task-response.json b/presentation/edison-mock-audience/slide-review-task-response.json new file mode 100644 index 00000000..200046c4 --- /dev/null +++ b/presentation/edison-mock-audience/slide-review-task-response.json @@ -0,0 +1,9000 @@ +{ + "status": "success", + "query": "You are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "user": null, + "created_at": "2026-07-26T02:29:02.692821Z", + "job_name": "job-futurehouse-data-analysis-crow-high", + "share_status": "private", + "permitted_accessors": { + "users": [], + "organizations": [] + }, + "build_owner": "FutureHouse", + "environment_name": null, + "agent_name": null, + "task_id": "afade0b1-f49e-4031-a86d-cb5ec4652536", + "project_id": null, + "agent_state": [ + { + "id": "27c49dd0-19e8-4c9c-8e7a-caec5c850f7f", + "trajectory_id": "afade0b1-f49e-4031-a86d-cb5ec4652536", + "trajectory_timestep": 2, + "step": "after_transition", + "state": { + "transition": { + "timestep": 2, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ], + "info": null + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Tensegrity Slides \u2014 Draft 1 (extracted content)\n 2\u2192\n 3\u2192Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4\u2192\n 5\u2192\n 6\u2192## Slide 1 \u2014 layout: Title Only\n 7\u2192\n 8\u2192**On-slide text:**\n 9\u2192- **Title 1**: Let\u2019s build better tensegrity structures faster\n 10\u2192- **TextBox 4**: Marcus Madsen\n 11\u2192Undergraduate researcher under Sterling Baird, Ph.D.\n 12\u2192- **TextBox 6**: IDETC 2026\n 13\u2192\n 14\u2192**Non-text shapes / media / placeholders:**\n 15\u2192- [PICTURE (13)] Picture 3\n 16\u2192- [PICTURE (13)] Picture 8\n 17\u2192\n 18\u2192**Presenter notes:**\n 19\u2192> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20\u2192> \n 21\u2192> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22\u2192\n 23\u2192\n 24\u2192## Slide 2 \u2014 layout: Title Only\n 25\u2192\n 26\u2192**On-slide text:**\n 27\u2192- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings\u2014and to problems closer to home.\n 28\u2192\n 29\u2192**Non-text shapes / media / placeholders:**\n 30\u2192- [MEDIA (16)] Online Media 5\n 31\u2192\n 32\u2192**Presenter notes:**\n 33\u2192> Replace this video with a downloaded and trimmed version of just the fall. \n 34\u2192> \n 35\u2192> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle \u2014 thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander\u00a0is\u00a0the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit\u00a0and get back up\u00a0\u2014 it survives multiple drops and keeps working.\n 36\u2192\n 37\u2192\n 38\u2192## Slide 3 (HIDDEN) \u2014 layout: Title Only\n 39\u2192\n 40\u2192**On-slide text:**\n 41\u2192- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42\u2192- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43\u2192- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44\u2192\n 45\u2192**Presenter notes:**\n 46\u2192> This might be better explained purely vocally while on the previous slide.\n 47\u2192\n 48\u2192\n 49\u2192## Slide 4 \u2014 layout: Title Only\n 50\u2192\n 51\u2192**On-slide text:**\n 52\u2192- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53\u2192- **TextBox 3**: Iteration\n 54\u2192- **TextBox 5**: Building\n 55\u2192- **TextBox 7**: Testing\n 56\u2192\n 57\u2192**Non-text shapes / media / placeholders:**\n 58\u2192- [MEDIA (16)] AdobeStock_207717222\n 59\u2192- [MEDIA (16)] Online Media 2\n 60\u2192- [PICTURE (13)] Picture 14\n 61\u2192- [PICTURE (13)] Picture 16\n 62\u2192- [PICTURE (13)] Picture 18\n 63\u2192\n 64\u2192**Presenter notes:**\n 65\u2192> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn\u2019t become distracting because it\u2019s fun to watch it spin.\n 66\u2192> \n 67\u2192> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68\u2192\n 69\u2192\n 70\u2192## Slide 5 (HIDDEN) \u2014 layout: Title Only\n 71\u2192\n 72\u2192**On-slide text:**\n 73\u2192- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74\u2192- **TextBox 14**: Baird et al. Matter (2022)\n 75\u2192\n 76\u2192**Non-text shapes / media / placeholders:**\n 77\u2192- [GROUP (6)] Group 2\n 78\u2192\n 79\u2192**Presenter notes:**\n 80\u2192> Consult Sterling on this slide. I don\u2019t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it\u2019s well enough known that it\u2019d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81\u2192\n 82\u2192\n 83\u2192## Slide 6 (HIDDEN) \u2014 layout: Title Only\n 84\u2192\n 85\u2192**On-slide text:**\n 86\u2192- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87\u2192- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88\u2192\n 89\u2192**Presenter notes:**\n 90\u2192> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91\u2192\n 92\u2192\n 93\u2192## Slide 7 \u2014 layout: Title Only\n 94\u2192\n 95\u2192**On-slide text:**\n 96\u2192- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97\u2192- **TextBox 5**: 6 variables\n 98\u2192- **TextBox 7**: 2 types of input data\n 99\u2192- **TextBox 9**: 2 objectives\n100\u2192- **TextBox 17**: *image of blackbox*\n101\u2192\n102\u2192**Non-text shapes / media / placeholders:**\n103\u2192- [LINE (9)] Connector: Curved 11\n104\u2192- [LINE (9)] Connector: Curved 13\n105\u2192- [LINE (9)] Connector: Curved 15\n106\u2192\n107\u2192**Presenter notes:**\n108\u2192> Very rough slide as I\u2019m not entirely sure what graphic or text to put on this, partially because I\u2019m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109\u2192\n110\u2192\n111\u2192## Slide 8 \u2014 layout: Title Only\n112\u2192\n113\u2192**On-slide text:**\n114\u2192- **Title 1**: Multi-material additive manufacturing supplies that data fast \u2014 single-build co-fabrication, with no joining of parts.\n115\u2192\n116\u2192**Non-text shapes / media / placeholders:**\n117\u2192- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118\u2192- [PICTURE (13)] Picture 4\n119\u2192\n120\u2192**Presenter notes:**\n121\u2192> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122\u2192\n123\u2192\n124\u2192## Slide 9 \u2014 layout: Title Only\n125\u2192\n126\u2192**On-slide text:**\n127\u2192- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128\u2192- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129\u2192- **TextBox 5**: *placeholder for slo motion of drop test*\n130\u2192\n131\u2192\n132\u2192## Slide 10 \u2014 layout: Title Only\n133\u2192\n134\u2192**On-slide text:**\n135\u2192- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136\u2192- **TextBox 3**: Printing\n137\u2192- **TextBox 5**: Testing\n138\u2192- **TextBox 7**: BO feed\n139\u2192\n140\u2192**Non-text shapes / media / placeholders:**\n141\u2192- [PICTURE (13)] Picture 14\n142\u2192- [PICTURE (13)] Picture 16\n143\u2192- [PICTURE (13)] Picture 18\n144\u2192- [PICTURE (13)] Graphic 8\n145\u2192\n146\u2192**Presenter notes:**\n147\u2192> I think it might be better if we replaced \u201cprinting\u201d with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148\u2192\n149\u2192\n150\u2192## Slide 11 (HIDDEN) \u2014 layout: Title Only\n151\u2192\n152\u2192**On-slide text:**\n153\u2192- **Title 1**: Reserved for results\n154\u2192\n155\u2192**Presenter notes:**\n156\u2192> Would be helpful if we could show that this process works in this case.\n157\u2192\n158\u2192\n159\u2192## Slide 12 \u2014 layout: Title Only\n160\u2192\n161\u2192**On-slide text:**\n162\u2192- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163\u2192- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164\u2192- **TextBox 3**: *image to show lattice structure\n165\u2192\n166\u2192\n167\u2192## Slide 13 \u2014 layout: Title Only\n168\u2192\n169\u2192**Non-text shapes / media / placeholders:**\n170\u2192- [PICTURE (13)] Picture 3\n171\u2192\n172\u2192**Presenter notes:**\n173\u2192> Slide for questions at the end\n174\u2192\n175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "info": { + "start_ts": 8147.330900226, + "end_ts": 8147.337548875 + } + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Presentation \u2014 Doumont Presentation Structure Template\n 2\u2192\n 3\u2192Transcribed from Jean-luc Doumont's presentation-structure template\n 4\u2192([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5\u2192adapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\n 6\u2192All rights reserved. Can be downloaded from .\n 7\u2192\n 8\u2192> To design your presentation, write down your ideas for each component below.\n 9\u2192> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10\u2192\n 11\u2192**Draft 3** \u2014 applies the inline review comments from @sgbaird on PR #84 and the\n 12\u2192priority revisions from the\n 13\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14\u2192Summaries of what changed are at the bottom of this file\n 15\u2192([Draft 2 \u2192 3](#changes-from-draft-2), [Draft 1 \u2192 2](#changes-from-draft-1)).\n 16\u2192\n 17\u2192**Scope note (frame everything below with this):** the study presented is the\n 18\u2192optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19\u2192processing parameters** (e.g., nozzle temperature, print speed). PLA\u2013TPU is a\n 20\u2192**proxy system** \u2014 not flight material \u2014 used to prototype a self-driving-lab\n 21\u2192style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22\u2192The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23\u2192beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24\u2192\n 25\u2192## Opening\n 26\u2192\n 27\u2192### Attention getter\n 28\u2192\n 29\u2192*A way to lead the audience to the need efficiently*\n 30\u2192\n 31\u2192Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32\u2192landings \u2014 and to problems closer to home.\n 33\u2192\n 34\u2192One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35\u2192clip) do the work:\n 36\u2192\n 37\u2192> Landing a payload on another planet means surviving an impact in conditions\n 38\u2192> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n 39\u2192> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40\u2192> structure: the lander *is* the energy absorber. And unlike crushable\n 41\u2192> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42\u2192> hit **and get back up** \u2014 it survives multiple drops and keeps working.\n 43\u2192\n 44\u2192Candidate Super Ball Bot videos to show (verified links):\n 45\u2192\n 46\u2192- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n 47\u2192 (official channel); the canonical overview.\n 48\u2192- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49\u2192 \u2014 Wall Street Journal; prototype drop footage.\n 50\u2192- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51\u2192 \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n 52\u2192\n 53\u2192Immediately after the hook, state the present study scope in one sentence\n 54\u2192(per the scope note above): *we study a printed PLA\u2013TPU T3 prism as a proxy\n 55\u2192system for prototyping an autonomous tensegrity-optimization workflow* \u2014 so\n 56\u2192the planetary framing never outruns the demonstrated scope.\n 57\u2192\n 58\u2192\n 60\u2192\n 61\u2192### Need\n 62\u2192\n 63\u2192*A difference between actual and desired situations*\n 64\u2192\n 65\u2192**Actual situation** (a two-sided pincer \u2014 stated as a scoped engineering\n 66\u2192judgment, not an absolute):\n 67\u2192\n 68\u2192- For these structures, obtaining trustworthy objective values from a\n 69\u2192 validated high-fidelity model would require substantial calibration of\n 70\u2192 interface, rate, contact, and defect behavior \u2014 and even heavy-duty\n 71\u2192 simulation falls short of the experiments we are running: peak transmitted\n 72\u2192 shock is a millisecond-scale transient that is notoriously difficult to\n 73\u2192 resolve numerically. This holds even for idealized cable\u2013strut tensegrities\n 74\u2192 in their actual engineering materials (steel or similar), not just for\n 75\u2192 printed PLA\u2013TPU.\n 76\u2192 \n 79\u2192- Exhaustive physical search fails on cost: a traditional design of\n 80\u2192 experiments over this relatively high-dimensional space (geometry + FDM\n 81\u2192 process parameters) would demand hundreds of specimens, each costing print\n 82\u2192 and test time.\n 83\u2192- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84\u2192 more complex structures \u2014 especially tensegrity *lattices* \u2014 become\n 85\u2192 extremely difficult and ultimately infeasible to hand-build.\n 86\u2192\n 87\u2192**Desired situation:**\n 88\u2192\n 89\u2192- Trustworthy performance data without hand assembly (easy creation of\n 90\u2192 tensegrity structures, scalable beyond a single prism).\n 91\u2192- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n 92\u2192 not the hundreds a traditional design of experiments would require.\n 93\u2192\n 94\u2192### Task\n 95\u2192\n 96\u2192*What I decided/was asked to do to address the need*\n 97\u2192\n 98\u2192Build a closed-loop design\u2013print\u2013test workflow that optimizes a **T3-prism\n 99\u2192tensegrity-inspired energy absorber together with its FDM processing\n100\u2192parameters** (e.g., temperature, print speed) directly from real, measured\n101\u2192impact data \u2014 maximizing energy absorbed per gram (specific energy\n102\u2192absorption) and compaction efficiency, subject to a cap on the peak force\n103\u2192transmitted to the payload. Extensions beyond the T3 prism are future work.\n104\u2192\n105\u2192### Main message\n106\u2192\n107\u2192*The one sentence I want my audience to remember*\n108\u2192\n109\u2192By closing the loop between multi-material 3D printing and Bayesian\n110\u2192optimization, we can optimize tensegrity-inspired energy absorbers directly\n111\u2192from real impact data \u2014 in dozens of prints, not the hundreds a traditional\n112\u2192design of experiments would demand.\n113\u2192\n114\u2192\n118\u2192\n119\u2192### Preview\n120\u2192\n121\u2192*A map of the body (ideally three points, max. five)*\n122\u2192\n123\u21921. When model calibration and exhaustive testing are both costly, each\n124\u2192 physical specimen must be chosen for its information value \u2014 so the\n125\u2192 measured experiment is our source of truth.\n126\u21922. Bayesian optimization makes physical experimentation affordable: it handles\n127\u2192 noisy measurements, small batches, and the constrained multi-objective\n128\u2192 trade-off \u2014 and Honegumi let us scaffold it in minutes.\n129\u21923. Multi-material additive manufacturing closes the loop: single-build PLA\u2013TPU\n130\u2192 co-fabrication turns each proposed design into test data in hours, with no\n131\u2192 joining of parts.\n132\u21924. *(Optional fourth point, if time allows)* The same closed loop points at\n133\u2192 what comes next: tensegrity lattices, flight-relevant materials, and\n134\u2192 safety hardware on Earth.\n135\u2192\n136\u2192## Body\n137\u2192\n138\u2192### Point 1\n139\u2192\n140\u2192When model calibration and exhaustive testing are both costly, each physical\n141\u2192specimen must be chosen for its information value.\n142\u2192\n143\u2192- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144\u2192 these structures, a sufficiently calibrated multiphysics model is expensive\n145\u2192 to build relative to the design campaign itself \u2014 and the hardest quantity,\n146\u2192 peak transmitted shock, is a millisecond-scale transient that even\n147\u2192 heavy-duty simulation struggles to resolve. The gap persists in the actual\n148\u2192 engineering materials a real lander would use (steel struts and cables),\n149\u2192 so this is not merely an artifact of printed PLA\u2013TPU.\n150\u2192- The alternative \u2014 sweeping the design space physically \u2014 fails on cost: a\n151\u2192 traditional design of experiments over geometry plus FDM process parameters\n152\u2192 would take hundreds of specimens, each needing print + test time.\n153\u2192- Therefore: treat the physical experiment as the authoritative source of\n154\u2192 objective values, and be ruthless about *which* experiments to run.\n155\u2192- Anticipate the FEA-veteran pushback (\"inconvenient \u2260 untrustworthy\"): frame\n156\u2192 it as a cost-and-dependence decision, and keep a backup slide with the\n157\u2192 literature (or our own validation) on simulation\u2013experiment discrepancy for\n158\u2192 impact transients.\n159\u2192\n160\u2192### Transition\n161\u2192\n162\u2192If every data point must be a real experiment, we need a method that extracts\n163\u2192the most from every specimen \u2014 we need to make the process radically more\n164\u2192sample-efficient.\n165\u2192\n166\u2192### Point 2\n167\u2192\n168\u2192Bayesian optimization makes physical experimentation affordable, even with\n169\u2192limited, noisy data.\n170\u2192\n171\u2192- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172\u2192 visual in the main talk; implementation detail goes to backup slides.\n173\u2192 Spoken version:\n174\u2192 > \"After each batch, the model estimates both expected performance and\n175\u2192 > uncertainty across the design space. The acquisition rule selects a small\n176\u2192 > next batch that is most likely to expand the feasible trade-off frontier,\n177\u2192 > while accounting for noisy tests and print failures.\"\n178\u2192- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179\u2192 motivate a noise-aware acquisition function (qNEHVI) rather than\n180\u2192 non-noise-aware alternatives. (Backup slide: how observation noise is\n181\u2192 actually modeled \u2014 replicates, noise identification \u2014 and budget-matched\n182\u2192 baselines such as Sobol/random sampling.)\n183\u2192- Small parallel batches: the surrogate proposes the next batch of specimens\n184\u2192 to print, balancing exploration and exploitation.\n185\u2192- Constrained multi-objective by construction: **objectives** = maximize\n186\u2192 specific energy absorption and compaction efficiency; **constraint** = peak\n187\u2192 transmitted force must stay under a cap; the deliverable is the *feasible*\n188\u2192 Pareto front between the two objectives among force-cap-satisfying designs.\n189\u2192 (Keep this formulation consistent everywhere \u2014 do not present \"SEA vs. peak\n190\u2192 force\" as the trade-off.)\n191\u2192- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192\u2192 this advanced task (noisy, batched, constrained, multi-objective) in\n193\u2192 minutes using [Honegumi](https://honegumi.readthedocs.io/) \u2014 the audience\n194\u2192 can do the same for their own problems; show the code-template picker\n195\u2192 briefly so they see how low the barrier is.\n196\u2192\n197\u2192### Transition\n198\u2192\n199\u2192The optimizer is no longer the bottleneck \u2014 the limit is now providing real\n200\u2192data quickly enough.\n201\u2192\n202\u2192### Point 3\n203\u2192\n204\u2192Multi-material additive manufacturing supplies that data fast \u2014 single-build\n205\u2192co-fabrication, with no joining of parts.\n206\u2192\n207\u2192- Single-build PLA\u2013TPU co-fabrication: rigid struts and flexible tension\n208\u2192 network in one print \u2014 no joining or hand assembly of members, which closes\n209\u2192 the \"easy creation\" gap from the Need and is what makes future lattices\n210\u2192 feasible at all. (Be precise: if support removal, trimming, or conditioning\n211\u2192 remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212\u2192 assembly.\")\n213\u2192- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214\u2192 as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215\u2192 pre-tension status) plus a short deformation sequence \u2014 so the EDL engineer\n216\u2192 doesn't file it as a \"crush structure\" and the grad student can tell it from\n217\u2192 an ordinary lattice.\n218\u2192- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219\u2192 tested specimen (quasi-static compression + instrumented drop-weight impact)\n220\u2192 in hours. (Backup slide: representative print-to-result time breakdown and\n221\u2192 which steps remain manual \u2014 candidate selection is automated; slicing,\n222\u2192 handling, and testing are not yet.)\n223\u2192- Now we have the data we need to complete the loop.\n224\u2192\n225\u2192### Point 4 (optional \u2014 future applications)\n226\u2192\n227\u2192*Include if timing allows; otherwise fold into the Conclusion.*\n228\u2192\n229\u2192The same closed loop transfers wherever tests are authoritative, each test is\n230\u2192costly, the design space is mixed, and several performance goals conflict:\n231\u2192\n232\u2192- Tensegrity *lattices* and more complex architectures \u2014 exactly where hand\n233\u2192 assembly becomes infeasible and single-build co-fabrication pays off.\n234\u2192- Migration from the PLA\u2013TPU proxy toward flight-relevant materials and\n235\u2192 impact regimes.\n236\u2192- Safety hardware on Earth (e.g., the tensegrity crutch tip \u2014 future work).\n237\u2192- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238\u2192 handling, testing) in the loop.\n239\u2192\n240\u2192### Evidence / results\n241\u2192\n242\u2192*What we found \u2014 ground the conclusion in shown data*\n243\u2192\n244\u2192Build the talk around a quantitative three-slide sequence (per the\n245\u2192mock-audience report; insert observed values once the campaign completes):\n246\u2192\n247\u21921. **Campaign ledger** \u2014 \"The campaign used N specimens across B adaptive\n248\u2192 batches under a fixed physical-test budget\": initialization vs. adaptive\n249\u2192 batches, replicates, print failures, cycle time, and the numerical value\n250\u2192 (and rationale) of the force cap.\n251\u21922. **Budget-matched baseline** \u2014 \"Adaptive selection improved the feasible\n252\u2192 design set faster than [predeclared baseline]\": performance vs. number of\n253\u2192 physical tests against Sobol/random sampling at the same budget, with\n254\u2192 uncertainty where available.\n255\u21923. **Measured feasible Pareto front** \u2014 specific energy absorption vs.\n256\u2192 compaction efficiency among designs satisfying the force cap, with\n257\u2192 replicate spread, marked infeasible designs, and specimen photos or\n258\u2192 force\u2013time traces for representative Pareto designs.\n259\u2192\n260\u2192Supporting raw-data candidates: drop-test force\u2013time traces, Sobol /\n261\u2192first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262\u2192\n263\u2192### Suggested 15-minute allocation\n264\u2192\n265\u2192*(From the mock-audience report \u2014 results get the largest block.)*\n266\u2192\n267\u2192| Time | Content |\n268\u2192|---|---|\n269\u2192| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270\u2192| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271\u2192| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272\u2192| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273\u2192| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274\u2192| 1 min | Limitations and transfer conditions |\n275\u2192| 1 min | Measured conclusion + bookend |\n276\u2192\n277\u2192Move kernel choices, encodings, and other implementation detail to backup\n278\u2192slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279\u2192[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280\u2192\n281\u2192## Closing\n282\u2192\n283\u2192### Review\n284\u2192\n285\u2192*A recap of the body, leading into the conclusion*\n286\u2192\n287\u2192Using real, measured impact data from additively manufactured\n288\u2192tensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\n289\u2192optimization routine \u2014 we rapidly iterate toward better energy absorbers.\n290\u2192This feedback loop makes tensegrity optimization dramatically faster: dozens\n291\u2192of prints instead of the hundreds a traditional design of experiments over\n292\u2192this design space would require.\n293\u2192\n294\u2192### Conclusion\n295\u2192\n296\u2192*What the above means to the audience in the end*\n297\u2192\n298\u2192Because the loop runs on physical measurements, it is useful precisely where\n299\u2192model calibration is not worth its cost \u2014 and the same closed-loop pattern\n300\u2192should transfer (a conditional claim, pending evidence) to other additively\n301\u2192manufactured architectures whose performance is dominated by\n302\u2192hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303\u2192*use this loop when tests are authoritative, each test is costly, the design\n304\u2192space is mixed, and several performance goals conflict.* For tensegrity\n305\u2192specifically, it opens a path from this T3 proxy study toward lattices,\n306\u2192flight-relevant materials, payload protection on other planets, and safety\n307\u2192hardware here on Earth (e.g., the crutch tip, as future work).\n308\u2192\n309\u2192### Close\n310\u2192\n311\u2192*A way to end the presentation clearly and elegantly*\n312\u2192\n313\u2192Return to the opening image: the next planetary lander's energy absorber \u2014 a\n314\u2192structure that has to survive not one impact but many \u2014 doesn't have to take\n315\u2192years of hand-built prototypes; with this loop, candidate designs can be\n316\u2192designed, printed, and tested against real impacts in weeks instead of months\n317\u2192or longer. End on the measured engineering result and its transfer condition,\n318\u2192then the bookend: every method that compresses design time moves our ventures\n319\u2192\u2014 on this planet and beyond \u2014 forward.\n320\u2192\n321\u2192---\n322\u2192\n323\u2192## Changes from Draft 2\n324\u2192\n325\u2192Applied from @sgbaird's inline review comments on PR #84 and the\n326\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327\u2192\n328\u2192**From the inline review comments:**\n329\u2192\n330\u21921. **Reusability in the hook** (line 22): the tensegrity lander survives\n331\u2192 multiple drops, unlike crushable/sacrificial alternatives.\n332\u21922. **Proxy-system framing** (lines 49, 87): PLA\u2013TPU would never fly; it is a\n333\u2192 proxy for prototyping a self-driving-lab-style workflow \u2014 stated in a\n334\u2192 scope note up front and re-stated right after the hook.\n335\u21923. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336\u2192 heavy-duty simulation falls short of the experiments (millisecond-scale\n337\u2192 peak shock), not that it merely costs too much.\n338\u21924. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339\u2192 complex structures and especially lattices are not.\n340\u21925. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341\u2192 (temperature, print speed, \u2026) only; the rest is explicitly future work.\n342\u21926. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343\u2192 literature search (or validation of experiments against simulation), plus\n344\u2192 the millisecond-resolution peak-shock argument.\n345\u21927. **Optional Point 4 added** (line 97): future applications, with a note it\n346\u2192 can fold into the Conclusion if time is short.\n347\u21928. **Steel-materials point** (line 104): simulations fall short for the\n348\u2192 actual engineering materials (steel struts/cables), not just printed\n349\u2192 PLA\u2013TPU.\n350\u21929. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351\u2192 in minutes, pitched so the audience sees they can use it too.\n352\u219210. **\"Thousands\" \u2192 \"hundreds\"** (line 173): baseline is now a traditional\n353\u2192 design of experiments over a high-dimensional space (hundreds), in the\n354\u2192 main message, Need, Point 1, and Review.\n355\u219211. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356\u2192 or longer\" for candidate designs.\n357\u2192\n358\u2192**From the mock-audience report's priority revisions:**\n359\u2192\n360\u219212. **Quantitative three-slide results sequence** (Priority 1): campaign\n361\u2192 ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front, with a\n362\u2192 fallback main-message wording if the campaign can't yet support the\n363\u2192 efficiency claim.\n364\u219213. **Anti-simulation premise replaced with a scoped cost decision**\n365\u2192 (Priority 2): Point 1 retitled to the information-value message;\n366\u2192 \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367\u2192 anticipated with a backup slide.\n368\u219214. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369\u2192 implementation detail moved to backup slides; Q&A prep pointed at the\n370\u2192 report's claims-vs-evidence table.\n371\u219215. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372\u2192 the main talk, machinery in backup slides; the misleading\n373\u2192 \"qNEHVI over standard expected improvement\" comparison replaced with\n374\u2192 \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375\u219216. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376\u2192 sequence in Point 3, so the structure isn't remembered as a generic crush\n377\u2192 structure or lattice.\n378\u219217. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379\u2192 co-fabrication / no joining of parts,\" with a note to list remaining\n380\u2192 manual steps and an automation-status caveat.\n381\u219218. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382\u2192 compaction efficiency, constraint = force cap, deliverable = *feasible*\n383\u2192 Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384\u219219. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385\u2192 transfer claim marked conditional, and the close ends on the measured\n386\u2192 result before the bookend.\n387\u2192\n388\u2192## Changes from Draft 1\n389\u2192\n390\u2192Feedback from the PR #84 review, applied in Draft 2:\n391\u2192\n392\u21921. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n393\u2192 dozens-not-thousands claim); body and message now carry the same story.\n394\u21922. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n395\u2192 manuscript's positioning (#75/#76) is that the physical experiment is the\n396\u2192 source of truth, explicitly avoiding calibrated FE simulation.\n397\u21923. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n398\u2192 parametric design space and avoid an over-claim to a design-automation\n399\u2192 audience.\n400\u21924. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401\u2192 energy absorption and compaction efficiency, cap peak transmitted force.\n402\u21925. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403\u2192 *and* the space is too big to brute-force \u2014 which makes BO + AM the\n404\u2192 inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405\u2192 above.)*\n406\u21926. **Point 1 given its own content** (the *why* behind the inefficiency)\n407\u2192 instead of restating the Need.\n408\u21927. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409\u2192 the specifics (noise-aware acquisition, small batches, multi-objective).\n410\u21928. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411\u2192 (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412\u2192 above.)*\n413\u21929. **Evidence/results slot added** before the Closing, so the Conclusion is\n414\u2192 grounded in shown data rather than three hedged \"may\"s.\n415\u219210. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n416\u2192 Ball Bot visual; verified video links included.\n417\u219211. **Close bookends the opening** (the next lander's absorber, designed in\n418\u2192 weeks) before the broader humankind line.\n419\u219212. **Spelling**: Doumont (was \"Duomont\").", + "name": "read", + "tool_call_id": "call_93haurOMDGiH2mQFFbaMaYKn", + "info": { + "start_ts": 8147.331065886, + "end_ts": 8147.336467845 + } + }, + { + "role": "tool", + "content": " 1\u2192# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n 2\u2192\n 3\u2192Video: (assigned in #83)\n 4\u2192\n 5\u2192Transcribed from @me-madsen's handwritten notes\n 6\u2192([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7\u2192dated 2026-07-15).\n 8\u2192\n 9\u2192## Overview\n10\u2192\n11\u2192A presentation is all about the right content, displayed the right way, at the\n12\u2192right time.\n13\u2192\n14\u2192- Verbal communication is sequential.\n15\u2192- Message is *interpretation* of the information.\n16\u2192 - Ensure the audience can get the most out of what you're doing.\n17\u2192 - Don't just give them information \u2014 help them understand it, interpret it,\n18\u2192 and internalize it.\n19\u2192\n20\u2192## Doumont's suggested rules for a presentation\n21\u2192\n22\u21920. Ensure you have **messages** for your audience, not just words.\n23\u21921. **Adapt to your audience.**\n24\u2192 - Think like the audience, not the speaker.\n25\u21922. **Maximize signal-to-noise ratio.**\n26\u2192 - Reduce noise (animations, laser pointers, anything that pulls attention\n27\u2192 away from your message).\n28\u21923. **Use effective redundancy.**\n29\u2192 - Tell your message in word *and* on slide: someone deaf should understand\n30\u2192 your messages just as well as someone blind.\n31\u2192 - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32\u2192 date).\n33\u2192\n34\u2192## When creating slides\n35\u2192\n36\u2192- If the audience doesn't need it, why put it on the slides?\n37\u2192- People shouldn't have to count the items on a slide to know how many are\n38\u2192 there.\n39\u2192- Don't use your slides as your \"presentation notes.\"\n40\u2192 - Don't show your notes to the audience.\n41\u2192 - **A bad slide is worse than no slide at all.**\n42\u2192- **One message per slide** is a good rule of thumb.\n43\u2192 - Prioritize the *so what* (or *therefore what*) of your message.\n44\u2192- Make it easy to interpret the information on the slide:\n45\u2192 - 3\u20135 items at most.\n46\u2192 - Align the title left.\n47\u2192 - Do not clutter slides with repetitive or unnecessary information: logos,\n48\u2192 affiliation, date, slide numbers.\n49\u2192- Doumont's suggested workflow:\n50\u2192 1. **Start with the title.**\n51\u2192 - Use a full sentence in the title.\n52\u2192 - The title is the main message of the slide.\n53\u2192 - Make it easy to read: left-aligned, with intentional line breaks.\n54\u2192 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55\u2192- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56\u2192\n57\u2192## General preparation\n58\u2192\n59\u2192- Planning for audience, content, and structure matters more than materials\n60\u2192 (i.e., slides).\n61\u2192- Try using one sheet of paper for presentation preparation.\n62\u2192- It's most important that your presentation is:\n63\u2192 - well planned, structured, and delivered;\n64\u2192 - **if you don't have a lot of time to prepare, don't focus on making\n65\u2192 slides.**\n66\u2192- Practice the presentation a few times. Practice at least once *without* your\n67\u2192 slideshow!\n68\u2192- If doing a handout, keep it separate from the slides you're preparing.\n69\u2192- Impress the audience with your body.\n70\u2192 - Use gestures, not laser pointers.\n71\u2192 - \"Control\" the audience with your body \u2014 keep their attention on your\n72\u2192 presentation.\n73\u2192\n74\u2192## Impressions / what to apply to the IDETC presentation\n75\u2192\n76\u2192\n77\u2192\n78\u2192## Questions / things to discuss\n79\u2192\n80\u2192", + "name": "read", + "tool_call_id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "info": { + "start_ts": 8147.331133416, + "end_ts": 8147.338304015 + } + }, + { + "role": "tool", + "content": " 1\u2192\\documentclass[10pt]{article}\n 2\u2192\n 3\u2192% --- Packages ---\n 4\u2192\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5\u2192\\usepackage[hidelinks]{hyperref}\n 6\u2192\\usepackage{parskip}\n 7\u2192\\usepackage{graphicx}\n 8\u2192\\usepackage{caption}\n 9\u2192\\usepackage{titlesec}\n 10\u2192\\usepackage{wrapfig}\n 11\u2192\\usepackage[numbers,sort&compress]{natbib}\n 12\u2192\n 13\u2192% --- Formatting ---\n 14\u2192\\setlength{\\parskip}{0.2em}\n 15\u2192\\setlength{\\parindent}{0em}\n 16\u2192\\titleformat*{\\section}{\\large\\bfseries}\n 17\u2192\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18\u2192\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19\u2192\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20\u2192\\setlength{\\emergencystretch}{2em}\n 21\u2192\\setlength{\\intextsep}{0.2em}\n 22\u2192\\bibliographystyle{abbrvnat}\n 23\u2192\n 24\u2192\\begin{document}\n 25\u2192\n 26\u2192\\begin{center}\n 27\u2192{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28\u21923D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29\u2192\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30\u2192\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31\u2192\\end{center}\n 32\u2192\n 33\u2192\\vspace{0.4em}\n 34\u2192\n 35\u2192\\section*{Abstract}\n 36\u2192Tensegrity-inspired architectures---rigid struts suspended within a\n 37\u2192continuous flexible network---can exhibit tunable nonlinear\n 38\u2192force--displacement responses and favorable energy absorption per unit\n 39\u2192mass, motivating their use in lightweight energy-absorbing and protective\n 40\u2192structures\n 41\u2192\\citep{skelton2009tensegrity,pajunen2019design}.\n 42\u2192Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43\u2192struts and flexible (TPU) elements in a single build\n 44\u2192\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45\u2192space---strut geometry, tension-element cross-section, connectivity\n 46\u2192topology, and unit-cell tiling---is too large to explore by trial and\n 47\u2192error. We present a closed-loop experimental campaign that uses\n 48\u2192Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49\u2192to drive a design--print--test workflow operating directly on physical\n 50\u2192measurements, without relying on calibrated finite-element simulation\n 51\u2192for objective evaluation.\n 52\u2192\n 53\u2192\\section{Introduction and Motivation}\n 54\u2192\n 55\u2192\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56\u2192\\centering\n 57\u2192\\vspace{-0.4em}\n 58\u2192\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59\u2192\\captionsetup{font=small}\n 60\u2192\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61\u2192Candidate tensegrity-inspired unit cells are instantiated from a\n 62\u2192parameter vector, fabricated by multi-material FDM, and tested under\n 63\u2192quasi-static compression and drop-weight impact. Measured peak force,\n 64\u2192specific energy absorption (SEA), and compaction efficiency update a\n 65\u2192Gaussian-process surrogate that proposes the next batch of designs.}\n 66\u2192\\label{fig:overview}\n 67\u2192\\vspace{-0.4em}\n 68\u2192\\end{wrapfigure}\n 69\u2192\n 70\u2192Tensegrity structures combine compression-only members with a\n 71\u2192pre-tensioned tension network to achieve stiffness without rigid\n 72\u2192joinery~\\citep{skelton2009tensegrity}. Their\n 73\u2192nonlinear, often load-limiting force--displacement response makes them\n 74\u2192attractive for impact mitigation, packaging, and wearable protective\n 75\u2192gear~\\citep{pajunen2019design}. Idealized\n 76\u2192cable--strut tensegrities are difficult to manufacture at scale, but\n 77\u2192\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78\u21923D printing reproduce key tensegrity behaviors---including\n 79\u2192post-buckling stability and load-limiting force--displacement\n 80\u2192response~\\citep{pajunen2019design}---motivating our extension to\n 81\u2192multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82\u2192elements. PLA provides rigid struts; TPU provides flexible,\n 83\u2192rate-dependent tension elements whose viscoelasticity is expected to\n 84\u2192contribute hysteretic energy dissipation under impact loading,\n 85\u2192consistent with the energy-absorbing behavior reported for rigid--TPU\n 86\u2192multi-material prints~\\citep{khatri2024energy}.\n 87\u2192\n 88\u2192The challenge is that small geometric and topological changes can\n 89\u2192produce large changes in the measured response, and high-fidelity\n 90\u2192simulation of FDM specimens is computationally expensive and often\n 91\u2192struggles to capture physical defects such as interfacial slip and\n 92\u2192TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93\u2192trial-and-error sweeps over the joint design space are also infeasible:\n 94\u2192even modest parameter resolutions yield thousands of candidates, each\n 95\u2192requiring print and test time. We instead treat the physical\n 96\u2192experiment as the source of truth and use BO to select which specimens\n 97\u2192are most informative to fabricate next---an approach used in related\n 98\u2192closed-loop materials and structures\n 99\u2192campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100\u2192Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101\u2192set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102\u2192designs using parallel, noisy multi-objective BO driven by measured\n103\u2192$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104\u2192\n105\u2192\\section{Approach}\n106\u2192\\subsection{Closed-Loop Workflow}\n107\u2192Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108\u2192proceeds in four steps: (i)~a parameterized unit-cell design is\n109\u2192instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110\u2192is sliced and printed on a multi-material FDM system; (iii)~it is\n111\u2192tested under quasi-static compression and instrumented drop-weight\n112\u2192impact; and (iv)~the resulting performance metrics update a\n113\u2192Gaussian-process (GP) surrogate, which a batch acquisition function\n114\u2192queries to recommend the next set of candidates. Surrogate updating\n115\u2192and experiment selection are automated; specimen handling, slicing,\n116\u2192and test setup remain manual.\n117\u2192\n118\u2192\\subsection{Design Space}\n119\u2192We parameterize a family of unit cells using a core-wrapping\n120\u2192architecture in which rigid PLA struts are enclosed by continuous TPU\n121\u2192skins; the wrapping rationale follows multi-material FDM strategies in\n122\u2192which rigid panels are wrapped by stretchable soft material to prevent\n123\u2192delamination and improve interfacial bonding at the rigid--flexible\n124\u2192interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125\u2192comprises continuous geometric variables (strut diameter and length,\n126\u2192tension-element width and thickness), an integer strut count per unit\n127\u2192cell, and two categorical variables (connectivity topology, unit-cell\n128\u2192tiling pattern). Categorical choices are encoded explicitly rather\n129\u2192than embedded in a continuous metric, and bounds on the continuous\n130\u2192variables are chosen to remain within the printer's resolution and\n131\u2192overhang limits and to keep specimens in a common form factor for the\n132\u2192test fixtures.\n133\u2192\n134\u2192\\subsection{Surrogate and Acquisition}\n135\u2192The GP surrogate is trained on three measured performance metrics:\n136\u2192peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137\u2192(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138\u2192objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139\u2192likelihood with priors to avoid pathological length scales at small $n$.\n140\u2192Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141\u2192interfacial defects motivates our use of q-noisy expected hypervolume\n142\u2192improvement (qNEHVI), which explicitly accounts for observation noise\n143\u2192in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144\u2192expected-improvement variants. Print failures are treated as a\n145\u2192probabilistic feasibility constraint: the acquisition score for each\n146\u2192candidate is its qNEHVI improvement weighted by the GP-estimated\n147\u2192probability of successful fabrication, following recent constrained\n148\u2192multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149\u2192Where applicable, we use numerically stable log-space variants of\n150\u2192improvement-based acquisitions to mitigate vanishing-gradient\n151\u2192pathologies as data and constraints\n152\u2192accumulate~\\citep{ament2023logei}.\n153\u2192\n154\u2192\\section{Methods}\n155\u2192\\subsection{Fabrication}\n156\u2192Specimens are fabricated on a multi-material FDM system capable of\n157\u2192co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158\u2192Print parameters (temperature, line width, infill, retraction) are\n159\u2192held fixed within a batch to isolate the effect of the design\n160\u2192variables. Each specimen is weighed and dimensionally inspected prior\n161\u2192to testing so that mass and geometric deviations are available as\n162\u2192covariates.\n163\u2192\n164\u2192\\subsection{Mechanical Testing}\n165\u2192Quasi-static compression is performed on a screw-driven load frame at\n166\u2192a fixed nominal strain rate, with force--displacement recorded to\n167\u2192densification. Drop-weight impact testing uses an instrumented tup at\n168\u2192a fixed impact energy; transmitted force is captured at high sample\n169\u2192rate, and specimens are imaged before and after impact to document\n170\u2192failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171\u2192unit mass, and compaction efficiency $\\eta$, defined consistently\n172\u2192across the campaign. A control specimen from an earlier batch is\n173\u2192retested periodically to monitor TPU batch-to-batch and printer drift.\n174\u2192\n175\u2192\\section{Expected Outcomes and Discussion}\n176\u2192We will report how BO-selected designs evolve across iterations, how\n177\u2192GP surrogate predictions and uncertainties change as data accumulate,\n178\u2192and which Pareto-efficient trade-offs emerge between SEA and peak\n179\u2192transmitted force within the explored design space. We will also\n180\u2192discuss practical lessons for operating the closed-loop workflow,\n181\u2192including handling of print failures, batch-to-batch variability in\n182\u2192TPU response, the influence of the rigid--flexible interface on\n183\u2192energy absorption, and the exploration--exploitation balance of the\n184\u2192acquisition function under realistic experimental noise. We expect\n185\u2192the campaign to clarify where physical experimentation is genuinely\n186\u2192required versus where simpler analytical or multi-fidelity surrogate\n187\u2192shortcuts~\\citep{mo2023accelerated} could be\n188\u2192substituted in future work.\n189\u2192\n190\u2192\\section{Conclusion}\n191\u2192This work demonstrates a partially automated, experiment-driven\n192\u2192closed-loop pipeline for designing multi-material 3D-printed\n193\u2192tensegrity-inspired energy absorbers, with surrogate updating and\n194\u2192candidate selection automated and fabrication and testing kept manual.\n195\u2192By updating a GP surrogate directly from physical measurements, the\n196\u2192workflow avoids direct dependence on calibrated finite-element\n197\u2192simulation for objective evaluation while still providing principled,\n198\u2192multi-objective design recommendations. The same closed-loop pattern is\n199\u2192expected to transfer to other multi-material additively manufactured\n200\u2192architectures whose performance is dominated by hard-to-simulate\n201\u2192effects.\n202\u2192\n203\u2192{\\tiny\n204\u2192\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205\u2192\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206\u2192% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207\u2192% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208\u2192\\providecommand{\\doi}[1]{}\n209\u2192\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210\u2192\\bibliography{references}\n211\u2192}\n212\u2192\n213\u2192\\end{document}", + "name": "read", + "tool_call_id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "info": { + "start_ts": 8147.332633466, + "end_ts": 8147.339043095 + } + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\", \"offset\": 174, \"limit\": 100}", + "name": "read" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ], + "info": null + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Tensegrity Slides \u2014 Draft 1 (extracted content)\n 2\u2192\n 3\u2192Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4\u2192\n 5\u2192\n 6\u2192## Slide 1 \u2014 layout: Title Only\n 7\u2192\n 8\u2192**On-slide text:**\n 9\u2192- **Title 1**: Let\u2019s build better tensegrity structures faster\n 10\u2192- **TextBox 4**: Marcus Madsen\n 11\u2192Undergraduate researcher under Sterling Baird, Ph.D.\n 12\u2192- **TextBox 6**: IDETC 2026\n 13\u2192\n 14\u2192**Non-text shapes / media / placeholders:**\n 15\u2192- [PICTURE (13)] Picture 3\n 16\u2192- [PICTURE (13)] Picture 8\n 17\u2192\n 18\u2192**Presenter notes:**\n 19\u2192> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20\u2192> \n 21\u2192> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22\u2192\n 23\u2192\n 24\u2192## Slide 2 \u2014 layout: Title Only\n 25\u2192\n 26\u2192**On-slide text:**\n 27\u2192- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings\u2014and to problems closer to home.\n 28\u2192\n 29\u2192**Non-text shapes / media / placeholders:**\n 30\u2192- [MEDIA (16)] Online Media 5\n 31\u2192\n 32\u2192**Presenter notes:**\n 33\u2192> Replace this video with a downloaded and trimmed version of just the fall. \n 34\u2192> \n 35\u2192> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle \u2014 thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander\u00a0is\u00a0the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit\u00a0and get back up\u00a0\u2014 it survives multiple drops and keeps working.\n 36\u2192\n 37\u2192\n 38\u2192## Slide 3 (HIDDEN) \u2014 layout: Title Only\n 39\u2192\n 40\u2192**On-slide text:**\n 41\u2192- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42\u2192- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43\u2192- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44\u2192\n 45\u2192**Presenter notes:**\n 46\u2192> This might be better explained purely vocally while on the previous slide.\n 47\u2192\n 48\u2192\n 49\u2192## Slide 4 \u2014 layout: Title Only\n 50\u2192\n 51\u2192**On-slide text:**\n 52\u2192- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53\u2192- **TextBox 3**: Iteration\n 54\u2192- **TextBox 5**: Building\n 55\u2192- **TextBox 7**: Testing\n 56\u2192\n 57\u2192**Non-text shapes / media / placeholders:**\n 58\u2192- [MEDIA (16)] AdobeStock_207717222\n 59\u2192- [MEDIA (16)] Online Media 2\n 60\u2192- [PICTURE (13)] Picture 14\n 61\u2192- [PICTURE (13)] Picture 16\n 62\u2192- [PICTURE (13)] Picture 18\n 63\u2192\n 64\u2192**Presenter notes:**\n 65\u2192> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn\u2019t become distracting because it\u2019s fun to watch it spin.\n 66\u2192> \n 67\u2192> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68\u2192\n 69\u2192\n 70\u2192## Slide 5 (HIDDEN) \u2014 layout: Title Only\n 71\u2192\n 72\u2192**On-slide text:**\n 73\u2192- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74\u2192- **TextBox 14**: Baird et al. Matter (2022)\n 75\u2192\n 76\u2192**Non-text shapes / media / placeholders:**\n 77\u2192- [GROUP (6)] Group 2\n 78\u2192\n 79\u2192**Presenter notes:**\n 80\u2192> Consult Sterling on this slide. I don\u2019t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it\u2019s well enough known that it\u2019d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81\u2192\n 82\u2192\n 83\u2192## Slide 6 (HIDDEN) \u2014 layout: Title Only\n 84\u2192\n 85\u2192**On-slide text:**\n 86\u2192- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87\u2192- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88\u2192\n 89\u2192**Presenter notes:**\n 90\u2192> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91\u2192\n 92\u2192\n 93\u2192## Slide 7 \u2014 layout: Title Only\n 94\u2192\n 95\u2192**On-slide text:**\n 96\u2192- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97\u2192- **TextBox 5**: 6 variables\n 98\u2192- **TextBox 7**: 2 types of input data\n 99\u2192- **TextBox 9**: 2 objectives\n100\u2192- **TextBox 17**: *image of blackbox*\n101\u2192\n102\u2192**Non-text shapes / media / placeholders:**\n103\u2192- [LINE (9)] Connector: Curved 11\n104\u2192- [LINE (9)] Connector: Curved 13\n105\u2192- [LINE (9)] Connector: Curved 15\n106\u2192\n107\u2192**Presenter notes:**\n108\u2192> Very rough slide as I\u2019m not entirely sure what graphic or text to put on this, partially because I\u2019m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109\u2192\n110\u2192\n111\u2192## Slide 8 \u2014 layout: Title Only\n112\u2192\n113\u2192**On-slide text:**\n114\u2192- **Title 1**: Multi-material additive manufacturing supplies that data fast \u2014 single-build co-fabrication, with no joining of parts.\n115\u2192\n116\u2192**Non-text shapes / media / placeholders:**\n117\u2192- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118\u2192- [PICTURE (13)] Picture 4\n119\u2192\n120\u2192**Presenter notes:**\n121\u2192> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122\u2192\n123\u2192\n124\u2192## Slide 9 \u2014 layout: Title Only\n125\u2192\n126\u2192**On-slide text:**\n127\u2192- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128\u2192- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129\u2192- **TextBox 5**: *placeholder for slo motion of drop test*\n130\u2192\n131\u2192\n132\u2192## Slide 10 \u2014 layout: Title Only\n133\u2192\n134\u2192**On-slide text:**\n135\u2192- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136\u2192- **TextBox 3**: Printing\n137\u2192- **TextBox 5**: Testing\n138\u2192- **TextBox 7**: BO feed\n139\u2192\n140\u2192**Non-text shapes / media / placeholders:**\n141\u2192- [PICTURE (13)] Picture 14\n142\u2192- [PICTURE (13)] Picture 16\n143\u2192- [PICTURE (13)] Picture 18\n144\u2192- [PICTURE (13)] Graphic 8\n145\u2192\n146\u2192**Presenter notes:**\n147\u2192> I think it might be better if we replaced \u201cprinting\u201d with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148\u2192\n149\u2192\n150\u2192## Slide 11 (HIDDEN) \u2014 layout: Title Only\n151\u2192\n152\u2192**On-slide text:**\n153\u2192- **Title 1**: Reserved for results\n154\u2192\n155\u2192**Presenter notes:**\n156\u2192> Would be helpful if we could show that this process works in this case.\n157\u2192\n158\u2192\n159\u2192## Slide 12 \u2014 layout: Title Only\n160\u2192\n161\u2192**On-slide text:**\n162\u2192- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163\u2192- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164\u2192- **TextBox 3**: *image to show lattice structure\n165\u2192\n166\u2192\n167\u2192## Slide 13 \u2014 layout: Title Only\n168\u2192\n169\u2192**Non-text shapes / media / placeholders:**\n170\u2192- [PICTURE (13)] Picture 3\n171\u2192\n172\u2192**Presenter notes:**\n173\u2192> Slide for questions at the end\n174\u2192\n175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "info": { + "start_ts": 8147.330900226, + "end_ts": 8147.337548875 + } + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Presentation \u2014 Doumont Presentation Structure Template\n 2\u2192\n 3\u2192Transcribed from Jean-luc Doumont's presentation-structure template\n 4\u2192([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5\u2192adapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\n 6\u2192All rights reserved. Can be downloaded from .\n 7\u2192\n 8\u2192> To design your presentation, write down your ideas for each component below.\n 9\u2192> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10\u2192\n 11\u2192**Draft 3** \u2014 applies the inline review comments from @sgbaird on PR #84 and the\n 12\u2192priority revisions from the\n 13\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14\u2192Summaries of what changed are at the bottom of this file\n 15\u2192([Draft 2 \u2192 3](#changes-from-draft-2), [Draft 1 \u2192 2](#changes-from-draft-1)).\n 16\u2192\n 17\u2192**Scope note (frame everything below with this):** the study presented is the\n 18\u2192optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19\u2192processing parameters** (e.g., nozzle temperature, print speed). PLA\u2013TPU is a\n 20\u2192**proxy system** \u2014 not flight material \u2014 used to prototype a self-driving-lab\n 21\u2192style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22\u2192The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23\u2192beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24\u2192\n 25\u2192## Opening\n 26\u2192\n 27\u2192### Attention getter\n 28\u2192\n 29\u2192*A way to lead the audience to the need efficiently*\n 30\u2192\n 31\u2192Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32\u2192landings \u2014 and to problems closer to home.\n 33\u2192\n 34\u2192One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35\u2192clip) do the work:\n 36\u2192\n 37\u2192> Landing a payload on another planet means surviving an impact in conditions\n 38\u2192> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n 39\u2192> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40\u2192> structure: the lander *is* the energy absorber. And unlike crushable\n 41\u2192> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42\u2192> hit **and get back up** \u2014 it survives multiple drops and keeps working.\n 43\u2192\n 44\u2192Candidate Super Ball Bot videos to show (verified links):\n 45\u2192\n 46\u2192- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n 47\u2192 (official channel); the canonical overview.\n 48\u2192- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49\u2192 \u2014 Wall Street Journal; prototype drop footage.\n 50\u2192- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51\u2192 \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n 52\u2192\n 53\u2192Immediately after the hook, state the present study scope in one sentence\n 54\u2192(per the scope note above): *we study a printed PLA\u2013TPU T3 prism as a proxy\n 55\u2192system for prototyping an autonomous tensegrity-optimization workflow* \u2014 so\n 56\u2192the planetary framing never outruns the demonstrated scope.\n 57\u2192\n 58\u2192\n 60\u2192\n 61\u2192### Need\n 62\u2192\n 63\u2192*A difference between actual and desired situations*\n 64\u2192\n 65\u2192**Actual situation** (a two-sided pincer \u2014 stated as a scoped engineering\n 66\u2192judgment, not an absolute):\n 67\u2192\n 68\u2192- For these structures, obtaining trustworthy objective values from a\n 69\u2192 validated high-fidelity model would require substantial calibration of\n 70\u2192 interface, rate, contact, and defect behavior \u2014 and even heavy-duty\n 71\u2192 simulation falls short of the experiments we are running: peak transmitted\n 72\u2192 shock is a millisecond-scale transient that is notoriously difficult to\n 73\u2192 resolve numerically. This holds even for idealized cable\u2013strut tensegrities\n 74\u2192 in their actual engineering materials (steel or similar), not just for\n 75\u2192 printed PLA\u2013TPU.\n 76\u2192 \n 79\u2192- Exhaustive physical search fails on cost: a traditional design of\n 80\u2192 experiments over this relatively high-dimensional space (geometry + FDM\n 81\u2192 process parameters) would demand hundreds of specimens, each costing print\n 82\u2192 and test time.\n 83\u2192- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84\u2192 more complex structures \u2014 especially tensegrity *lattices* \u2014 become\n 85\u2192 extremely difficult and ultimately infeasible to hand-build.\n 86\u2192\n 87\u2192**Desired situation:**\n 88\u2192\n 89\u2192- Trustworthy performance data without hand assembly (easy creation of\n 90\u2192 tensegrity structures, scalable beyond a single prism).\n 91\u2192- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n 92\u2192 not the hundreds a traditional design of experiments would require.\n 93\u2192\n 94\u2192### Task\n 95\u2192\n 96\u2192*What I decided/was asked to do to address the need*\n 97\u2192\n 98\u2192Build a closed-loop design\u2013print\u2013test workflow that optimizes a **T3-prism\n 99\u2192tensegrity-inspired energy absorber together with its FDM processing\n100\u2192parameters** (e.g., temperature, print speed) directly from real, measured\n101\u2192impact data \u2014 maximizing energy absorbed per gram (specific energy\n102\u2192absorption) and compaction efficiency, subject to a cap on the peak force\n103\u2192transmitted to the payload. Extensions beyond the T3 prism are future work.\n104\u2192\n105\u2192### Main message\n106\u2192\n107\u2192*The one sentence I want my audience to remember*\n108\u2192\n109\u2192By closing the loop between multi-material 3D printing and Bayesian\n110\u2192optimization, we can optimize tensegrity-inspired energy absorbers directly\n111\u2192from real impact data \u2014 in dozens of prints, not the hundreds a traditional\n112\u2192design of experiments would demand.\n113\u2192\n114\u2192\n118\u2192\n119\u2192### Preview\n120\u2192\n121\u2192*A map of the body (ideally three points, max. five)*\n122\u2192\n123\u21921. When model calibration and exhaustive testing are both costly, each\n124\u2192 physical specimen must be chosen for its information value \u2014 so the\n125\u2192 measured experiment is our source of truth.\n126\u21922. Bayesian optimization makes physical experimentation affordable: it handles\n127\u2192 noisy measurements, small batches, and the constrained multi-objective\n128\u2192 trade-off \u2014 and Honegumi let us scaffold it in minutes.\n129\u21923. Multi-material additive manufacturing closes the loop: single-build PLA\u2013TPU\n130\u2192 co-fabrication turns each proposed design into test data in hours, with no\n131\u2192 joining of parts.\n132\u21924. *(Optional fourth point, if time allows)* The same closed loop points at\n133\u2192 what comes next: tensegrity lattices, flight-relevant materials, and\n134\u2192 safety hardware on Earth.\n135\u2192\n136\u2192## Body\n137\u2192\n138\u2192### Point 1\n139\u2192\n140\u2192When model calibration and exhaustive testing are both costly, each physical\n141\u2192specimen must be chosen for its information value.\n142\u2192\n143\u2192- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144\u2192 these structures, a sufficiently calibrated multiphysics model is expensive\n145\u2192 to build relative to the design campaign itself \u2014 and the hardest quantity,\n146\u2192 peak transmitted shock, is a millisecond-scale transient that even\n147\u2192 heavy-duty simulation struggles to resolve. The gap persists in the actual\n148\u2192 engineering materials a real lander would use (steel struts and cables),\n149\u2192 so this is not merely an artifact of printed PLA\u2013TPU.\n150\u2192- The alternative \u2014 sweeping the design space physically \u2014 fails on cost: a\n151\u2192 traditional design of experiments over geometry plus FDM process parameters\n152\u2192 would take hundreds of specimens, each needing print + test time.\n153\u2192- Therefore: treat the physical experiment as the authoritative source of\n154\u2192 objective values, and be ruthless about *which* experiments to run.\n155\u2192- Anticipate the FEA-veteran pushback (\"inconvenient \u2260 untrustworthy\"): frame\n156\u2192 it as a cost-and-dependence decision, and keep a backup slide with the\n157\u2192 literature (or our own validation) on simulation\u2013experiment discrepancy for\n158\u2192 impact transients.\n159\u2192\n160\u2192### Transition\n161\u2192\n162\u2192If every data point must be a real experiment, we need a method that extracts\n163\u2192the most from every specimen \u2014 we need to make the process radically more\n164\u2192sample-efficient.\n165\u2192\n166\u2192### Point 2\n167\u2192\n168\u2192Bayesian optimization makes physical experimentation affordable, even with\n169\u2192limited, noisy data.\n170\u2192\n171\u2192- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172\u2192 visual in the main talk; implementation detail goes to backup slides.\n173\u2192 Spoken version:\n174\u2192 > \"After each batch, the model estimates both expected performance and\n175\u2192 > uncertainty across the design space. The acquisition rule selects a small\n176\u2192 > next batch that is most likely to expand the feasible trade-off frontier,\n177\u2192 > while accounting for noisy tests and print failures.\"\n178\u2192- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179\u2192 motivate a noise-aware acquisition function (qNEHVI) rather than\n180\u2192 non-noise-aware alternatives. (Backup slide: how observation noise is\n181\u2192 actually modeled \u2014 replicates, noise identification \u2014 and budget-matched\n182\u2192 baselines such as Sobol/random sampling.)\n183\u2192- Small parallel batches: the surrogate proposes the next batch of specimens\n184\u2192 to print, balancing exploration and exploitation.\n185\u2192- Constrained multi-objective by construction: **objectives** = maximize\n186\u2192 specific energy absorption and compaction efficiency; **constraint** = peak\n187\u2192 transmitted force must stay under a cap; the deliverable is the *feasible*\n188\u2192 Pareto front between the two objectives among force-cap-satisfying designs.\n189\u2192 (Keep this formulation consistent everywhere \u2014 do not present \"SEA vs. peak\n190\u2192 force\" as the trade-off.)\n191\u2192- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192\u2192 this advanced task (noisy, batched, constrained, multi-objective) in\n193\u2192 minutes using [Honegumi](https://honegumi.readthedocs.io/) \u2014 the audience\n194\u2192 can do the same for their own problems; show the code-template picker\n195\u2192 briefly so they see how low the barrier is.\n196\u2192\n197\u2192### Transition\n198\u2192\n199\u2192The optimizer is no longer the bottleneck \u2014 the limit is now providing real\n200\u2192data quickly enough.\n201\u2192\n202\u2192### Point 3\n203\u2192\n204\u2192Multi-material additive manufacturing supplies that data fast \u2014 single-build\n205\u2192co-fabrication, with no joining of parts.\n206\u2192\n207\u2192- Single-build PLA\u2013TPU co-fabrication: rigid struts and flexible tension\n208\u2192 network in one print \u2014 no joining or hand assembly of members, which closes\n209\u2192 the \"easy creation\" gap from the Need and is what makes future lattices\n210\u2192 feasible at all. (Be precise: if support removal, trimming, or conditioning\n211\u2192 remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212\u2192 assembly.\")\n213\u2192- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214\u2192 as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215\u2192 pre-tension status) plus a short deformation sequence \u2014 so the EDL engineer\n216\u2192 doesn't file it as a \"crush structure\" and the grad student can tell it from\n217\u2192 an ordinary lattice.\n218\u2192- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219\u2192 tested specimen (quasi-static compression + instrumented drop-weight impact)\n220\u2192 in hours. (Backup slide: representative print-to-result time breakdown and\n221\u2192 which steps remain manual \u2014 candidate selection is automated; slicing,\n222\u2192 handling, and testing are not yet.)\n223\u2192- Now we have the data we need to complete the loop.\n224\u2192\n225\u2192### Point 4 (optional \u2014 future applications)\n226\u2192\n227\u2192*Include if timing allows; otherwise fold into the Conclusion.*\n228\u2192\n229\u2192The same closed loop transfers wherever tests are authoritative, each test is\n230\u2192costly, the design space is mixed, and several performance goals conflict:\n231\u2192\n232\u2192- Tensegrity *lattices* and more complex architectures \u2014 exactly where hand\n233\u2192 assembly becomes infeasible and single-build co-fabrication pays off.\n234\u2192- Migration from the PLA\u2013TPU proxy toward flight-relevant materials and\n235\u2192 impact regimes.\n236\u2192- Safety hardware on Earth (e.g., the tensegrity crutch tip \u2014 future work).\n237\u2192- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238\u2192 handling, testing) in the loop.\n239\u2192\n240\u2192### Evidence / results\n241\u2192\n242\u2192*What we found \u2014 ground the conclusion in shown data*\n243\u2192\n244\u2192Build the talk around a quantitative three-slide sequence (per the\n245\u2192mock-audience report; insert observed values once the campaign completes):\n246\u2192\n247\u21921. **Campaign ledger** \u2014 \"The campaign used N specimens across B adaptive\n248\u2192 batches under a fixed physical-test budget\": initialization vs. adaptive\n249\u2192 batches, replicates, print failures, cycle time, and the numerical value\n250\u2192 (and rationale) of the force cap.\n251\u21922. **Budget-matched baseline** \u2014 \"Adaptive selection improved the feasible\n252\u2192 design set faster than [predeclared baseline]\": performance vs. number of\n253\u2192 physical tests against Sobol/random sampling at the same budget, with\n254\u2192 uncertainty where available.\n255\u21923. **Measured feasible Pareto front** \u2014 specific energy absorption vs.\n256\u2192 compaction efficiency among designs satisfying the force cap, with\n257\u2192 replicate spread, marked infeasible designs, and specimen photos or\n258\u2192 force\u2013time traces for representative Pareto designs.\n259\u2192\n260\u2192Supporting raw-data candidates: drop-test force\u2013time traces, Sobol /\n261\u2192first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262\u2192\n263\u2192### Suggested 15-minute allocation\n264\u2192\n265\u2192*(From the mock-audience report \u2014 results get the largest block.)*\n266\u2192\n267\u2192| Time | Content |\n268\u2192|---|---|\n269\u2192| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270\u2192| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271\u2192| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272\u2192| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273\u2192| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274\u2192| 1 min | Limitations and transfer conditions |\n275\u2192| 1 min | Measured conclusion + bookend |\n276\u2192\n277\u2192Move kernel choices, encodings, and other implementation detail to backup\n278\u2192slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279\u2192[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280\u2192\n281\u2192## Closing\n282\u2192\n283\u2192### Review\n284\u2192\n285\u2192*A recap of the body, leading into the conclusion*\n286\u2192\n287\u2192Using real, measured impact data from additively manufactured\n288\u2192tensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\n289\u2192optimization routine \u2014 we rapidly iterate toward better energy absorbers.\n290\u2192This feedback loop makes tensegrity optimization dramatically faster: dozens\n291\u2192of prints instead of the hundreds a traditional design of experiments over\n292\u2192this design space would require.\n293\u2192\n294\u2192### Conclusion\n295\u2192\n296\u2192*What the above means to the audience in the end*\n297\u2192\n298\u2192Because the loop runs on physical measurements, it is useful precisely where\n299\u2192model calibration is not worth its cost \u2014 and the same closed-loop pattern\n300\u2192should transfer (a conditional claim, pending evidence) to other additively\n301\u2192manufactured architectures whose performance is dominated by\n302\u2192hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303\u2192*use this loop when tests are authoritative, each test is costly, the design\n304\u2192space is mixed, and several performance goals conflict.* For tensegrity\n305\u2192specifically, it opens a path from this T3 proxy study toward lattices,\n306\u2192flight-relevant materials, payload protection on other planets, and safety\n307\u2192hardware here on Earth (e.g., the crutch tip, as future work).\n308\u2192\n309\u2192### Close\n310\u2192\n311\u2192*A way to end the presentation clearly and elegantly*\n312\u2192\n313\u2192Return to the opening image: the next planetary lander's energy absorber \u2014 a\n314\u2192structure that has to survive not one impact but many \u2014 doesn't have to take\n315\u2192years of hand-built prototypes; with this loop, candidate designs can be\n316\u2192designed, printed, and tested against real impacts in weeks instead of months\n317\u2192or longer. End on the measured engineering result and its transfer condition,\n318\u2192then the bookend: every method that compresses design time moves our ventures\n319\u2192\u2014 on this planet and beyond \u2014 forward.\n320\u2192\n321\u2192---\n322\u2192\n323\u2192## Changes from Draft 2\n324\u2192\n325\u2192Applied from @sgbaird's inline review comments on PR #84 and the\n326\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327\u2192\n328\u2192**From the inline review comments:**\n329\u2192\n330\u21921. **Reusability in the hook** (line 22): the tensegrity lander survives\n331\u2192 multiple drops, unlike crushable/sacrificial alternatives.\n332\u21922. **Proxy-system framing** (lines 49, 87): PLA\u2013TPU would never fly; it is a\n333\u2192 proxy for prototyping a self-driving-lab-style workflow \u2014 stated in a\n334\u2192 scope note up front and re-stated right after the hook.\n335\u21923. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336\u2192 heavy-duty simulation falls short of the experiments (millisecond-scale\n337\u2192 peak shock), not that it merely costs too much.\n338\u21924. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339\u2192 complex structures and especially lattices are not.\n340\u21925. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341\u2192 (temperature, print speed, \u2026) only; the rest is explicitly future work.\n342\u21926. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343\u2192 literature search (or validation of experiments against simulation), plus\n344\u2192 the millisecond-resolution peak-shock argument.\n345\u21927. **Optional Point 4 added** (line 97): future applications, with a note it\n346\u2192 can fold into the Conclusion if time is short.\n347\u21928. **Steel-materials point** (line 104): simulations fall short for the\n348\u2192 actual engineering materials (steel struts/cables), not just printed\n349\u2192 PLA\u2013TPU.\n350\u21929. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351\u2192 in minutes, pitched so the audience sees they can use it too.\n352\u219210. **\"Thousands\" \u2192 \"hundreds\"** (line 173): baseline is now a traditional\n353\u2192 design of experiments over a high-dimensional space (hundreds), in the\n354\u2192 main message, Need, Point 1, and Review.\n355\u219211. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356\u2192 or longer\" for candidate designs.\n357\u2192\n358\u2192**From the mock-audience report's priority revisions:**\n359\u2192\n360\u219212. **Quantitative three-slide results sequence** (Priority 1): campaign\n361\u2192 ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front, with a\n362\u2192 fallback main-message wording if the campaign can't yet support the\n363\u2192 efficiency claim.\n364\u219213. **Anti-simulation premise replaced with a scoped cost decision**\n365\u2192 (Priority 2): Point 1 retitled to the information-value message;\n366\u2192 \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367\u2192 anticipated with a backup slide.\n368\u219214. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369\u2192 implementation detail moved to backup slides; Q&A prep pointed at the\n370\u2192 report's claims-vs-evidence table.\n371\u219215. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372\u2192 the main talk, machinery in backup slides; the misleading\n373\u2192 \"qNEHVI over standard expected improvement\" comparison replaced with\n374\u2192 \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375\u219216. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376\u2192 sequence in Point 3, so the structure isn't remembered as a generic crush\n377\u2192 structure or lattice.\n378\u219217. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379\u2192 co-fabrication / no joining of parts,\" with a note to list remaining\n380\u2192 manual steps and an automation-status caveat.\n381\u219218. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382\u2192 compaction efficiency, constraint = force cap, deliverable = *feasible*\n383\u2192 Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384\u219219. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385\u2192 transfer claim marked conditional, and the close ends on the measured\n386\u2192 result before the bookend.\n387\u2192\n388\u2192## Changes from Draft 1\n389\u2192\n390\u2192Feedback from the PR #84 review, applied in Draft 2:\n391\u2192\n392\u21921. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n393\u2192 dozens-not-thousands claim); body and message now carry the same story.\n394\u21922. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n395\u2192 manuscript's positioning (#75/#76) is that the physical experiment is the\n396\u2192 source of truth, explicitly avoiding calibrated FE simulation.\n397\u21923. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n398\u2192 parametric design space and avoid an over-claim to a design-automation\n399\u2192 audience.\n400\u21924. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401\u2192 energy absorption and compaction efficiency, cap peak transmitted force.\n402\u21925. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403\u2192 *and* the space is too big to brute-force \u2014 which makes BO + AM the\n404\u2192 inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405\u2192 above.)*\n406\u21926. **Point 1 given its own content** (the *why* behind the inefficiency)\n407\u2192 instead of restating the Need.\n408\u21927. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409\u2192 the specifics (noise-aware acquisition, small batches, multi-objective).\n410\u21928. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411\u2192 (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412\u2192 above.)*\n413\u21929. **Evidence/results slot added** before the Closing, so the Conclusion is\n414\u2192 grounded in shown data rather than three hedged \"may\"s.\n415\u219210. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n416\u2192 Ball Bot visual; verified video links included.\n417\u219211. **Close bookends the opening** (the next lander's absorber, designed in\n418\u2192 weeks) before the broader humankind line.\n419\u219212. **Spelling**: Doumont (was \"Duomont\").", + "name": "read", + "tool_call_id": "call_93haurOMDGiH2mQFFbaMaYKn", + "info": { + "start_ts": 8147.331065886, + "end_ts": 8147.336467845 + } + }, + { + "role": "tool", + "content": " 1\u2192# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n 2\u2192\n 3\u2192Video: (assigned in #83)\n 4\u2192\n 5\u2192Transcribed from @me-madsen's handwritten notes\n 6\u2192([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7\u2192dated 2026-07-15).\n 8\u2192\n 9\u2192## Overview\n10\u2192\n11\u2192A presentation is all about the right content, displayed the right way, at the\n12\u2192right time.\n13\u2192\n14\u2192- Verbal communication is sequential.\n15\u2192- Message is *interpretation* of the information.\n16\u2192 - Ensure the audience can get the most out of what you're doing.\n17\u2192 - Don't just give them information \u2014 help them understand it, interpret it,\n18\u2192 and internalize it.\n19\u2192\n20\u2192## Doumont's suggested rules for a presentation\n21\u2192\n22\u21920. Ensure you have **messages** for your audience, not just words.\n23\u21921. **Adapt to your audience.**\n24\u2192 - Think like the audience, not the speaker.\n25\u21922. **Maximize signal-to-noise ratio.**\n26\u2192 - Reduce noise (animations, laser pointers, anything that pulls attention\n27\u2192 away from your message).\n28\u21923. **Use effective redundancy.**\n29\u2192 - Tell your message in word *and* on slide: someone deaf should understand\n30\u2192 your messages just as well as someone blind.\n31\u2192 - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32\u2192 date).\n33\u2192\n34\u2192## When creating slides\n35\u2192\n36\u2192- If the audience doesn't need it, why put it on the slides?\n37\u2192- People shouldn't have to count the items on a slide to know how many are\n38\u2192 there.\n39\u2192- Don't use your slides as your \"presentation notes.\"\n40\u2192 - Don't show your notes to the audience.\n41\u2192 - **A bad slide is worse than no slide at all.**\n42\u2192- **One message per slide** is a good rule of thumb.\n43\u2192 - Prioritize the *so what* (or *therefore what*) of your message.\n44\u2192- Make it easy to interpret the information on the slide:\n45\u2192 - 3\u20135 items at most.\n46\u2192 - Align the title left.\n47\u2192 - Do not clutter slides with repetitive or unnecessary information: logos,\n48\u2192 affiliation, date, slide numbers.\n49\u2192- Doumont's suggested workflow:\n50\u2192 1. **Start with the title.**\n51\u2192 - Use a full sentence in the title.\n52\u2192 - The title is the main message of the slide.\n53\u2192 - Make it easy to read: left-aligned, with intentional line breaks.\n54\u2192 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55\u2192- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56\u2192\n57\u2192## General preparation\n58\u2192\n59\u2192- Planning for audience, content, and structure matters more than materials\n60\u2192 (i.e., slides).\n61\u2192- Try using one sheet of paper for presentation preparation.\n62\u2192- It's most important that your presentation is:\n63\u2192 - well planned, structured, and delivered;\n64\u2192 - **if you don't have a lot of time to prepare, don't focus on making\n65\u2192 slides.**\n66\u2192- Practice the presentation a few times. Practice at least once *without* your\n67\u2192 slideshow!\n68\u2192- If doing a handout, keep it separate from the slides you're preparing.\n69\u2192- Impress the audience with your body.\n70\u2192 - Use gestures, not laser pointers.\n71\u2192 - \"Control\" the audience with your body \u2014 keep their attention on your\n72\u2192 presentation.\n73\u2192\n74\u2192## Impressions / what to apply to the IDETC presentation\n75\u2192\n76\u2192\n77\u2192\n78\u2192## Questions / things to discuss\n79\u2192\n80\u2192", + "name": "read", + "tool_call_id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "info": { + "start_ts": 8147.331133416, + "end_ts": 8147.338304015 + } + }, + { + "role": "tool", + "content": " 1\u2192\\documentclass[10pt]{article}\n 2\u2192\n 3\u2192% --- Packages ---\n 4\u2192\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5\u2192\\usepackage[hidelinks]{hyperref}\n 6\u2192\\usepackage{parskip}\n 7\u2192\\usepackage{graphicx}\n 8\u2192\\usepackage{caption}\n 9\u2192\\usepackage{titlesec}\n 10\u2192\\usepackage{wrapfig}\n 11\u2192\\usepackage[numbers,sort&compress]{natbib}\n 12\u2192\n 13\u2192% --- Formatting ---\n 14\u2192\\setlength{\\parskip}{0.2em}\n 15\u2192\\setlength{\\parindent}{0em}\n 16\u2192\\titleformat*{\\section}{\\large\\bfseries}\n 17\u2192\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18\u2192\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19\u2192\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20\u2192\\setlength{\\emergencystretch}{2em}\n 21\u2192\\setlength{\\intextsep}{0.2em}\n 22\u2192\\bibliographystyle{abbrvnat}\n 23\u2192\n 24\u2192\\begin{document}\n 25\u2192\n 26\u2192\\begin{center}\n 27\u2192{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28\u21923D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29\u2192\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30\u2192\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31\u2192\\end{center}\n 32\u2192\n 33\u2192\\vspace{0.4em}\n 34\u2192\n 35\u2192\\section*{Abstract}\n 36\u2192Tensegrity-inspired architectures---rigid struts suspended within a\n 37\u2192continuous flexible network---can exhibit tunable nonlinear\n 38\u2192force--displacement responses and favorable energy absorption per unit\n 39\u2192mass, motivating their use in lightweight energy-absorbing and protective\n 40\u2192structures\n 41\u2192\\citep{skelton2009tensegrity,pajunen2019design}.\n 42\u2192Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43\u2192struts and flexible (TPU) elements in a single build\n 44\u2192\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45\u2192space---strut geometry, tension-element cross-section, connectivity\n 46\u2192topology, and unit-cell tiling---is too large to explore by trial and\n 47\u2192error. We present a closed-loop experimental campaign that uses\n 48\u2192Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49\u2192to drive a design--print--test workflow operating directly on physical\n 50\u2192measurements, without relying on calibrated finite-element simulation\n 51\u2192for objective evaluation.\n 52\u2192\n 53\u2192\\section{Introduction and Motivation}\n 54\u2192\n 55\u2192\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56\u2192\\centering\n 57\u2192\\vspace{-0.4em}\n 58\u2192\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59\u2192\\captionsetup{font=small}\n 60\u2192\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61\u2192Candidate tensegrity-inspired unit cells are instantiated from a\n 62\u2192parameter vector, fabricated by multi-material FDM, and tested under\n 63\u2192quasi-static compression and drop-weight impact. Measured peak force,\n 64\u2192specific energy absorption (SEA), and compaction efficiency update a\n 65\u2192Gaussian-process surrogate that proposes the next batch of designs.}\n 66\u2192\\label{fig:overview}\n 67\u2192\\vspace{-0.4em}\n 68\u2192\\end{wrapfigure}\n 69\u2192\n 70\u2192Tensegrity structures combine compression-only members with a\n 71\u2192pre-tensioned tension network to achieve stiffness without rigid\n 72\u2192joinery~\\citep{skelton2009tensegrity}. Their\n 73\u2192nonlinear, often load-limiting force--displacement response makes them\n 74\u2192attractive for impact mitigation, packaging, and wearable protective\n 75\u2192gear~\\citep{pajunen2019design}. Idealized\n 76\u2192cable--strut tensegrities are difficult to manufacture at scale, but\n 77\u2192\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78\u21923D printing reproduce key tensegrity behaviors---including\n 79\u2192post-buckling stability and load-limiting force--displacement\n 80\u2192response~\\citep{pajunen2019design}---motivating our extension to\n 81\u2192multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82\u2192elements. PLA provides rigid struts; TPU provides flexible,\n 83\u2192rate-dependent tension elements whose viscoelasticity is expected to\n 84\u2192contribute hysteretic energy dissipation under impact loading,\n 85\u2192consistent with the energy-absorbing behavior reported for rigid--TPU\n 86\u2192multi-material prints~\\citep{khatri2024energy}.\n 87\u2192\n 88\u2192The challenge is that small geometric and topological changes can\n 89\u2192produce large changes in the measured response, and high-fidelity\n 90\u2192simulation of FDM specimens is computationally expensive and often\n 91\u2192struggles to capture physical defects such as interfacial slip and\n 92\u2192TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93\u2192trial-and-error sweeps over the joint design space are also infeasible:\n 94\u2192even modest parameter resolutions yield thousands of candidates, each\n 95\u2192requiring print and test time. We instead treat the physical\n 96\u2192experiment as the source of truth and use BO to select which specimens\n 97\u2192are most informative to fabricate next---an approach used in related\n 98\u2192closed-loop materials and structures\n 99\u2192campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100\u2192Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101\u2192set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102\u2192designs using parallel, noisy multi-objective BO driven by measured\n103\u2192$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104\u2192\n105\u2192\\section{Approach}\n106\u2192\\subsection{Closed-Loop Workflow}\n107\u2192Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108\u2192proceeds in four steps: (i)~a parameterized unit-cell design is\n109\u2192instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110\u2192is sliced and printed on a multi-material FDM system; (iii)~it is\n111\u2192tested under quasi-static compression and instrumented drop-weight\n112\u2192impact; and (iv)~the resulting performance metrics update a\n113\u2192Gaussian-process (GP) surrogate, which a batch acquisition function\n114\u2192queries to recommend the next set of candidates. Surrogate updating\n115\u2192and experiment selection are automated; specimen handling, slicing,\n116\u2192and test setup remain manual.\n117\u2192\n118\u2192\\subsection{Design Space}\n119\u2192We parameterize a family of unit cells using a core-wrapping\n120\u2192architecture in which rigid PLA struts are enclosed by continuous TPU\n121\u2192skins; the wrapping rationale follows multi-material FDM strategies in\n122\u2192which rigid panels are wrapped by stretchable soft material to prevent\n123\u2192delamination and improve interfacial bonding at the rigid--flexible\n124\u2192interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125\u2192comprises continuous geometric variables (strut diameter and length,\n126\u2192tension-element width and thickness), an integer strut count per unit\n127\u2192cell, and two categorical variables (connectivity topology, unit-cell\n128\u2192tiling pattern). Categorical choices are encoded explicitly rather\n129\u2192than embedded in a continuous metric, and bounds on the continuous\n130\u2192variables are chosen to remain within the printer's resolution and\n131\u2192overhang limits and to keep specimens in a common form factor for the\n132\u2192test fixtures.\n133\u2192\n134\u2192\\subsection{Surrogate and Acquisition}\n135\u2192The GP surrogate is trained on three measured performance metrics:\n136\u2192peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137\u2192(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138\u2192objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139\u2192likelihood with priors to avoid pathological length scales at small $n$.\n140\u2192Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141\u2192interfacial defects motivates our use of q-noisy expected hypervolume\n142\u2192improvement (qNEHVI), which explicitly accounts for observation noise\n143\u2192in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144\u2192expected-improvement variants. Print failures are treated as a\n145\u2192probabilistic feasibility constraint: the acquisition score for each\n146\u2192candidate is its qNEHVI improvement weighted by the GP-estimated\n147\u2192probability of successful fabrication, following recent constrained\n148\u2192multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149\u2192Where applicable, we use numerically stable log-space variants of\n150\u2192improvement-based acquisitions to mitigate vanishing-gradient\n151\u2192pathologies as data and constraints\n152\u2192accumulate~\\citep{ament2023logei}.\n153\u2192\n154\u2192\\section{Methods}\n155\u2192\\subsection{Fabrication}\n156\u2192Specimens are fabricated on a multi-material FDM system capable of\n157\u2192co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158\u2192Print parameters (temperature, line width, infill, retraction) are\n159\u2192held fixed within a batch to isolate the effect of the design\n160\u2192variables. Each specimen is weighed and dimensionally inspected prior\n161\u2192to testing so that mass and geometric deviations are available as\n162\u2192covariates.\n163\u2192\n164\u2192\\subsection{Mechanical Testing}\n165\u2192Quasi-static compression is performed on a screw-driven load frame at\n166\u2192a fixed nominal strain rate, with force--displacement recorded to\n167\u2192densification. Drop-weight impact testing uses an instrumented tup at\n168\u2192a fixed impact energy; transmitted force is captured at high sample\n169\u2192rate, and specimens are imaged before and after impact to document\n170\u2192failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171\u2192unit mass, and compaction efficiency $\\eta$, defined consistently\n172\u2192across the campaign. A control specimen from an earlier batch is\n173\u2192retested periodically to monitor TPU batch-to-batch and printer drift.\n174\u2192\n175\u2192\\section{Expected Outcomes and Discussion}\n176\u2192We will report how BO-selected designs evolve across iterations, how\n177\u2192GP surrogate predictions and uncertainties change as data accumulate,\n178\u2192and which Pareto-efficient trade-offs emerge between SEA and peak\n179\u2192transmitted force within the explored design space. We will also\n180\u2192discuss practical lessons for operating the closed-loop workflow,\n181\u2192including handling of print failures, batch-to-batch variability in\n182\u2192TPU response, the influence of the rigid--flexible interface on\n183\u2192energy absorption, and the exploration--exploitation balance of the\n184\u2192acquisition function under realistic experimental noise. We expect\n185\u2192the campaign to clarify where physical experimentation is genuinely\n186\u2192required versus where simpler analytical or multi-fidelity surrogate\n187\u2192shortcuts~\\citep{mo2023accelerated} could be\n188\u2192substituted in future work.\n189\u2192\n190\u2192\\section{Conclusion}\n191\u2192This work demonstrates a partially automated, experiment-driven\n192\u2192closed-loop pipeline for designing multi-material 3D-printed\n193\u2192tensegrity-inspired energy absorbers, with surrogate updating and\n194\u2192candidate selection automated and fabrication and testing kept manual.\n195\u2192By updating a GP surrogate directly from physical measurements, the\n196\u2192workflow avoids direct dependence on calibrated finite-element\n197\u2192simulation for objective evaluation while still providing principled,\n198\u2192multi-objective design recommendations. The same closed-loop pattern is\n199\u2192expected to transfer to other multi-material additively manufactured\n200\u2192architectures whose performance is dominated by hard-to-simulate\n201\u2192effects.\n202\u2192\n203\u2192{\\tiny\n204\u2192\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205\u2192\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206\u2192% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207\u2192% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208\u2192\\providecommand{\\doi}[1]{}\n209\u2192\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210\u2192\\bibliography{references}\n211\u2192}\n212\u2192\n213\u2192\\end{document}", + "name": "read", + "tool_call_id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "info": { + "start_ts": 8147.332633466, + "end_ts": 8147.339043095 + } + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\", \"offset\": 174, \"limit\": 100}", + "name": "read" + } + } + ], + "info": null + }, + { + "role": "tool", + "content": "175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "info": { + "start_ts": 8153.825818486, + "end_ts": 8153.828437556 + } + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8In1tJq32AD2tTyHX3tmIlLf", + "type": "function", + "function": { + "arguments": "{\"answer\": \"# Mock audience report: IDETC-CIE 2026 slide deck, Draft 1\\n\\n## Executive read\\n\\nThe visible deck has a clear visual spine: **motivation \\u2192 slow iteration \\u2192 Bayesian optimization \\u2192 multi-material printing \\u2192 impact testing \\u2192 closed loop \\u2192 future applications**. The strongest section is slides 8\\u201310, where fabrication, testing, and adaptive selection finally appear as one workflow.\\n\\nBut Draft 1 does not yet deliver the technical case promised by the agreed outline. The visible deck omits four things a DAC-10 audience will need:\\n\\n1. **Honest scope:** no visible slide says that the PLA\\u2013TPU T3 prism is a proxy system rather than flight hardware.\\n2. **A precise optimization problem:** the six variables, two objectives, force constraint, noise treatment, and fabrication failures are not intelligible on slide 7.\\n3. **Quantitative evidence:** the sole results slot is hidden and empty, yet slides 10 and 12 already claim acceleration and transfer.\\n4. **A measured conclusion:** the talk jumps from the workflow to speculative applications without showing a campaign ledger, comparison against a budget-matched baseline, or feasible Pareto front.\\n\\nThe slide titles generally follow Doumont\\u2019s full-sentence-message rule, but several messages outrun the evidence. The deck also departs substantially from the Draft 3 timing plan, which reserves **5 of 15 minutes for results**. As built, results receive zero visible slides.\\n\\n---\\n\\n# P0. The program manager in the audience\\n\\n## (a) My reaction in the room\\n\\nSlide 1 tells me the team wants to work faster, but it does not tell me what is being optimized or what has actually been built. The title sounds aspirational rather than traceable to a project deliverable. I immediately want the formal title from the abstract or a shorter version of it.\\n\\nThe Super Ball Bot footage on slide 2 is an effective hook. I understand why reusable impact attenuation matters. But the slide creates a scope risk: I am looking at planetary landing hardware while the actual work is a printed PLA\\u2013TPU T3 proxy. Because that distinction is not stated immediately, I may spend the rest of the talk evaluating the work against flight-readiness criteria the project never intended to meet.\\n\\nSlide 4 gives me the business problem, but not a baseline. \\u201cSlow and resource intensive\\u201d needs a number: hours per print-test cycle, number of factors, or the candidate count under a declared design of experiments. Without that, I cannot assess whether the proposed loop attacks the dominant schedule driver.\\n\\nSlide 7 is where I expect the project definition. Instead, \\u201c6 variables,\\u201d \\u201c2 types of input data,\\u201d and \\u201c2 objectives\\u201d read like inventory labels. I cannot reconstruct the statement of work from them. I need to see what is automated, what remains manual, what the force cap is, and what constitutes a successful campaign.\\n\\nSlides 8 and 9 make the project tangible. A labeled specimen and an actual drop test would build confidence. Yet \\u201csingle-build\\u201d and \\u201cno joining\\u201d need a process qualification caveat, and I need the measurement chain to be explicit. The abstract refers to an instrumented tup and force\\u2013time data; slide 9 says accelerometers. Those may be compatible, but the deck presently leaves the relationship unclear.\\n\\nSlide 10 is the right management view of the system, but \\u201csignificantly accelerate\\u201d is a result claim placed on a workflow diagram. At this point I ask, \\u201cCompared with what, by how much, and over how many specimens?\\u201d Then the deck skips the hidden results slide and goes directly to future applications. That is the point where my confidence drops sharply. The project is asking me to extrapolate before closing the evidence loop.\\n\\nSlide 12 is too early and too broad. A crutch tip and lattice can be legitimate next phases, but only after the team shows what the present campaign established and what technical risks remain. The blank questions slide gives me no durable take-home message.\\n\\n### PM stage-risk register\\n\\n| Risk | Likelihood | Consequence | Mitigation |\\n|---|---:|---:|---|\\n| Results are incomplete by conference lock | Medium to high | Critical: central claim becomes unsupported | Set a dated campaign freeze; predeclare a fallback message and minimum publishable result package |\\n| Embedded Super Ball Bot or drop-test video fails | Medium | Moderate: weak opening or broken pacing | Use local, trimmed files; retain a still-frame fallback on every video slide |\\n| Talk overruns because several videos and explanations are improvised | High | High: results or conclusion gets cut | Script videos to 10\\u201315 seconds total each; rehearse to 13:30; assign 5 minutes to results |\\n| Scope is mistaken for flight-hardware validation | High | High: credibility damage in Q&A | Put \\u201cPLA\\u2013TPU T3 proxy; not flight hardware\\u201d on slide 2 or the next slide |\\n| Acceleration claim lacks a budget-matched comparator | High | Critical for DAC review audience | Show Sobol/random or another predeclared baseline at the same physical-test budget |\\n| Measurement chain is challenged | Medium | High | Add a methods schematic and backup slide on calibration, sampling, filtering, and metric extraction |\\n| \\u201cNo joining\\u201d or \\u201cnear autonomous\\u201d is read as full automation | Medium | Moderate | List manual steps explicitly: slicing, handling, setup, and testing |\\n| Animations distract or fail to communicate without narration | Medium | Moderate | Prefer one short clip plus static annotated frames; follow Doumont\\u2019s signal-to-noise rule |\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey are building a loop that prints PLA\\u2013TPU tensegrity-like absorbers, drop-tests them, and lets Bayesian optimization choose the next print.\\u201d\\n\\nThat retains the basic workflow but loses the intended **dozens rather than hundreds** claim because the deck never demonstrates it. I would also probably describe the work as \\u201clander research,\\u201d which is a distortion caused by the missing proxy-system statement.\\n\\n## (c) My top three Q&A questions\\n\\n1. What is the dated plan to complete the campaign, baseline comparison, and repeat testing before the conference slide freeze?\\n2. Which exact operations are automated today, which remain manual, and what is the measured end-to-end cycle time per adaptive batch?\\n3. What evidence will justify \\u201cdozens rather than hundreds,\\u201d and what will you claim if the budget-matched baseline does not show an advantage?\\n\\n## (d) My most pointed objection\\n\\n**You are presenting a claimed acceleration without showing a completed campaign, a comparator, or even a visible results slide.**\\n\\nUnanswered, this is **critical**. I would not fund a scale-up phase from this deck. I might fund completion of the present campaign if the team showed a disciplined schedule, predefined success criteria, and a credible fallback.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 11.** It must become a real, scheduled results package, preferably three slides: campaign ledger, budget-matched baseline, and feasible Pareto front.\\n- **Hidden-slide decision:** **Unhide slide 11**, but not in its current empty form. The talk cannot make its principal claim without it.\\n\\n### PM slide-level punch list, ordered by schedule criticality\\n\\n**Must be fixed first**\\n\\n1. **Slide 11/results sequence:** assign owners and dates for data freeze, quality control, baseline computation, replicate analysis, and final plots. Define fallback wording now.\\n2. **Slide 2:** add the explicit scope line, \\u201cThis study uses a printed PLA\\u2013TPU T3 prism as a proxy for developing the workflow; it is not flight hardware.\\u201d\\n3. **Slide 7:** replace category counts with the actual optimization problem, test budget, batch size, objectives, constraint, and noise/failure treatment.\\n4. **Slide 10:** remove \\u201csignificantly\\u201d until slide 11 demonstrates a quantitative improvement; show automation boundaries and the loop\\u2019s measured cycle time.\\n5. **Slides 2 and 9:** download and trim media, test on the conference laptop, and put a static fallback image on each slide.\\n\\n**Then fix**\\n\\n6. **Slide 4:** quantify the current cost or candidate count and identify the bottleneck.\\n7. **Slide 8:** label PLA, TPU, interface, and any post-processing; qualify \\u201cno joining.\\u201d\\n8. **Slide 9:** reconcile accelerometers with the instrumented-tup description and show how force, energy absorption, and compaction efficiency are obtained.\\n9. **Slide 12:** convert the application collage into a conditional next-phase roadmap with gates.\\n10. **Slide 13:** end on the measured result and deployment rule, not a blank Q&A screen.\\n\\n**Nice to have**\\n\\n11. Replace decorative or looping media on slide 4 with one static process image unless it communicates a measured delay.\\n12. Add backup slides on Bayesian optimization implementation, finite-element-analysis positioning, repeatability, force-cap rationale, and print failures.\\n\\n---\\n\\n# P1. The skeptical Bayesian-optimization insider\\n\\n## (a) My reaction in the room\\n\\nThe opening is competent but generic for a design-automation conference. Slides 1\\u20134 set up an expensive physical experiment, which is a valid application class for Bayesian optimization (BO). I become interested when the presenter reaches slide 7, because that is where methodological seriousness should appear.\\n\\nInstead, slide 7 says only \\u201c6 variables,\\u201d \\u201c2 types of input data,\\u201d and \\u201c2 objectives.\\u201d I do not learn the variables, their types, how categorical choices are encoded, whether process parameters are optimized, how noisy observations enter the Gaussian processes, how replicates identify noise, how failures are modeled, or how the force cap enters the acquisition. The title says BO handles noisy data, but the visual does not substantiate that message.\\n\\nThere is also an unresolved specification conflict across the supplied materials. The agreed outline says the study jointly optimizes T3 geometry and FDM processing parameters. The abstract\\u2019s design vector lists four continuous geometric variables, one integer, and two categorical variables, apparently **seven variables**, while its fabrication section says print parameters are held fixed within a batch. The slide says **six variables**. A DAC audience will notice if the spoken details do not reconcile these versions.\\n\\nSlides 8\\u201310 explain why physical feedback is useful, but they do not establish why qNEHVI is the right algorithm or whether adaptive selection helped. The visible deck never names qNEHVI, the feasible Pareto front, the peak-force constraint, batch size, initialization, or test budget. Hiding slide 5 is reasonable if it is only a generic \\u201cwhat is BO?\\u201d slide copied from another talk. But slide 7 must then carry the application-specific methodological content.\\n\\nWhen slide 10 says the workflow \\u201csignificantly accelerate[s]\\u201d optimization, I wait for a performance-versus-budget curve. It never arrives. Slide 12 then generalizes to other applications. My default conclusion is exactly the skeptical one posed in the prompt: this appears to be off-the-shelf qNEHVI attached to an interesting physical apparatus, with no demonstrated algorithmic or empirical advantage yet.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cIt was an experimental self-driving-lab-style application of batch multi-objective BO to printed impact absorbers, but the talk did not show whether BO beat space-filling sampling.\\u201d\\n\\nThat differs from the intended message because I would not repeat \\u201cdozens, not hundreds\\u201d without a budget-matched baseline.\\n\\n## (c) My top three Q&A questions\\n\\n1. What are the exact decision variables and encodings, and why do the deck, scope statement, and abstract appear to disagree on whether there are six or seven variables and whether FDM parameters are optimized?\\n2. How is observation noise estimated: fixed noise, learned heteroscedastic noise, technical replicates, or repeated control specimens? What is the replicate allocation?\\n3. Against what budget-matched baseline do you evaluate qNEHVI, and what metric do you report: feasible hypervolume, best feasible objective values, or attainment probability versus physical tests?\\n\\n## (d) My most pointed objection\\n\\n**Nothing visible distinguishes a validated BO contribution from an untested application of standard qNEHVI.**\\n\\nUnanswered, this is **critical for DAC-10 reception**. Application novelty can be enough, but only if the physical campaign is rigorous and the adaptive method is compared fairly under the same test budget.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 7.** Replace the black-box inventory with a visual of posterior mean plus uncertainty \\u2192 constrained batch acquisition \\u2192 next physical tests, accompanied by the exact objectives and force constraint.\\n- **Hidden-slide decision:** **Keep slide 5 hidden** as a standalone generic BO tutorial. Merge one plain-language BO sentence into slide 7 and put qNEHVI details in backup.\\n\\n---\\n\\n# P2. The aerospace practitioner\\n\\n## (a) My reaction in the room\\n\\nThe Super Ball Bot hook works because I know the lineage. Reusable landing structures are interesting. But the title\\u2019s claim that tensegrity provides \\u201crobust, reusable solutions\\u201d is broader than what this deck demonstrates. A lab drop of a polymer proxy is not evidence of robustness in thermal extremes, vacuum, dust, radiation, repeated impacts, or off-axis terrain contact.\\n\\nThe deck never immediately tells me that the actual specimen is **tensegrity-inspired**, lacks ideal compression-only and tension-only membership, and is not intended to fly. That makes slide 2 vulnerable to looking like borrowed aerospace relevance.\\n\\nSlide 4 is plausible but too broad. Tensegrity design can be difficult, but aerospace development is not made slow only by design-space search. Verification, environmental qualification, uncertainty, packaging, guidance, and system integration matter. A faster specimen loop is useful, but it is one subsystem tool.\\n\\nI am not helped much by slide 7. I need BO translated into engineering behavior: it chooses the next small batch based on predicted performance and uncertainty while rejecting designs expected to violate a payload-force limit. The slide should name that limit and show the actual trade-off.\\n\\nSlides 8 and 9 are the first ones that answer \\u201cwhat did you build and how did you hit it?\\u201d A labeled specimen, deformation sequence, drop mass, speed or energy, orientation, and force measurement would hold my attention. Two simultaneous looping videos may not. I also want to know whether the structure rebounds, survives repeated drops, and retains its response, because the opening emphasizes reuse.\\n\\nSlide 10 is understandable. Slide 12 loses me because it jumps from a T3 polymer coupon to lattices and crutch tips without stating transfer conditions or technology gates.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey use an optimizer to choose which 3D-printed soft\\u2013rigid impact absorber to drop-test next, with the eventual motivation of reusable landing structures.\\u201d\\n\\nThat is fairly close, but I would deliberately downgrade \\u201ctensegrity\\u201d to \\u201ctensegrity-inspired absorber\\u201d and treat planetary landing as motivation only.\\n\\n## (c) My top three Q&A questions\\n\\n1. What makes the printed T3 specimen mechanically tensegrity-inspired rather than an ordinary compliant crush structure, and is it actually prestressed?\\n2. What impact energy, mass, orientation, and transmitted-force cap are used, and how do they relate to a payload-protection requirement?\\n3. How does performance change over repeated impacts, and what failure modes or permanent set limit the claimed reusability?\\n\\n## (d) My most pointed objection\\n\\n**The opening borrows flight relevance and reusability, but the visible deck never establishes the proxy boundary or shows repeated-impact evidence.**\\n\\nUnanswered, this is **highly damaging** to credibility with aerospace practitioners, though it does not invalidate the optimization workflow itself.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 2.** Keep the hook, but place a photograph of the actual T3 specimen beside the Super Ball Bot and state \\u201cmotivation, not demonstrated flight readiness.\\u201d\\n- **Hidden-slide decision:** **Keep slide 3 hidden.** A generic elbow-friction GIF would distract and oversimplify. Merge a precise tensegrity-mechanism annotation into slide 8 instead.\\n\\n---\\n\\n# P3. The additive-manufacturing/materials researcher\\n\\n## (a) My reaction in the room\\n\\nSlides 1\\u20134 give me a recognizable motivation for rapid experimental iteration. My interest rises on slide 8 because multi-material fused deposition modeling is where the claimed loop can succeed or fail.\\n\\nThe message \\u201csingle-build co-fabrication, with no joining of parts\\u201d is attractive, but it needs exact boundaries. Is there support removal, trimming, conditioning, manual tensioning, or fixture insertion? Does \\u201csingle build\\u201d mean one tool-change sequence on one machine, or truly co-deposited interfaces throughout the architecture? Are PLA and thermoplastic polyurethane (TPU) mechanically interlocked, chemically bonded, or merely in contact?\\n\\nThe abstract provides more useful information than the slide: a core-wrapping architecture is intended to resist delamination, specimens are weighed and dimensionally inspected, and a control is retested to monitor drift. Those are exactly the details the talk should surface. Slide 8\\u2019s planned printing video will not replace a labeled cross-section or interface image.\\n\\nSlide 9 raises repeatability questions. TPU is rate-, temperature-, moisture-, and history-dependent. \\u201cLimited, noisy data\\u201d on slide 7 acknowledges noise but does not show the control plan. I need batch identity, drying/conditioning, build position, print orientation, machine state, interface failures, and replicates.\\n\\nThe deck also appears inconsistent about process parameters. The agreed scope says FDM processing parameters are optimized with structure, but the abstract says temperature, line width, infill, and retraction are held fixed within a batch. This could mean they vary between batches or are controlled rather than optimized, but the deck must say which.\\n\\nI like the physical loop on slide 10. I do not yet accept that multi-material printing \\u201csupplies data fast\\u201d until cycle time and failure rate are shown.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey co-print PLA and TPU impact specimens and use each mechanical test to guide the next design batch.\\u201d\\n\\nI would probably omit \\u201cno joining\\u201d and \\u201cdozens rather than hundreds\\u201d until I saw interface qualification, failure accounting, and campaign data.\\n\\n## (c) My top three Q&A questions\\n\\n1. How is the PLA\\u2013TPU interface designed and qualified, and what fraction of specimens fail by delamination or print defects?\\n2. Which material, machine, conditioning, and build-position variables are controlled, measured as covariates, or optimized?\\n3. How many true replicates and repeated control specimens are used to separate BO improvement from printer drift and TPU batch variation?\\n\\n## (d) My most pointed objection\\n\\n**The deck treats co-fabrication as an enabling fact without showing interface integrity, repeatability, or the remaining manual processing steps.**\\n\\nUnanswered, this is **highly damaging** because print variation is part of both the physical mechanism and the statistical noise model.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 8.** Replace or supplement the printing clip with an annotated specimen and interface detail: PLA struts, continuous TPU network, deposition/wrapping strategy, prestress status, post-processing, and observed failure modes.\\n- **Hidden-slide decision:** **Keep slide 3 hidden.** Its proposed generic GIFs do not answer the materials questions. Put the relevant deformation mechanism on slide 8.\\n\\n---\\n\\n# P4. The first-year graduate student\\n\\n## (a) My reaction in the room\\n\\nSlide 1 sounds positive, and slide 2 gives me a memorable reason to care. I understand that the lander survives an impact because the structure itself deforms. But I do not yet know what \\u201ctensegrity\\u201d means, and the hidden slide 3 would not necessarily fix that: an elbow GIF and a squishing GIF could leave me with an inaccurate friction-versus-elasticity story.\\n\\nSlide 4 is easy to follow. The three words \\u201cIteration, Building, Testing\\u201d form a simple problem statement, although the images need to make their relationship obvious.\\n\\nI get lost on slide 7. Six variables of what? What are the two input-data types? Are the two objectives \\u201cabsorb energy\\u201d and \\u201creduce force,\\u201d or something else? I do not know what a black box means in this setting. The title says Bayesian optimization works with noisy data, but I have not been shown how it decides anything.\\n\\nSlide 8 helps because I can see printing. Still, I need labels that distinguish the rigid and flexible parts and one sentence explaining why the object is \\u201ctensegrity-inspired.\\u201d Slide 9 is intuitive if the clips are synchronized and annotated. I would understand even better if the slide drew the metric extraction directly on a force\\u2013time or force\\u2013displacement trace.\\n\\nSlide 10 is the clearest slide in the deck. Printing \\u2192 testing \\u2192 optimizer \\u2192 next print is the idea I will remember. But it arrives after the technical terms rather than before them. I would benefit from seeing a version of this loop before slide 7 and then revisiting it after the methods.\\n\\nSlide 12 feels like a new presentation. I have not seen whether the current system worked, so the crutch tip and lattice do not feel earned. Then the talk ends on a blank question slide rather than telling me the answer.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey repeatedly print and drop-test flexible 3D structures, and a machine-learning model decides what to print next.\\u201d\\n\\nMy retelling loses the two objectives, the force constraint, and the \\u201cdozens rather than hundreds\\u201d quantitative point. I might incorrectly call the method generic machine learning rather than Bayesian optimization.\\n\\n## (c) My top three Q&A questions\\n\\n1. What exactly is tensegrity, and which parts of your printed specimen are in tension and compression?\\n2. What are the six things the optimizer can change, and what are the two scores it is trying to improve?\\n3. How does the optimizer know whether to try something uncertain rather than the design it currently thinks is best?\\n\\n## (d) My most pointed objection\\n\\n**The deck names the ingredients before giving me a plain-language map of how they work together.**\\n\\nUnanswered, this is **moderately damaging**. I retain the loop but lose the actual research question and technical contribution.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 7.** It needs one concrete candidate, predicted performance plus uncertainty, and the next selected batch, not abstract counts around a black box.\\n- **Hidden-slide decision:** **Unhide neither slide 3 nor slide 5 as written.** If forced to choose one, **keep slide 5 hidden** and integrate its one-sentence concept into a redesigned slide 7. Generic explanation would add time without restoring the missing optimization details.\\n\\n---\\n\\n# P5. The finite-element-analysis veteran\\n\\n## (a) My reaction in the room\\n\\nThe opening is fine until slide 4 implies that physical iteration is the central route around slow design. I start waiting for the argument about simulation. Because slide 6 is hidden, the deck never states the agreed, defensible position: a sufficiently calibrated model may cost more than it saves for this campaign, particularly when interfaces, defects, rate dependence, contact, and millisecond shock transients dominate.\\n\\nThat omission avoids an overt attack on finite-element analysis (FEA), which is good, but it also creates a logical hole. Why is physical BO the chosen source of objective values? Why not a hybrid strategy, reduced-order model, analytical screening, or multi-fidelity optimization? The deck merely proceeds from \\u201cdesign is slow\\u201d to \\u201cBO makes experimentation affordable.\\u201d\\n\\nI object to any suggestion that experimental data are automatically \\u201creal\\u201d and simulations are not. Measurements also involve sensor bandwidth, filtering, fixture dynamics, uncertainty, and model-based transformations. Slide 9\\u2019s \\u201caccelerometers gather real data\\u201d wording is particularly vulnerable. A force estimate derived from acceleration still rests on calibration and assumptions.\\n\\nSlide 10 is a useful experimental workflow. It should be presented as a scoped engineering choice, not a universal replacement for simulation. Slide 12\\u2019s transfer claim needs the condition from the outline: use this pattern where tests are authoritative, costly, the design space is mixed, and goals conflict.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey bypass a calibrated finite-element model and optimize directly against impact tests because the printed interfaces and transient response are difficult to model economically.\\u201d\\n\\nThat is close to the intended experiment-first rationale, but I had to infer it; the visible slides do not say it clearly.\\n\\n## (c) My top three Q&A questions\\n\\n1. What evidence shows that calibrating a useful simulation would cost more than it saves for this campaign, rather than merely being inconvenient?\\n2. How do you verify the measured peak transmitted force, including sensor bandwidth, fixture dynamics, filtering, and uncertainty?\\n3. Why exclude analytical or low-fidelity screening from a multi-fidelity BO strategy, especially for infeasible geometries and gross structural trends?\\n\\n## (d) My most pointed objection\\n\\n**The deck uses experiment-first optimization without stating a scoped cost-and-validity argument or acknowledging where simulation could still add value.**\\n\\nUnanswered, this is **highly damaging in Q&A**. It makes the methodology look ideologically anti-simulation rather than economically reasoned.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 4.** It should become the two-sided decision: exhaustive physical search is too costly, while campaign-grade calibration for the hardest responses is also costly; therefore choose physical tests adaptively.\\n- **Hidden-slide decision:** **Merge slide 6 into slide 4**, rather than unhide it as written. Keep a fuller evidence-backed simulation/experiment comparison as backup.\\n\\n---\\n\\n# P6. The friendly industry generalist\\n\\n## (a) My reaction in the room\\n\\nThe opening video is memorable, and I like the practical goal of shortening build\\u2013test iteration. Slide 4 is easy to understand. My attention slips on slide 7 because the counts and black-box symbol do not tell a story. I do not need the name qNEHVI, but I need to know that the software balances trying promising designs with learning from uncertain ones and respects a force limit.\\n\\nSlides 8\\u201310 are the strongest run. If the presenter shows one labeled specimen, one clean drop clip, and one loop diagram, I can see how I might use the pattern in another product-development problem. I want a cycle-time number and an indication of what software or human work is required.\\n\\nSlide 12 dilutes the message by offering several destinations before proving the present result. The ending is weak. A blank Q&A slide discards the chance to leave the audience with the workflow, the best specimen, and a measured improvement.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cPrint it, break or drop it, feed the measurements back, and let the optimizer pick the next batch instead of running a huge test matrix.\\u201d\\n\\nThat captures the intended practical pattern, but I would probably overgeneralize it beyond the specific conditions under which it is useful.\\n\\n## (c) My top three Q&A questions\\n\\n1. How many prints and calendar days did it take to reach a useful design compared with your previous workflow?\\n2. What parts of this system could another engineering team reuse without building the same drop tower or writing a custom optimizer?\\n3. What happens when a print fails or a test produces an outlier?\\n\\n## (d) My most pointed objection\\n\\n**I understand the loop, but the deck never gives me the one number or result that proves it was worth building.**\\n\\nUnanswered, this is **moderately to highly damaging**. I would remember the concept but not have a reason to adopt or sponsor it.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 13.** Replace the blank Q&A screen with the best measured result, the closed-loop diagram, and one deployment rule; retain a small \\u201cQuestions?\\u201d label.\\n- **Hidden-slide decision:** **Unhide slide 11** once populated. Results are more valuable than another explanatory slide.\\n\\n---\\n\\n# Synthesis\\n\\n## Cross-persona themes\\n\\n1. **The scope boundary is missing.** The Super Ball Bot is a good hook, but every persona risks interpreting a PLA\\u2013TPU proxy as a claim about flight hardware.\\n2. **Slide 7 is the weakest visible technical slide.** It serves neither experts nor novices. Counts such as \\u201c6 variables\\u201d and \\u201c2 objectives\\u201d are information, not a message.\\n3. **The main claim currently lacks evidence.** \\u201cSignificantly accelerate,\\u201d \\u201csupplies data fast,\\u201d and \\u201cdozens, not hundreds\\u201d require a campaign ledger and a budget-matched comparison.\\n4. **The results omission breaks the story.** The deck follows the outline through the workflow, then skips the outline\\u2019s largest time block and moves directly to future work.\\n5. **The physical system needs more definition.** The audience needs a labeled specimen, a clear explanation of \\u201ctensegrity-inspired,\\u201d interface design, remaining manual steps, and the measurement chain.\\n6. **Noise must be operational rather than rhetorical.** Saying the data are noisy is insufficient. Replicates, periodic controls, drift, failures, and uncertainty should be shown.\\n7. **Experiment-first needs a scoped rationale.** The deck should not dismiss FEA, but it should explain why adaptive physical testing is the economical source of objective values for this campaign.\\n8. **The close is not a conclusion.** Future applications and a blank Q&A slide do not substitute for the measured finding and its conditions of transfer.\\n9. **Media are a communication and schedule risk.** Short clips can work, but multiple looping videos violate the signal-to-noise principle and create failure modes.\\n10. **The project specification needs reconciliation.** The supplied materials differ on six versus seven variables, whether FDM process parameters are optimized, and whether peak force is a constraint or part of the Pareto trade-off.\\n\\n## Five highest-priority slide-level revisions\\n\\n### 1. Slide 11: replace one empty slot with a three-slide quantitative results sequence\\n\\nUse full-sentence titles tied to observed data:\\n\\n- **11A:** \\u201cThe campaign used **N** specimens in **B** adaptive batches, including **R** replicates and **F** failed prints.\\u201d\\n- **11B:** \\u201cAt the same physical-test budget, adaptive selection reached **[measured feasible-performance result]** faster than **[predeclared Sobol/random baseline]**.\\u201d\\n- **11C:** \\u201cThe measured feasible Pareto front trades specific energy absorption against compaction efficiency while respecting a **[value and unit]** peak-force cap.\\u201d\\n\\nShow uncertainty, replicate spread, infeasible points, and representative specimen photographs or traces. If the campaign cannot support the baseline claim, change the talk\\u2019s main message to: **\\u201cWe evaluate whether noise-aware multi-objective Bayesian optimization can reduce the physical-test budget.\\u201d**\\n\\n### 2. Slide 7: state the actual constrained optimization problem\\n\\nReplace the current black-box/count design with:\\n\\n- the named decision variables and their types;\\n- a small batch entering the printer/test loop;\\n- objectives: maximize specific energy absorption and compaction efficiency;\\n- constraint: peak transmitted force below a stated cap;\\n- qNEHVI\\u2019s role in one plain sentence: it chooses a small next batch likely to expand the feasible trade-off frontier while accounting for uncertainty and noisy tests;\\n- the declared test budget, initialization size, and batch size.\\n\\nResolve six versus seven variables and whether processing parameters are decision variables before finalizing this slide.\\n\\n### 3. Slide 2: put the proxy boundary beside the hook\\n\\nKeep the Super Ball Bot clip, but add an image of the actual specimen and the visible sentence:\\n\\n> **\\u201cHere, a printed PLA\\u2013TPU T3 prism is a proxy for developing the workflow, not flight hardware.\\u201d**\\n\\nChange or qualify \\u201crobust, reusable solutions\\u201d unless the present campaign includes repeat-impact evidence.\\n\\n### 4. Slide 4: turn the vague need into the two-sided engineering decision\\n\\nRetitle it:\\n\\n> **\\u201cBecause exhaustive testing and campaign-grade model calibration are both costly, each physical specimen must be chosen for its information value.\\u201d**\\n\\nAdd one quantified physical-search estimate and one scoped calibration challenge. Do not say simulation is untrustworthy. State that FEA and lower-fidelity models remain possible screening or future multi-fidelity tools.\\n\\n### 5. Slides 10 and 13: separate the demonstrated loop from the conclusion\\n\\n- **Slide 10:** retitle to **\\u201cThe closed loop turns each measured test into the next small batch of specimens.\\u201d** Remove \\u201csignificantly accelerate\\u201d until the results demonstrate it. Mark automated and manual stages.\\n- **Slide 13:** retain the closed-loop thumbnail, add the best quantitative result, and state the deployment rule: **use the loop when tests are authoritative, each test is costly, the design space is mixed, and performance goals conflict.** Add \\u201cQuestions?\\u201d without clearing the conclusion from the screen.\\n\\n## Hidden-slide adjudication\\n\\n| Hidden slide | Recommendation | Rationale |\\n|---|---|---|\\n| **3: tensegrity usefulness/mechanism** | **Merge into slide 8** | The generic elbow and squishing GIFs add noise and risk an inaccurate mechanism; an annotated actual specimen and deformation sequence are stronger. |\\n| **5: generic BO explanation** | **Merge into slide 7; keep original as backup or delete** | DAC experts do not need a generic advocacy slide, but novices need one plain-language sentence and a concrete acquisition visual. |\\n| **6: specimen information value/experiment-first** | **Merge into slide 4; keep evidence-rich version as backup** | The argument is structurally necessary, but the current title is vague and the visual does not support the scoped calibration-versus-campaign-cost decision. |\\n| **11: results** | **Unhide and expand to three slides** | Results are the evidentiary center of the talk and were allocated five minutes in the agreed outline. An empty or hidden results slot makes the main claim untenable. |\\n\\n## Visible-slide claims needing evidence, hedging, or backup\\n\\n| Slide | Claim | Required treatment |\\n|---|---|---|\\n| **1** | \\u201cBuild better tensegrity structures faster\\u201d | Define \\u201cbetter\\u201d with the two objectives and force constraint; support \\u201cfaster\\u201d with the results comparison. |\\n| **2** | Tensegrity is robust and reusable for planetary landings | Cite the Super Ball Bot source; distinguish concept evidence from this study; provide repeat-impact evidence or hedge reusability. |\\n| **2** | Parachutes and retrorockets struggle in the stated conditions | Cite an authoritative aerospace source if retained in narration; avoid implying universal inadequacy. |\\n| **4** | Current tensegrity design is slow and resource intensive | Quantify candidate count, print/test time, hand-assembly burden, or calibration effort. |\\n| **7** | BO makes experiments affordable with limited, noisy data | Show campaign budget, noise characterization, replicates, baseline, and performance versus tests. Prepare qNEHVI and Gaussian-process details in backup. |\\n| **7** | Six variables, two input-data types, two objectives | Reconcile with the apparent seven-variable abstract specification and identify every quantity. Clarify \\u201cinput-data types.\\u201d |\\n| **8** | Multi-material AM supplies data fast | Report print-to-result cycle time and failure rate. |\\n| **8** | Single-build co-fabrication with no joining | Show interface and post-processing; use \\u201cno joining of PLA and TPU members\\u201d if other manual operations remain. |\\n| **9** | Accelerometers gather the relevant impact data | Show sensor locations and the derivation of transmitted force, specific energy absorption, and compaction efficiency. Reconcile this wording with the abstract\\u2019s instrumented-tup description. |\\n| **10** | The loop significantly accelerates optimization | Remove \\u201csignificantly\\u201d unless a defined baseline and uncertainty support it. Statistical significance should not be implied casually. |\\n| **12** | Workflow will transfer to lattices and other applications | Present as conditional future work with transfer criteria, not an achieved result. Attribute any external crutch-tip image and claim. |\\n\\n### Prepared backup slides\\n\\n1. Full design-variable table, bounds, encoding, and fixed versus optimized process parameters.\\n2. qNEHVI implementation, Gaussian-process assumptions, constraint treatment, batch size, and acquisition settings.\\n3. Noise plan: replicates, periodic control, heteroscedasticity, drift, outlier policy, and uncertainty propagation.\\n4. Budget-matched baseline definition and evaluation metric.\\n5. Force-cap rationale and full objective definitions.\\n6. Sensor chain, calibration, sample rate, filtering, fixture dynamics, and metric extraction.\\n7. PLA\\u2013TPU interface, conditioning, build layout, dimensional/mass deviations, and print-failure modes.\\n8. Scoped FEA rationale with citations or internal validation; possible analytical or multi-fidelity extensions.\\n9. Automation map and cycle-time breakdown.\\n10. Repeated-impact behavior if reusability remains in the opening.\\n\\n## Merged, ordered presenter TODO list\\n\\n### Must fix before the talk\\n\\n1. **Freeze the scientific specification.** Reconcile the number and type of decision variables, whether FDM parameters are optimized or controlled, the two objectives, the force constraint, treatment of print failures, and the measurement chain. Use one formulation across slides, abstract, paper, and Q&A.\\n2. **Set a results completion plan.** Establish dates and owners for campaign completion, repeat tests, data quality review, baseline analysis, and slide freeze. Predeclare the fallback claim if the baseline does not support \\u201cdozens rather than hundreds.\\u201d\\n3. **Build the three-slide results sequence.** Campaign ledger \\u2192 budget-matched baseline \\u2192 measured feasible Pareto front. Allocate approximately five minutes, as the agreed outline requires.\\n4. **Add the proxy statement to slide 2.** State plainly that the PLA\\u2013TPU T3 specimen is a workflow proxy and not flight hardware.\\n5. **Redesign slide 7.** Show the exact constrained multi-objective problem, test budget, small batches, uncertainty, noisy observations, and the role of qNEHVI.\\n6. **Remove unsupported result language.** Until supported, delete \\u201csignificantly\\u201d from slide 10 and avoid \\u201cdozens rather than hundreds\\u201d in the title, narration, or close.\\n7. **Clarify experiment-first positioning.** Merge the information-value argument into slide 4 and prepare an evidence-backed FEA backup slide.\\n8. **Make the experiment auditable.** On slide 9 or backup, reconcile accelerometers and the instrumented tup; show how all three performance metrics are calculated and how uncertainty is handled.\\n9. **De-risk every video.** Store local trimmed files, disable distracting loops, test them on presentation hardware, and include static fallback frames.\\n10. **Rehearse to 13:30\\u201314:00.** Protect the results and conclusion from being cut. Practice once without slides, consistent with Doumont\\u2019s preparation advice.\\n\\n### Should fix\\n\\n11. **Replace slide 8\\u2019s generic print emphasis with mechanism and materials evidence.** Label PLA, TPU, interface strategy, prestress status, deformation, post-processing, and failure modes.\\n12. **Quantify slide 4.** Give at least one campaign-relevant cost, duration, or candidate-count estimate.\\n13. **Show automation boundaries on slide 10.** Candidate selection and surrogate updating may be automated; slicing, handling, test setup, and testing remain manual according to the abstract.\\n14. **Convert slide 12 to a gated roadmap.** Separate demonstrated T3-proxy results from future lattices, flight materials, and terrestrial applications.\\n15. **Turn slide 13 into the conclusion.** Leave the audience with the best result and transfer rule while taking questions.\\n16. **Prepare technical backup slides.** Prioritize baseline fairness, noise, force-cap rationale, print failures, materials repeatability, and FEA positioning.\\n\\n### Polish\\n\\n17. Replace the slide 1 title with a specific message, such as **\\u201cPhysical Bayesian optimization searches multi-material tensegrity absorbers one small test batch at a time.\\u201d**\\n18. Left-align titles and use intentional line breaks; check every slide at six-per-page scale.\\n19. Use one purposeful visual per slide. Avoid decorative loading or elbow animations.\\n20. Standardize terminology: \\u201cBayesian optimization,\\u201d not \\u201cBays Opt\\u201d; \\u201ctensegrity-inspired\\u201d where appropriate; define specific energy absorption once.\\n21. Add citations directly beside borrowed footage, images, and quantitative claims.\\n\\n## Who the deck serves best and worst\\n\\n- **Best served:** **P6, the friendly industry generalist.** The visible sequence is visually intuitive, practical, and easy to reduce to \\u201cprint \\u2192 test \\u2192 learn \\u2192 repeat.\\u201d\\n- **Worst served:** **P1, the skeptical BO insider.** The current deck omits nearly every item needed to judge a DAC-10 contribution: exact problem formulation, budget, baseline, noise model, constraint handling, and empirical adaptive advantage.\\n\\nThat is **not the right trade-off for DAC-10**. The deck should remain accessible to P4 and P6, but its technical center must satisfy P1 without turning into a BO lecture. One rigorous optimization slide, three quantitative results slides, and targeted backup material would achieve that balance.\\n\\n## Predicted reception\\n\\n- **As is: 4/10.** The physical workflow and application are memorable, but the absent scope statement, empty results section, vague BO slide, and unsupported acceleration claim leave the DAC contribution unproven.\\n- **After the must-fixes: 8/10.** A scoped proxy claim, reconciled methods, fair budget-matched comparison, feasible Pareto front, and measured conclusion would turn the same visual story into a credible design-optimization application talk.\\n\\n## Discretionary analytical decisions\\n\\n- Evaluated only the visible slide order for the simulated in-room reactions, while using hidden slides and presenter notes to judge revision options.\\n- Treated planned placeholder visuals as if implemented according to the presenter\\u2019s notes, but judged the selected visual concept and associated stage risk rather than absent graphic quality.\\n- Prioritized the Draft 3 outline over conflicting Draft 1 wording because the prompt identifies it as the agreed story arc, scope framing, results plan, and timing plan.\\n- Rated objections qualitatively as moderate, high, or critical rather than assigning unsupported numerical probabilities.\\n- Recommended three results slides rather than preserving one reserved slot because the agreed outline specifies three distinct evidentiary products and allocates five minutes to results.\\n- Recommended merging hidden slides 3, 5, and 6 rather than simply unhiding them because their necessary ideas can be conveyed with higher signal-to-noise on existing slides.\\n- Treated the six-versus-seven-variable and optimized-versus-fixed-process-parameter differences as unresolved specification conflicts requiring reconciliation, rather than inferring which document is correct.\\n- Assessed claims against the supplied deck extraction, Draft 3 outline, Doumont notes, and submitted abstract only; no independent validation of cited literature or unseen experimental data was attempted.\"}", + "name": "submit_answer" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "tool", + "content": "175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "info": { + "start_ts": 8153.825818486, + "end_ts": 8153.828437556 + } + } + ], + "next_observation": [ + { + "role": "tool", + "content": "# Mock audience report: IDETC-CIE 2026 slide deck, Draft 1\n\n## Executive read\n\nThe visible deck has a clear visual spine: **motivation \u2192 slow iteration \u2192 Bayesian optimization \u2192 multi-material printing \u2192 impact testing \u2192 closed loop \u2192 future applications**. The strongest section is slides 8\u201310, where fabrication, testing, and adaptive selection finally appear as one workflow.\n\nBut Draft 1 does not yet deliver the technical case promised by the agreed outline. The visible deck omits four things a DAC-10 audience will need:\n\n1. **Honest scope:** no visible slide says that the PLA\u2013TPU T3 prism is a proxy system rather than flight hardware.\n2. **A precise optimization problem:** the six variables, two objectives, force constraint, noise treatment, and fabrication failures are not intelligible on slide 7.\n3. **Quantitative evidence:** the sole results slot is hidden and empty, yet slides 10 and 12 already claim acceleration and transfer.\n4. **A measured conclusion:** the talk jumps from the workflow to speculative applications without showing a campaign ledger, comparison against a budget-matched baseline, or feasible Pareto front.\n\nThe slide titles generally follow Doumont\u2019s full-sentence-message rule, but several messages outrun the evidence. The deck also departs substantially from the Draft 3 timing plan, which reserves **5 of 15 minutes for results**. As built, results receive zero visible slides.\n\n---\n\n# P0. The program manager in the audience\n\n## (a) My reaction in the room\n\nSlide 1 tells me the team wants to work faster, but it does not tell me what is being optimized or what has actually been built. The title sounds aspirational rather than traceable to a project deliverable. I immediately want the formal title from the abstract or a shorter version of it.\n\nThe Super Ball Bot footage on slide 2 is an effective hook. I understand why reusable impact attenuation matters. But the slide creates a scope risk: I am looking at planetary landing hardware while the actual work is a printed PLA\u2013TPU T3 proxy. Because that distinction is not stated immediately, I may spend the rest of the talk evaluating the work against flight-readiness criteria the project never intended to meet.\n\nSlide 4 gives me the business problem, but not a baseline. \u201cSlow and resource intensive\u201d needs a number: hours per print-test cycle, number of factors, or the candidate count under a declared design of experiments. Without that, I cannot assess whether the proposed loop attacks the dominant schedule driver.\n\nSlide 7 is where I expect the project definition. Instead, \u201c6 variables,\u201d \u201c2 types of input data,\u201d and \u201c2 objectives\u201d read like inventory labels. I cannot reconstruct the statement of work from them. I need to see what is automated, what remains manual, what the force cap is, and what constitutes a successful campaign.\n\nSlides 8 and 9 make the project tangible. A labeled specimen and an actual drop test would build confidence. Yet \u201csingle-build\u201d and \u201cno joining\u201d need a process qualification caveat, and I need the measurement chain to be explicit. The abstract refers to an instrumented tup and force\u2013time data; slide 9 says accelerometers. Those may be compatible, but the deck presently leaves the relationship unclear.\n\nSlide 10 is the right management view of the system, but \u201csignificantly accelerate\u201d is a result claim placed on a workflow diagram. At this point I ask, \u201cCompared with what, by how much, and over how many specimens?\u201d Then the deck skips the hidden results slide and goes directly to future applications. That is the point where my confidence drops sharply. The project is asking me to extrapolate before closing the evidence loop.\n\nSlide 12 is too early and too broad. A crutch tip and lattice can be legitimate next phases, but only after the team shows what the present campaign established and what technical risks remain. The blank questions slide gives me no durable take-home message.\n\n### PM stage-risk register\n\n| Risk | Likelihood | Consequence | Mitigation |\n|---|---:|---:|---|\n| Results are incomplete by conference lock | Medium to high | Critical: central claim becomes unsupported | Set a dated campaign freeze; predeclare a fallback message and minimum publishable result package |\n| Embedded Super Ball Bot or drop-test video fails | Medium | Moderate: weak opening or broken pacing | Use local, trimmed files; retain a still-frame fallback on every video slide |\n| Talk overruns because several videos and explanations are improvised | High | High: results or conclusion gets cut | Script videos to 10\u201315 seconds total each; rehearse to 13:30; assign 5 minutes to results |\n| Scope is mistaken for flight-hardware validation | High | High: credibility damage in Q&A | Put \u201cPLA\u2013TPU T3 proxy; not flight hardware\u201d on slide 2 or the next slide |\n| Acceleration claim lacks a budget-matched comparator | High | Critical for DAC review audience | Show Sobol/random or another predeclared baseline at the same physical-test budget |\n| Measurement chain is challenged | Medium | High | Add a methods schematic and backup slide on calibration, sampling, filtering, and metric extraction |\n| \u201cNo joining\u201d or \u201cnear autonomous\u201d is read as full automation | Medium | Moderate | List manual steps explicitly: slicing, handling, setup, and testing |\n| Animations distract or fail to communicate without narration | Medium | Moderate | Prefer one short clip plus static annotated frames; follow Doumont\u2019s signal-to-noise rule |\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey are building a loop that prints PLA\u2013TPU tensegrity-like absorbers, drop-tests them, and lets Bayesian optimization choose the next print.\u201d\n\nThat retains the basic workflow but loses the intended **dozens rather than hundreds** claim because the deck never demonstrates it. I would also probably describe the work as \u201clander research,\u201d which is a distortion caused by the missing proxy-system statement.\n\n## (c) My top three Q&A questions\n\n1. What is the dated plan to complete the campaign, baseline comparison, and repeat testing before the conference slide freeze?\n2. Which exact operations are automated today, which remain manual, and what is the measured end-to-end cycle time per adaptive batch?\n3. What evidence will justify \u201cdozens rather than hundreds,\u201d and what will you claim if the budget-matched baseline does not show an advantage?\n\n## (d) My most pointed objection\n\n**You are presenting a claimed acceleration without showing a completed campaign, a comparator, or even a visible results slide.**\n\nUnanswered, this is **critical**. I would not fund a scale-up phase from this deck. I might fund completion of the present campaign if the team showed a disciplined schedule, predefined success criteria, and a credible fallback.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 11.** It must become a real, scheduled results package, preferably three slides: campaign ledger, budget-matched baseline, and feasible Pareto front.\n- **Hidden-slide decision:** **Unhide slide 11**, but not in its current empty form. The talk cannot make its principal claim without it.\n\n### PM slide-level punch list, ordered by schedule criticality\n\n**Must be fixed first**\n\n1. **Slide 11/results sequence:** assign owners and dates for data freeze, quality control, baseline computation, replicate analysis, and final plots. Define fallback wording now.\n2. **Slide 2:** add the explicit scope line, \u201cThis study uses a printed PLA\u2013TPU T3 prism as a proxy for developing the workflow; it is not flight hardware.\u201d\n3. **Slide 7:** replace category counts with the actual optimization problem, test budget, batch size, objectives, constraint, and noise/failure treatment.\n4. **Slide 10:** remove \u201csignificantly\u201d until slide 11 demonstrates a quantitative improvement; show automation boundaries and the loop\u2019s measured cycle time.\n5. **Slides 2 and 9:** download and trim media, test on the conference laptop, and put a static fallback image on each slide.\n\n**Then fix**\n\n6. **Slide 4:** quantify the current cost or candidate count and identify the bottleneck.\n7. **Slide 8:** label PLA, TPU, interface, and any post-processing; qualify \u201cno joining.\u201d\n8. **Slide 9:** reconcile accelerometers with the instrumented-tup description and show how force, energy absorption, and compaction efficiency are obtained.\n9. **Slide 12:** convert the application collage into a conditional next-phase roadmap with gates.\n10. **Slide 13:** end on the measured result and deployment rule, not a blank Q&A screen.\n\n**Nice to have**\n\n11. Replace decorative or looping media on slide 4 with one static process image unless it communicates a measured delay.\n12. Add backup slides on Bayesian optimization implementation, finite-element-analysis positioning, repeatability, force-cap rationale, and print failures.\n\n---\n\n# P1. The skeptical Bayesian-optimization insider\n\n## (a) My reaction in the room\n\nThe opening is competent but generic for a design-automation conference. Slides 1\u20134 set up an expensive physical experiment, which is a valid application class for Bayesian optimization (BO). I become interested when the presenter reaches slide 7, because that is where methodological seriousness should appear.\n\nInstead, slide 7 says only \u201c6 variables,\u201d \u201c2 types of input data,\u201d and \u201c2 objectives.\u201d I do not learn the variables, their types, how categorical choices are encoded, whether process parameters are optimized, how noisy observations enter the Gaussian processes, how replicates identify noise, how failures are modeled, or how the force cap enters the acquisition. The title says BO handles noisy data, but the visual does not substantiate that message.\n\nThere is also an unresolved specification conflict across the supplied materials. The agreed outline says the study jointly optimizes T3 geometry and FDM processing parameters. The abstract\u2019s design vector lists four continuous geometric variables, one integer, and two categorical variables, apparently **seven variables**, while its fabrication section says print parameters are held fixed within a batch. The slide says **six variables**. A DAC audience will notice if the spoken details do not reconcile these versions.\n\nSlides 8\u201310 explain why physical feedback is useful, but they do not establish why qNEHVI is the right algorithm or whether adaptive selection helped. The visible deck never names qNEHVI, the feasible Pareto front, the peak-force constraint, batch size, initialization, or test budget. Hiding slide 5 is reasonable if it is only a generic \u201cwhat is BO?\u201d slide copied from another talk. But slide 7 must then carry the application-specific methodological content.\n\nWhen slide 10 says the workflow \u201csignificantly accelerate[s]\u201d optimization, I wait for a performance-versus-budget curve. It never arrives. Slide 12 then generalizes to other applications. My default conclusion is exactly the skeptical one posed in the prompt: this appears to be off-the-shelf qNEHVI attached to an interesting physical apparatus, with no demonstrated algorithmic or empirical advantage yet.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cIt was an experimental self-driving-lab-style application of batch multi-objective BO to printed impact absorbers, but the talk did not show whether BO beat space-filling sampling.\u201d\n\nThat differs from the intended message because I would not repeat \u201cdozens, not hundreds\u201d without a budget-matched baseline.\n\n## (c) My top three Q&A questions\n\n1. What are the exact decision variables and encodings, and why do the deck, scope statement, and abstract appear to disagree on whether there are six or seven variables and whether FDM parameters are optimized?\n2. How is observation noise estimated: fixed noise, learned heteroscedastic noise, technical replicates, or repeated control specimens? What is the replicate allocation?\n3. Against what budget-matched baseline do you evaluate qNEHVI, and what metric do you report: feasible hypervolume, best feasible objective values, or attainment probability versus physical tests?\n\n## (d) My most pointed objection\n\n**Nothing visible distinguishes a validated BO contribution from an untested application of standard qNEHVI.**\n\nUnanswered, this is **critical for DAC-10 reception**. Application novelty can be enough, but only if the physical campaign is rigorous and the adaptive method is compared fairly under the same test budget.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 7.** Replace the black-box inventory with a visual of posterior mean plus uncertainty \u2192 constrained batch acquisition \u2192 next physical tests, accompanied by the exact objectives and force constraint.\n- **Hidden-slide decision:** **Keep slide 5 hidden** as a standalone generic BO tutorial. Merge one plain-language BO sentence into slide 7 and put qNEHVI details in backup.\n\n---\n\n# P2. The aerospace practitioner\n\n## (a) My reaction in the room\n\nThe Super Ball Bot hook works because I know the lineage. Reusable landing structures are interesting. But the title\u2019s claim that tensegrity provides \u201crobust, reusable solutions\u201d is broader than what this deck demonstrates. A lab drop of a polymer proxy is not evidence of robustness in thermal extremes, vacuum, dust, radiation, repeated impacts, or off-axis terrain contact.\n\nThe deck never immediately tells me that the actual specimen is **tensegrity-inspired**, lacks ideal compression-only and tension-only membership, and is not intended to fly. That makes slide 2 vulnerable to looking like borrowed aerospace relevance.\n\nSlide 4 is plausible but too broad. Tensegrity design can be difficult, but aerospace development is not made slow only by design-space search. Verification, environmental qualification, uncertainty, packaging, guidance, and system integration matter. A faster specimen loop is useful, but it is one subsystem tool.\n\nI am not helped much by slide 7. I need BO translated into engineering behavior: it chooses the next small batch based on predicted performance and uncertainty while rejecting designs expected to violate a payload-force limit. The slide should name that limit and show the actual trade-off.\n\nSlides 8 and 9 are the first ones that answer \u201cwhat did you build and how did you hit it?\u201d A labeled specimen, deformation sequence, drop mass, speed or energy, orientation, and force measurement would hold my attention. Two simultaneous looping videos may not. I also want to know whether the structure rebounds, survives repeated drops, and retains its response, because the opening emphasizes reuse.\n\nSlide 10 is understandable. Slide 12 loses me because it jumps from a T3 polymer coupon to lattices and crutch tips without stating transfer conditions or technology gates.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey use an optimizer to choose which 3D-printed soft\u2013rigid impact absorber to drop-test next, with the eventual motivation of reusable landing structures.\u201d\n\nThat is fairly close, but I would deliberately downgrade \u201ctensegrity\u201d to \u201ctensegrity-inspired absorber\u201d and treat planetary landing as motivation only.\n\n## (c) My top three Q&A questions\n\n1. What makes the printed T3 specimen mechanically tensegrity-inspired rather than an ordinary compliant crush structure, and is it actually prestressed?\n2. What impact energy, mass, orientation, and transmitted-force cap are used, and how do they relate to a payload-protection requirement?\n3. How does performance change over repeated impacts, and what failure modes or permanent set limit the claimed reusability?\n\n## (d) My most pointed objection\n\n**The opening borrows flight relevance and reusability, but the visible deck never establishes the proxy boundary or shows repeated-impact evidence.**\n\nUnanswered, this is **highly damaging** to credibility with aerospace practitioners, though it does not invalidate the optimization workflow itself.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 2.** Keep the hook, but place a photograph of the actual T3 specimen beside the Super Ball Bot and state \u201cmotivation, not demonstrated flight readiness.\u201d\n- **Hidden-slide decision:** **Keep slide 3 hidden.** A generic elbow-friction GIF would distract and oversimplify. Merge a precise tensegrity-mechanism annotation into slide 8 instead.\n\n---\n\n# P3. The additive-manufacturing/materials researcher\n\n## (a) My reaction in the room\n\nSlides 1\u20134 give me a recognizable motivation for rapid experimental iteration. My interest rises on slide 8 because multi-material fused deposition modeling is where the claimed loop can succeed or fail.\n\nThe message \u201csingle-build co-fabrication, with no joining of parts\u201d is attractive, but it needs exact boundaries. Is there support removal, trimming, conditioning, manual tensioning, or fixture insertion? Does \u201csingle build\u201d mean one tool-change sequence on one machine, or truly co-deposited interfaces throughout the architecture? Are PLA and thermoplastic polyurethane (TPU) mechanically interlocked, chemically bonded, or merely in contact?\n\nThe abstract provides more useful information than the slide: a core-wrapping architecture is intended to resist delamination, specimens are weighed and dimensionally inspected, and a control is retested to monitor drift. Those are exactly the details the talk should surface. Slide 8\u2019s planned printing video will not replace a labeled cross-section or interface image.\n\nSlide 9 raises repeatability questions. TPU is rate-, temperature-, moisture-, and history-dependent. \u201cLimited, noisy data\u201d on slide 7 acknowledges noise but does not show the control plan. I need batch identity, drying/conditioning, build position, print orientation, machine state, interface failures, and replicates.\n\nThe deck also appears inconsistent about process parameters. The agreed scope says FDM processing parameters are optimized with structure, but the abstract says temperature, line width, infill, and retraction are held fixed within a batch. This could mean they vary between batches or are controlled rather than optimized, but the deck must say which.\n\nI like the physical loop on slide 10. I do not yet accept that multi-material printing \u201csupplies data fast\u201d until cycle time and failure rate are shown.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey co-print PLA and TPU impact specimens and use each mechanical test to guide the next design batch.\u201d\n\nI would probably omit \u201cno joining\u201d and \u201cdozens rather than hundreds\u201d until I saw interface qualification, failure accounting, and campaign data.\n\n## (c) My top three Q&A questions\n\n1. How is the PLA\u2013TPU interface designed and qualified, and what fraction of specimens fail by delamination or print defects?\n2. Which material, machine, conditioning, and build-position variables are controlled, measured as covariates, or optimized?\n3. How many true replicates and repeated control specimens are used to separate BO improvement from printer drift and TPU batch variation?\n\n## (d) My most pointed objection\n\n**The deck treats co-fabrication as an enabling fact without showing interface integrity, repeatability, or the remaining manual processing steps.**\n\nUnanswered, this is **highly damaging** because print variation is part of both the physical mechanism and the statistical noise model.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 8.** Replace or supplement the printing clip with an annotated specimen and interface detail: PLA struts, continuous TPU network, deposition/wrapping strategy, prestress status, post-processing, and observed failure modes.\n- **Hidden-slide decision:** **Keep slide 3 hidden.** Its proposed generic GIFs do not answer the materials questions. Put the relevant deformation mechanism on slide 8.\n\n---\n\n# P4. The first-year graduate student\n\n## (a) My reaction in the room\n\nSlide 1 sounds positive, and slide 2 gives me a memorable reason to care. I understand that the lander survives an impact because the structure itself deforms. But I do not yet know what \u201ctensegrity\u201d means, and the hidden slide 3 would not necessarily fix that: an elbow GIF and a squishing GIF could leave me with an inaccurate friction-versus-elasticity story.\n\nSlide 4 is easy to follow. The three words \u201cIteration, Building, Testing\u201d form a simple problem statement, although the images need to make their relationship obvious.\n\nI get lost on slide 7. Six variables of what? What are the two input-data types? Are the two objectives \u201cabsorb energy\u201d and \u201creduce force,\u201d or something else? I do not know what a black box means in this setting. The title says Bayesian optimization works with noisy data, but I have not been shown how it decides anything.\n\nSlide 8 helps because I can see printing. Still, I need labels that distinguish the rigid and flexible parts and one sentence explaining why the object is \u201ctensegrity-inspired.\u201d Slide 9 is intuitive if the clips are synchronized and annotated. I would understand even better if the slide drew the metric extraction directly on a force\u2013time or force\u2013displacement trace.\n\nSlide 10 is the clearest slide in the deck. Printing \u2192 testing \u2192 optimizer \u2192 next print is the idea I will remember. But it arrives after the technical terms rather than before them. I would benefit from seeing a version of this loop before slide 7 and then revisiting it after the methods.\n\nSlide 12 feels like a new presentation. I have not seen whether the current system worked, so the crutch tip and lattice do not feel earned. Then the talk ends on a blank question slide rather than telling me the answer.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey repeatedly print and drop-test flexible 3D structures, and a machine-learning model decides what to print next.\u201d\n\nMy retelling loses the two objectives, the force constraint, and the \u201cdozens rather than hundreds\u201d quantitative point. I might incorrectly call the method generic machine learning rather than Bayesian optimization.\n\n## (c) My top three Q&A questions\n\n1. What exactly is tensegrity, and which parts of your printed specimen are in tension and compression?\n2. What are the six things the optimizer can change, and what are the two scores it is trying to improve?\n3. How does the optimizer know whether to try something uncertain rather than the design it currently thinks is best?\n\n## (d) My most pointed objection\n\n**The deck names the ingredients before giving me a plain-language map of how they work together.**\n\nUnanswered, this is **moderately damaging**. I retain the loop but lose the actual research question and technical contribution.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 7.** It needs one concrete candidate, predicted performance plus uncertainty, and the next selected batch, not abstract counts around a black box.\n- **Hidden-slide decision:** **Unhide neither slide 3 nor slide 5 as written.** If forced to choose one, **keep slide 5 hidden** and integrate its one-sentence concept into a redesigned slide 7. Generic explanation would add time without restoring the missing optimization details.\n\n---\n\n# P5. The finite-element-analysis veteran\n\n## (a) My reaction in the room\n\nThe opening is fine until slide 4 implies that physical iteration is the central route around slow design. I start waiting for the argument about simulation. Because slide 6 is hidden, the deck never states the agreed, defensible position: a sufficiently calibrated model may cost more than it saves for this campaign, particularly when interfaces, defects, rate dependence, contact, and millisecond shock transients dominate.\n\nThat omission avoids an overt attack on finite-element analysis (FEA), which is good, but it also creates a logical hole. Why is physical BO the chosen source of objective values? Why not a hybrid strategy, reduced-order model, analytical screening, or multi-fidelity optimization? The deck merely proceeds from \u201cdesign is slow\u201d to \u201cBO makes experimentation affordable.\u201d\n\nI object to any suggestion that experimental data are automatically \u201creal\u201d and simulations are not. Measurements also involve sensor bandwidth, filtering, fixture dynamics, uncertainty, and model-based transformations. Slide 9\u2019s \u201caccelerometers gather real data\u201d wording is particularly vulnerable. A force estimate derived from acceleration still rests on calibration and assumptions.\n\nSlide 10 is a useful experimental workflow. It should be presented as a scoped engineering choice, not a universal replacement for simulation. Slide 12\u2019s transfer claim needs the condition from the outline: use this pattern where tests are authoritative, costly, the design space is mixed, and goals conflict.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey bypass a calibrated finite-element model and optimize directly against impact tests because the printed interfaces and transient response are difficult to model economically.\u201d\n\nThat is close to the intended experiment-first rationale, but I had to infer it; the visible slides do not say it clearly.\n\n## (c) My top three Q&A questions\n\n1. What evidence shows that calibrating a useful simulation would cost more than it saves for this campaign, rather than merely being inconvenient?\n2. How do you verify the measured peak transmitted force, including sensor bandwidth, fixture dynamics, filtering, and uncertainty?\n3. Why exclude analytical or low-fidelity screening from a multi-fidelity BO strategy, especially for infeasible geometries and gross structural trends?\n\n## (d) My most pointed objection\n\n**The deck uses experiment-first optimization without stating a scoped cost-and-validity argument or acknowledging where simulation could still add value.**\n\nUnanswered, this is **highly damaging in Q&A**. It makes the methodology look ideologically anti-simulation rather than economically reasoned.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 4.** It should become the two-sided decision: exhaustive physical search is too costly, while campaign-grade calibration for the hardest responses is also costly; therefore choose physical tests adaptively.\n- **Hidden-slide decision:** **Merge slide 6 into slide 4**, rather than unhide it as written. Keep a fuller evidence-backed simulation/experiment comparison as backup.\n\n---\n\n# P6. The friendly industry generalist\n\n## (a) My reaction in the room\n\nThe opening video is memorable, and I like the practical goal of shortening build\u2013test iteration. Slide 4 is easy to understand. My attention slips on slide 7 because the counts and black-box symbol do not tell a story. I do not need the name qNEHVI, but I need to know that the software balances trying promising designs with learning from uncertain ones and respects a force limit.\n\nSlides 8\u201310 are the strongest run. If the presenter shows one labeled specimen, one clean drop clip, and one loop diagram, I can see how I might use the pattern in another product-development problem. I want a cycle-time number and an indication of what software or human work is required.\n\nSlide 12 dilutes the message by offering several destinations before proving the present result. The ending is weak. A blank Q&A slide discards the chance to leave the audience with the workflow, the best specimen, and a measured improvement.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cPrint it, break or drop it, feed the measurements back, and let the optimizer pick the next batch instead of running a huge test matrix.\u201d\n\nThat captures the intended practical pattern, but I would probably overgeneralize it beyond the specific conditions under which it is useful.\n\n## (c) My top three Q&A questions\n\n1. How many prints and calendar days did it take to reach a useful design compared with your previous workflow?\n2. What parts of this system could another engineering team reuse without building the same drop tower or writing a custom optimizer?\n3. What happens when a print fails or a test produces an outlier?\n\n## (d) My most pointed objection\n\n**I understand the loop, but the deck never gives me the one number or result that proves it was worth building.**\n\nUnanswered, this is **moderately to highly damaging**. I would remember the concept but not have a reason to adopt or sponsor it.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 13.** Replace the blank Q&A screen with the best measured result, the closed-loop diagram, and one deployment rule; retain a small \u201cQuestions?\u201d label.\n- **Hidden-slide decision:** **Unhide slide 11** once populated. Results are more valuable than another explanatory slide.\n\n---\n\n# Synthesis\n\n## Cross-persona themes\n\n1. **The scope boundary is missing.** The Super Ball Bot is a good hook, but every persona risks interpreting a PLA\u2013TPU proxy as a claim about flight hardware.\n2. **Slide 7 is the weakest visible technical slide.** It serves neither experts nor novices. Counts such as \u201c6 variables\u201d and \u201c2 objectives\u201d are information, not a message.\n3. **The main claim currently lacks evidence.** \u201cSignificantly accelerate,\u201d \u201csupplies data fast,\u201d and \u201cdozens, not hundreds\u201d require a campaign ledger and a budget-matched comparison.\n4. **The results omission breaks the story.** The deck follows the outline through the workflow, then skips the outline\u2019s largest time block and moves directly to future work.\n5. **The physical system needs more definition.** The audience needs a labeled specimen, a clear explanation of \u201ctensegrity-inspired,\u201d interface design, remaining manual steps, and the measurement chain.\n6. **Noise must be operational rather than rhetorical.** Saying the data are noisy is insufficient. Replicates, periodic controls, drift, failures, and uncertainty should be shown.\n7. **Experiment-first needs a scoped rationale.** The deck should not dismiss FEA, but it should explain why adaptive physical testing is the economical source of objective values for this campaign.\n8. **The close is not a conclusion.** Future applications and a blank Q&A slide do not substitute for the measured finding and its conditions of transfer.\n9. **Media are a communication and schedule risk.** Short clips can work, but multiple looping videos violate the signal-to-noise principle and create failure modes.\n10. **The project specification needs reconciliation.** The supplied materials differ on six versus seven variables, whether FDM process parameters are optimized, and whether peak force is a constraint or part of the Pareto trade-off.\n\n## Five highest-priority slide-level revisions\n\n### 1. Slide 11: replace one empty slot with a three-slide quantitative results sequence\n\nUse full-sentence titles tied to observed data:\n\n- **11A:** \u201cThe campaign used **N** specimens in **B** adaptive batches, including **R** replicates and **F** failed prints.\u201d\n- **11B:** \u201cAt the same physical-test budget, adaptive selection reached **[measured feasible-performance result]** faster than **[predeclared Sobol/random baseline]**.\u201d\n- **11C:** \u201cThe measured feasible Pareto front trades specific energy absorption against compaction efficiency while respecting a **[value and unit]** peak-force cap.\u201d\n\nShow uncertainty, replicate spread, infeasible points, and representative specimen photographs or traces. If the campaign cannot support the baseline claim, change the talk\u2019s main message to: **\u201cWe evaluate whether noise-aware multi-objective Bayesian optimization can reduce the physical-test budget.\u201d**\n\n### 2. Slide 7: state the actual constrained optimization problem\n\nReplace the current black-box/count design with:\n\n- the named decision variables and their types;\n- a small batch entering the printer/test loop;\n- objectives: maximize specific energy absorption and compaction efficiency;\n- constraint: peak transmitted force below a stated cap;\n- qNEHVI\u2019s role in one plain sentence: it chooses a small next batch likely to expand the feasible trade-off frontier while accounting for uncertainty and noisy tests;\n- the declared test budget, initialization size, and batch size.\n\nResolve six versus seven variables and whether processing parameters are decision variables before finalizing this slide.\n\n### 3. Slide 2: put the proxy boundary beside the hook\n\nKeep the Super Ball Bot clip, but add an image of the actual specimen and the visible sentence:\n\n> **\u201cHere, a printed PLA\u2013TPU T3 prism is a proxy for developing the workflow, not flight hardware.\u201d**\n\nChange or qualify \u201crobust, reusable solutions\u201d unless the present campaign includes repeat-impact evidence.\n\n### 4. Slide 4: turn the vague need into the two-sided engineering decision\n\nRetitle it:\n\n> **\u201cBecause exhaustive testing and campaign-grade model calibration are both costly, each physical specimen must be chosen for its information value.\u201d**\n\nAdd one quantified physical-search estimate and one scoped calibration challenge. Do not say simulation is untrustworthy. State that FEA and lower-fidelity models remain possible screening or future multi-fidelity tools.\n\n### 5. Slides 10 and 13: separate the demonstrated loop from the conclusion\n\n- **Slide 10:** retitle to **\u201cThe closed loop turns each measured test into the next small batch of specimens.\u201d** Remove \u201csignificantly accelerate\u201d until the results demonstrate it. Mark automated and manual stages.\n- **Slide 13:** retain the closed-loop thumbnail, add the best quantitative result, and state the deployment rule: **use the loop when tests are authoritative, each test is costly, the design space is mixed, and performance goals conflict.** Add \u201cQuestions?\u201d without clearing the conclusion from the screen.\n\n## Hidden-slide adjudication\n\n| Hidden slide | Recommendation | Rationale |\n|---|---|---|\n| **3: tensegrity usefulness/mechanism** | **Merge into slide 8** | The generic elbow and squishing GIFs add noise and risk an inaccurate mechanism; an annotated actual specimen and deformation sequence are stronger. |\n| **5: generic BO explanation** | **Merge into slide 7; keep original as backup or delete** | DAC experts do not need a generic advocacy slide, but novices need one plain-language sentence and a concrete acquisition visual. |\n| **6: specimen information value/experiment-first** | **Merge into slide 4; keep evidence-rich version as backup** | The argument is structurally necessary, but the current title is vague and the visual does not support the scoped calibration-versus-campaign-cost decision. |\n| **11: results** | **Unhide and expand to three slides** | Results are the evidentiary center of the talk and were allocated five minutes in the agreed outline. An empty or hidden results slot makes the main claim untenable. |\n\n## Visible-slide claims needing evidence, hedging, or backup\n\n| Slide | Claim | Required treatment |\n|---|---|---|\n| **1** | \u201cBuild better tensegrity structures faster\u201d | Define \u201cbetter\u201d with the two objectives and force constraint; support \u201cfaster\u201d with the results comparison. |\n| **2** | Tensegrity is robust and reusable for planetary landings | Cite the Super Ball Bot source; distinguish concept evidence from this study; provide repeat-impact evidence or hedge reusability. |\n| **2** | Parachutes and retrorockets struggle in the stated conditions | Cite an authoritative aerospace source if retained in narration; avoid implying universal inadequacy. |\n| **4** | Current tensegrity design is slow and resource intensive | Quantify candidate count, print/test time, hand-assembly burden, or calibration effort. |\n| **7** | BO makes experiments affordable with limited, noisy data | Show campaign budget, noise characterization, replicates, baseline, and performance versus tests. Prepare qNEHVI and Gaussian-process details in backup. |\n| **7** | Six variables, two input-data types, two objectives | Reconcile with the apparent seven-variable abstract specification and identify every quantity. Clarify \u201cinput-data types.\u201d |\n| **8** | Multi-material AM supplies data fast | Report print-to-result cycle time and failure rate. |\n| **8** | Single-build co-fabrication with no joining | Show interface and post-processing; use \u201cno joining of PLA and TPU members\u201d if other manual operations remain. |\n| **9** | Accelerometers gather the relevant impact data | Show sensor locations and the derivation of transmitted force, specific energy absorption, and compaction efficiency. Reconcile this wording with the abstract\u2019s instrumented-tup description. |\n| **10** | The loop significantly accelerates optimization | Remove \u201csignificantly\u201d unless a defined baseline and uncertainty support it. Statistical significance should not be implied casually. |\n| **12** | Workflow will transfer to lattices and other applications | Present as conditional future work with transfer criteria, not an achieved result. Attribute any external crutch-tip image and claim. |\n\n### Prepared backup slides\n\n1. Full design-variable table, bounds, encoding, and fixed versus optimized process parameters.\n2. qNEHVI implementation, Gaussian-process assumptions, constraint treatment, batch size, and acquisition settings.\n3. Noise plan: replicates, periodic control, heteroscedasticity, drift, outlier policy, and uncertainty propagation.\n4. Budget-matched baseline definition and evaluation metric.\n5. Force-cap rationale and full objective definitions.\n6. Sensor chain, calibration, sample rate, filtering, fixture dynamics, and metric extraction.\n7. PLA\u2013TPU interface, conditioning, build layout, dimensional/mass deviations, and print-failure modes.\n8. Scoped FEA rationale with citations or internal validation; possible analytical or multi-fidelity extensions.\n9. Automation map and cycle-time breakdown.\n10. Repeated-impact behavior if reusability remains in the opening.\n\n## Merged, ordered presenter TODO list\n\n### Must fix before the talk\n\n1. **Freeze the scientific specification.** Reconcile the number and type of decision variables, whether FDM parameters are optimized or controlled, the two objectives, the force constraint, treatment of print failures, and the measurement chain. Use one formulation across slides, abstract, paper, and Q&A.\n2. **Set a results completion plan.** Establish dates and owners for campaign completion, repeat tests, data quality review, baseline analysis, and slide freeze. Predeclare the fallback claim if the baseline does not support \u201cdozens rather than hundreds.\u201d\n3. **Build the three-slide results sequence.** Campaign ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front. Allocate approximately five minutes, as the agreed outline requires.\n4. **Add the proxy statement to slide 2.** State plainly that the PLA\u2013TPU T3 specimen is a workflow proxy and not flight hardware.\n5. **Redesign slide 7.** Show the exact constrained multi-objective problem, test budget, small batches, uncertainty, noisy observations, and the role of qNEHVI.\n6. **Remove unsupported result language.** Until supported, delete \u201csignificantly\u201d from slide 10 and avoid \u201cdozens rather than hundreds\u201d in the title, narration, or close.\n7. **Clarify experiment-first positioning.** Merge the information-value argument into slide 4 and prepare an evidence-backed FEA backup slide.\n8. **Make the experiment auditable.** On slide 9 or backup, reconcile accelerometers and the instrumented tup; show how all three performance metrics are calculated and how uncertainty is handled.\n9. **De-risk every video.** Store local trimmed files, disable distracting loops, test them on presentation hardware, and include static fallback frames.\n10. **Rehearse to 13:30\u201314:00.** Protect the results and conclusion from being cut. Practice once without slides, consistent with Doumont\u2019s preparation advice.\n\n### Should fix\n\n11. **Replace slide 8\u2019s generic print emphasis with mechanism and materials evidence.** Label PLA, TPU, interface strategy, prestress status, deformation, post-processing, and failure modes.\n12. **Quantify slide 4.** Give at least one campaign-relevant cost, duration, or candidate-count estimate.\n13. **Show automation boundaries on slide 10.** Candidate selection and surrogate updating may be automated; slicing, handling, test setup, and testing remain manual according to the abstract.\n14. **Convert slide 12 to a gated roadmap.** Separate demonstrated T3-proxy results from future lattices, flight materials, and terrestrial applications.\n15. **Turn slide 13 into the conclusion.** Leave the audience with the best result and transfer rule while taking questions.\n16. **Prepare technical backup slides.** Prioritize baseline fairness, noise, force-cap rationale, print failures, materials repeatability, and FEA positioning.\n\n### Polish\n\n17. Replace the slide 1 title with a specific message, such as **\u201cPhysical Bayesian optimization searches multi-material tensegrity absorbers one small test batch at a time.\u201d**\n18. Left-align titles and use intentional line breaks; check every slide at six-per-page scale.\n19. Use one purposeful visual per slide. Avoid decorative loading or elbow animations.\n20. Standardize terminology: \u201cBayesian optimization,\u201d not \u201cBays Opt\u201d; \u201ctensegrity-inspired\u201d where appropriate; define specific energy absorption once.\n21. Add citations directly beside borrowed footage, images, and quantitative claims.\n\n## Who the deck serves best and worst\n\n- **Best served:** **P6, the friendly industry generalist.** The visible sequence is visually intuitive, practical, and easy to reduce to \u201cprint \u2192 test \u2192 learn \u2192 repeat.\u201d\n- **Worst served:** **P1, the skeptical BO insider.** The current deck omits nearly every item needed to judge a DAC-10 contribution: exact problem formulation, budget, baseline, noise model, constraint handling, and empirical adaptive advantage.\n\nThat is **not the right trade-off for DAC-10**. The deck should remain accessible to P4 and P6, but its technical center must satisfy P1 without turning into a BO lecture. One rigorous optimization slide, three quantitative results slides, and targeted backup material would achieve that balance.\n\n## Predicted reception\n\n- **As is: 4/10.** The physical workflow and application are memorable, but the absent scope statement, empty results section, vague BO slide, and unsupported acceleration claim leave the DAC contribution unproven.\n- **After the must-fixes: 8/10.** A scoped proxy claim, reconciled methods, fair budget-matched comparison, feasible Pareto front, and measured conclusion would turn the same visual story into a credible design-optimization application talk.\n\n## Discretionary analytical decisions\n\n- Evaluated only the visible slide order for the simulated in-room reactions, while using hidden slides and presenter notes to judge revision options.\n- Treated planned placeholder visuals as if implemented according to the presenter\u2019s notes, but judged the selected visual concept and associated stage risk rather than absent graphic quality.\n- Prioritized the Draft 3 outline over conflicting Draft 1 wording because the prompt identifies it as the agreed story arc, scope framing, results plan, and timing plan.\n- Rated objections qualitatively as moderate, high, or critical rather than assigning unsupported numerical probabilities.\n- Recommended three results slides rather than preserving one reserved slot because the agreed outline specifies three distinct evidentiary products and allocates five minutes to results.\n- Recommended merging hidden slides 3, 5, and 6 rather than simply unhiding them because their necessary ideas can be conveyed with higher signal-to-noise on existing slides.\n- Treated the six-versus-seven-variable and optimized-versus-fixed-process-parameter differences as unresolved specification conflicts requiring reconciliation, rather than inferring which document is correct.\n- Assessed claims against the supplied deck extraction, Draft 3 outline, Doumont notes, and submitted abstract only; no independent validation of cited literature or unseen experimental data was attempted.", + "name": "submit_answer", + "tool_call_id": "call_8In1tJq32AD2tTyHX3tmIlLf", + "info": { + "start_ts": 8302.413890092, + "end_ts": 8302.416029592 + } + } + ], + "reward": 0.0, + "truncated": false, + "done": true, + "value": 0.0, + "metadata": { + "time_elapsed_before_transition": 1.1510896089994276, + "time_elapsed_agent_get_asv": 143.87266783500036, + "time_elapsed_after_agent_get_asv": 1.0095709670003998, + "time_elapsed_env_step": 0.002633800000694464, + "time_elapsed_after_env_step": 0.9450106980002602 + }, + "action": { + "call_id": { + "run_id": "4566fd2d-f49d-4655-ac55-692f306b1db0", + "fwd_id": "f6b2ecbb-a1f2-438d-9b88-1a216eb20c49" + }, + "op_name": "IdentityOp-3b7eacd87007", + "op_class_name": "ldp.graph.common_ops.IdentityOp", + "value": { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8In1tJq32AD2tTyHX3tmIlLf", + "type": "function", + "function": { + "arguments": "{\"answer\": \"# Mock audience report: IDETC-CIE 2026 slide deck, Draft 1\\n\\n## Executive read\\n\\nThe visible deck has a clear visual spine: **motivation \\u2192 slow iteration \\u2192 Bayesian optimization \\u2192 multi-material printing \\u2192 impact testing \\u2192 closed loop \\u2192 future applications**. The strongest section is slides 8\\u201310, where fabrication, testing, and adaptive selection finally appear as one workflow.\\n\\nBut Draft 1 does not yet deliver the technical case promised by the agreed outline. The visible deck omits four things a DAC-10 audience will need:\\n\\n1. **Honest scope:** no visible slide says that the PLA\\u2013TPU T3 prism is a proxy system rather than flight hardware.\\n2. **A precise optimization problem:** the six variables, two objectives, force constraint, noise treatment, and fabrication failures are not intelligible on slide 7.\\n3. **Quantitative evidence:** the sole results slot is hidden and empty, yet slides 10 and 12 already claim acceleration and transfer.\\n4. **A measured conclusion:** the talk jumps from the workflow to speculative applications without showing a campaign ledger, comparison against a budget-matched baseline, or feasible Pareto front.\\n\\nThe slide titles generally follow Doumont\\u2019s full-sentence-message rule, but several messages outrun the evidence. The deck also departs substantially from the Draft 3 timing plan, which reserves **5 of 15 minutes for results**. As built, results receive zero visible slides.\\n\\n---\\n\\n# P0. The program manager in the audience\\n\\n## (a) My reaction in the room\\n\\nSlide 1 tells me the team wants to work faster, but it does not tell me what is being optimized or what has actually been built. The title sounds aspirational rather than traceable to a project deliverable. I immediately want the formal title from the abstract or a shorter version of it.\\n\\nThe Super Ball Bot footage on slide 2 is an effective hook. I understand why reusable impact attenuation matters. But the slide creates a scope risk: I am looking at planetary landing hardware while the actual work is a printed PLA\\u2013TPU T3 proxy. Because that distinction is not stated immediately, I may spend the rest of the talk evaluating the work against flight-readiness criteria the project never intended to meet.\\n\\nSlide 4 gives me the business problem, but not a baseline. \\u201cSlow and resource intensive\\u201d needs a number: hours per print-test cycle, number of factors, or the candidate count under a declared design of experiments. Without that, I cannot assess whether the proposed loop attacks the dominant schedule driver.\\n\\nSlide 7 is where I expect the project definition. Instead, \\u201c6 variables,\\u201d \\u201c2 types of input data,\\u201d and \\u201c2 objectives\\u201d read like inventory labels. I cannot reconstruct the statement of work from them. I need to see what is automated, what remains manual, what the force cap is, and what constitutes a successful campaign.\\n\\nSlides 8 and 9 make the project tangible. A labeled specimen and an actual drop test would build confidence. Yet \\u201csingle-build\\u201d and \\u201cno joining\\u201d need a process qualification caveat, and I need the measurement chain to be explicit. The abstract refers to an instrumented tup and force\\u2013time data; slide 9 says accelerometers. Those may be compatible, but the deck presently leaves the relationship unclear.\\n\\nSlide 10 is the right management view of the system, but \\u201csignificantly accelerate\\u201d is a result claim placed on a workflow diagram. At this point I ask, \\u201cCompared with what, by how much, and over how many specimens?\\u201d Then the deck skips the hidden results slide and goes directly to future applications. That is the point where my confidence drops sharply. The project is asking me to extrapolate before closing the evidence loop.\\n\\nSlide 12 is too early and too broad. A crutch tip and lattice can be legitimate next phases, but only after the team shows what the present campaign established and what technical risks remain. The blank questions slide gives me no durable take-home message.\\n\\n### PM stage-risk register\\n\\n| Risk | Likelihood | Consequence | Mitigation |\\n|---|---:|---:|---|\\n| Results are incomplete by conference lock | Medium to high | Critical: central claim becomes unsupported | Set a dated campaign freeze; predeclare a fallback message and minimum publishable result package |\\n| Embedded Super Ball Bot or drop-test video fails | Medium | Moderate: weak opening or broken pacing | Use local, trimmed files; retain a still-frame fallback on every video slide |\\n| Talk overruns because several videos and explanations are improvised | High | High: results or conclusion gets cut | Script videos to 10\\u201315 seconds total each; rehearse to 13:30; assign 5 minutes to results |\\n| Scope is mistaken for flight-hardware validation | High | High: credibility damage in Q&A | Put \\u201cPLA\\u2013TPU T3 proxy; not flight hardware\\u201d on slide 2 or the next slide |\\n| Acceleration claim lacks a budget-matched comparator | High | Critical for DAC review audience | Show Sobol/random or another predeclared baseline at the same physical-test budget |\\n| Measurement chain is challenged | Medium | High | Add a methods schematic and backup slide on calibration, sampling, filtering, and metric extraction |\\n| \\u201cNo joining\\u201d or \\u201cnear autonomous\\u201d is read as full automation | Medium | Moderate | List manual steps explicitly: slicing, handling, setup, and testing |\\n| Animations distract or fail to communicate without narration | Medium | Moderate | Prefer one short clip plus static annotated frames; follow Doumont\\u2019s signal-to-noise rule |\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey are building a loop that prints PLA\\u2013TPU tensegrity-like absorbers, drop-tests them, and lets Bayesian optimization choose the next print.\\u201d\\n\\nThat retains the basic workflow but loses the intended **dozens rather than hundreds** claim because the deck never demonstrates it. I would also probably describe the work as \\u201clander research,\\u201d which is a distortion caused by the missing proxy-system statement.\\n\\n## (c) My top three Q&A questions\\n\\n1. What is the dated plan to complete the campaign, baseline comparison, and repeat testing before the conference slide freeze?\\n2. Which exact operations are automated today, which remain manual, and what is the measured end-to-end cycle time per adaptive batch?\\n3. What evidence will justify \\u201cdozens rather than hundreds,\\u201d and what will you claim if the budget-matched baseline does not show an advantage?\\n\\n## (d) My most pointed objection\\n\\n**You are presenting a claimed acceleration without showing a completed campaign, a comparator, or even a visible results slide.**\\n\\nUnanswered, this is **critical**. I would not fund a scale-up phase from this deck. I might fund completion of the present campaign if the team showed a disciplined schedule, predefined success criteria, and a credible fallback.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 11.** It must become a real, scheduled results package, preferably three slides: campaign ledger, budget-matched baseline, and feasible Pareto front.\\n- **Hidden-slide decision:** **Unhide slide 11**, but not in its current empty form. The talk cannot make its principal claim without it.\\n\\n### PM slide-level punch list, ordered by schedule criticality\\n\\n**Must be fixed first**\\n\\n1. **Slide 11/results sequence:** assign owners and dates for data freeze, quality control, baseline computation, replicate analysis, and final plots. Define fallback wording now.\\n2. **Slide 2:** add the explicit scope line, \\u201cThis study uses a printed PLA\\u2013TPU T3 prism as a proxy for developing the workflow; it is not flight hardware.\\u201d\\n3. **Slide 7:** replace category counts with the actual optimization problem, test budget, batch size, objectives, constraint, and noise/failure treatment.\\n4. **Slide 10:** remove \\u201csignificantly\\u201d until slide 11 demonstrates a quantitative improvement; show automation boundaries and the loop\\u2019s measured cycle time.\\n5. **Slides 2 and 9:** download and trim media, test on the conference laptop, and put a static fallback image on each slide.\\n\\n**Then fix**\\n\\n6. **Slide 4:** quantify the current cost or candidate count and identify the bottleneck.\\n7. **Slide 8:** label PLA, TPU, interface, and any post-processing; qualify \\u201cno joining.\\u201d\\n8. **Slide 9:** reconcile accelerometers with the instrumented-tup description and show how force, energy absorption, and compaction efficiency are obtained.\\n9. **Slide 12:** convert the application collage into a conditional next-phase roadmap with gates.\\n10. **Slide 13:** end on the measured result and deployment rule, not a blank Q&A screen.\\n\\n**Nice to have**\\n\\n11. Replace decorative or looping media on slide 4 with one static process image unless it communicates a measured delay.\\n12. Add backup slides on Bayesian optimization implementation, finite-element-analysis positioning, repeatability, force-cap rationale, and print failures.\\n\\n---\\n\\n# P1. The skeptical Bayesian-optimization insider\\n\\n## (a) My reaction in the room\\n\\nThe opening is competent but generic for a design-automation conference. Slides 1\\u20134 set up an expensive physical experiment, which is a valid application class for Bayesian optimization (BO). I become interested when the presenter reaches slide 7, because that is where methodological seriousness should appear.\\n\\nInstead, slide 7 says only \\u201c6 variables,\\u201d \\u201c2 types of input data,\\u201d and \\u201c2 objectives.\\u201d I do not learn the variables, their types, how categorical choices are encoded, whether process parameters are optimized, how noisy observations enter the Gaussian processes, how replicates identify noise, how failures are modeled, or how the force cap enters the acquisition. The title says BO handles noisy data, but the visual does not substantiate that message.\\n\\nThere is also an unresolved specification conflict across the supplied materials. The agreed outline says the study jointly optimizes T3 geometry and FDM processing parameters. The abstract\\u2019s design vector lists four continuous geometric variables, one integer, and two categorical variables, apparently **seven variables**, while its fabrication section says print parameters are held fixed within a batch. The slide says **six variables**. A DAC audience will notice if the spoken details do not reconcile these versions.\\n\\nSlides 8\\u201310 explain why physical feedback is useful, but they do not establish why qNEHVI is the right algorithm or whether adaptive selection helped. The visible deck never names qNEHVI, the feasible Pareto front, the peak-force constraint, batch size, initialization, or test budget. Hiding slide 5 is reasonable if it is only a generic \\u201cwhat is BO?\\u201d slide copied from another talk. But slide 7 must then carry the application-specific methodological content.\\n\\nWhen slide 10 says the workflow \\u201csignificantly accelerate[s]\\u201d optimization, I wait for a performance-versus-budget curve. It never arrives. Slide 12 then generalizes to other applications. My default conclusion is exactly the skeptical one posed in the prompt: this appears to be off-the-shelf qNEHVI attached to an interesting physical apparatus, with no demonstrated algorithmic or empirical advantage yet.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cIt was an experimental self-driving-lab-style application of batch multi-objective BO to printed impact absorbers, but the talk did not show whether BO beat space-filling sampling.\\u201d\\n\\nThat differs from the intended message because I would not repeat \\u201cdozens, not hundreds\\u201d without a budget-matched baseline.\\n\\n## (c) My top three Q&A questions\\n\\n1. What are the exact decision variables and encodings, and why do the deck, scope statement, and abstract appear to disagree on whether there are six or seven variables and whether FDM parameters are optimized?\\n2. How is observation noise estimated: fixed noise, learned heteroscedastic noise, technical replicates, or repeated control specimens? What is the replicate allocation?\\n3. Against what budget-matched baseline do you evaluate qNEHVI, and what metric do you report: feasible hypervolume, best feasible objective values, or attainment probability versus physical tests?\\n\\n## (d) My most pointed objection\\n\\n**Nothing visible distinguishes a validated BO contribution from an untested application of standard qNEHVI.**\\n\\nUnanswered, this is **critical for DAC-10 reception**. Application novelty can be enough, but only if the physical campaign is rigorous and the adaptive method is compared fairly under the same test budget.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 7.** Replace the black-box inventory with a visual of posterior mean plus uncertainty \\u2192 constrained batch acquisition \\u2192 next physical tests, accompanied by the exact objectives and force constraint.\\n- **Hidden-slide decision:** **Keep slide 5 hidden** as a standalone generic BO tutorial. Merge one plain-language BO sentence into slide 7 and put qNEHVI details in backup.\\n\\n---\\n\\n# P2. The aerospace practitioner\\n\\n## (a) My reaction in the room\\n\\nThe Super Ball Bot hook works because I know the lineage. Reusable landing structures are interesting. But the title\\u2019s claim that tensegrity provides \\u201crobust, reusable solutions\\u201d is broader than what this deck demonstrates. A lab drop of a polymer proxy is not evidence of robustness in thermal extremes, vacuum, dust, radiation, repeated impacts, or off-axis terrain contact.\\n\\nThe deck never immediately tells me that the actual specimen is **tensegrity-inspired**, lacks ideal compression-only and tension-only membership, and is not intended to fly. That makes slide 2 vulnerable to looking like borrowed aerospace relevance.\\n\\nSlide 4 is plausible but too broad. Tensegrity design can be difficult, but aerospace development is not made slow only by design-space search. Verification, environmental qualification, uncertainty, packaging, guidance, and system integration matter. A faster specimen loop is useful, but it is one subsystem tool.\\n\\nI am not helped much by slide 7. I need BO translated into engineering behavior: it chooses the next small batch based on predicted performance and uncertainty while rejecting designs expected to violate a payload-force limit. The slide should name that limit and show the actual trade-off.\\n\\nSlides 8 and 9 are the first ones that answer \\u201cwhat did you build and how did you hit it?\\u201d A labeled specimen, deformation sequence, drop mass, speed or energy, orientation, and force measurement would hold my attention. Two simultaneous looping videos may not. I also want to know whether the structure rebounds, survives repeated drops, and retains its response, because the opening emphasizes reuse.\\n\\nSlide 10 is understandable. Slide 12 loses me because it jumps from a T3 polymer coupon to lattices and crutch tips without stating transfer conditions or technology gates.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey use an optimizer to choose which 3D-printed soft\\u2013rigid impact absorber to drop-test next, with the eventual motivation of reusable landing structures.\\u201d\\n\\nThat is fairly close, but I would deliberately downgrade \\u201ctensegrity\\u201d to \\u201ctensegrity-inspired absorber\\u201d and treat planetary landing as motivation only.\\n\\n## (c) My top three Q&A questions\\n\\n1. What makes the printed T3 specimen mechanically tensegrity-inspired rather than an ordinary compliant crush structure, and is it actually prestressed?\\n2. What impact energy, mass, orientation, and transmitted-force cap are used, and how do they relate to a payload-protection requirement?\\n3. How does performance change over repeated impacts, and what failure modes or permanent set limit the claimed reusability?\\n\\n## (d) My most pointed objection\\n\\n**The opening borrows flight relevance and reusability, but the visible deck never establishes the proxy boundary or shows repeated-impact evidence.**\\n\\nUnanswered, this is **highly damaging** to credibility with aerospace practitioners, though it does not invalidate the optimization workflow itself.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 2.** Keep the hook, but place a photograph of the actual T3 specimen beside the Super Ball Bot and state \\u201cmotivation, not demonstrated flight readiness.\\u201d\\n- **Hidden-slide decision:** **Keep slide 3 hidden.** A generic elbow-friction GIF would distract and oversimplify. Merge a precise tensegrity-mechanism annotation into slide 8 instead.\\n\\n---\\n\\n# P3. The additive-manufacturing/materials researcher\\n\\n## (a) My reaction in the room\\n\\nSlides 1\\u20134 give me a recognizable motivation for rapid experimental iteration. My interest rises on slide 8 because multi-material fused deposition modeling is where the claimed loop can succeed or fail.\\n\\nThe message \\u201csingle-build co-fabrication, with no joining of parts\\u201d is attractive, but it needs exact boundaries. Is there support removal, trimming, conditioning, manual tensioning, or fixture insertion? Does \\u201csingle build\\u201d mean one tool-change sequence on one machine, or truly co-deposited interfaces throughout the architecture? Are PLA and thermoplastic polyurethane (TPU) mechanically interlocked, chemically bonded, or merely in contact?\\n\\nThe abstract provides more useful information than the slide: a core-wrapping architecture is intended to resist delamination, specimens are weighed and dimensionally inspected, and a control is retested to monitor drift. Those are exactly the details the talk should surface. Slide 8\\u2019s planned printing video will not replace a labeled cross-section or interface image.\\n\\nSlide 9 raises repeatability questions. TPU is rate-, temperature-, moisture-, and history-dependent. \\u201cLimited, noisy data\\u201d on slide 7 acknowledges noise but does not show the control plan. I need batch identity, drying/conditioning, build position, print orientation, machine state, interface failures, and replicates.\\n\\nThe deck also appears inconsistent about process parameters. The agreed scope says FDM processing parameters are optimized with structure, but the abstract says temperature, line width, infill, and retraction are held fixed within a batch. This could mean they vary between batches or are controlled rather than optimized, but the deck must say which.\\n\\nI like the physical loop on slide 10. I do not yet accept that multi-material printing \\u201csupplies data fast\\u201d until cycle time and failure rate are shown.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey co-print PLA and TPU impact specimens and use each mechanical test to guide the next design batch.\\u201d\\n\\nI would probably omit \\u201cno joining\\u201d and \\u201cdozens rather than hundreds\\u201d until I saw interface qualification, failure accounting, and campaign data.\\n\\n## (c) My top three Q&A questions\\n\\n1. How is the PLA\\u2013TPU interface designed and qualified, and what fraction of specimens fail by delamination or print defects?\\n2. Which material, machine, conditioning, and build-position variables are controlled, measured as covariates, or optimized?\\n3. How many true replicates and repeated control specimens are used to separate BO improvement from printer drift and TPU batch variation?\\n\\n## (d) My most pointed objection\\n\\n**The deck treats co-fabrication as an enabling fact without showing interface integrity, repeatability, or the remaining manual processing steps.**\\n\\nUnanswered, this is **highly damaging** because print variation is part of both the physical mechanism and the statistical noise model.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 8.** Replace or supplement the printing clip with an annotated specimen and interface detail: PLA struts, continuous TPU network, deposition/wrapping strategy, prestress status, post-processing, and observed failure modes.\\n- **Hidden-slide decision:** **Keep slide 3 hidden.** Its proposed generic GIFs do not answer the materials questions. Put the relevant deformation mechanism on slide 8.\\n\\n---\\n\\n# P4. The first-year graduate student\\n\\n## (a) My reaction in the room\\n\\nSlide 1 sounds positive, and slide 2 gives me a memorable reason to care. I understand that the lander survives an impact because the structure itself deforms. But I do not yet know what \\u201ctensegrity\\u201d means, and the hidden slide 3 would not necessarily fix that: an elbow GIF and a squishing GIF could leave me with an inaccurate friction-versus-elasticity story.\\n\\nSlide 4 is easy to follow. The three words \\u201cIteration, Building, Testing\\u201d form a simple problem statement, although the images need to make their relationship obvious.\\n\\nI get lost on slide 7. Six variables of what? What are the two input-data types? Are the two objectives \\u201cabsorb energy\\u201d and \\u201creduce force,\\u201d or something else? I do not know what a black box means in this setting. The title says Bayesian optimization works with noisy data, but I have not been shown how it decides anything.\\n\\nSlide 8 helps because I can see printing. Still, I need labels that distinguish the rigid and flexible parts and one sentence explaining why the object is \\u201ctensegrity-inspired.\\u201d Slide 9 is intuitive if the clips are synchronized and annotated. I would understand even better if the slide drew the metric extraction directly on a force\\u2013time or force\\u2013displacement trace.\\n\\nSlide 10 is the clearest slide in the deck. Printing \\u2192 testing \\u2192 optimizer \\u2192 next print is the idea I will remember. But it arrives after the technical terms rather than before them. I would benefit from seeing a version of this loop before slide 7 and then revisiting it after the methods.\\n\\nSlide 12 feels like a new presentation. I have not seen whether the current system worked, so the crutch tip and lattice do not feel earned. Then the talk ends on a blank question slide rather than telling me the answer.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey repeatedly print and drop-test flexible 3D structures, and a machine-learning model decides what to print next.\\u201d\\n\\nMy retelling loses the two objectives, the force constraint, and the \\u201cdozens rather than hundreds\\u201d quantitative point. I might incorrectly call the method generic machine learning rather than Bayesian optimization.\\n\\n## (c) My top three Q&A questions\\n\\n1. What exactly is tensegrity, and which parts of your printed specimen are in tension and compression?\\n2. What are the six things the optimizer can change, and what are the two scores it is trying to improve?\\n3. How does the optimizer know whether to try something uncertain rather than the design it currently thinks is best?\\n\\n## (d) My most pointed objection\\n\\n**The deck names the ingredients before giving me a plain-language map of how they work together.**\\n\\nUnanswered, this is **moderately damaging**. I retain the loop but lose the actual research question and technical contribution.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 7.** It needs one concrete candidate, predicted performance plus uncertainty, and the next selected batch, not abstract counts around a black box.\\n- **Hidden-slide decision:** **Unhide neither slide 3 nor slide 5 as written.** If forced to choose one, **keep slide 5 hidden** and integrate its one-sentence concept into a redesigned slide 7. Generic explanation would add time without restoring the missing optimization details.\\n\\n---\\n\\n# P5. The finite-element-analysis veteran\\n\\n## (a) My reaction in the room\\n\\nThe opening is fine until slide 4 implies that physical iteration is the central route around slow design. I start waiting for the argument about simulation. Because slide 6 is hidden, the deck never states the agreed, defensible position: a sufficiently calibrated model may cost more than it saves for this campaign, particularly when interfaces, defects, rate dependence, contact, and millisecond shock transients dominate.\\n\\nThat omission avoids an overt attack on finite-element analysis (FEA), which is good, but it also creates a logical hole. Why is physical BO the chosen source of objective values? Why not a hybrid strategy, reduced-order model, analytical screening, or multi-fidelity optimization? The deck merely proceeds from \\u201cdesign is slow\\u201d to \\u201cBO makes experimentation affordable.\\u201d\\n\\nI object to any suggestion that experimental data are automatically \\u201creal\\u201d and simulations are not. Measurements also involve sensor bandwidth, filtering, fixture dynamics, uncertainty, and model-based transformations. Slide 9\\u2019s \\u201caccelerometers gather real data\\u201d wording is particularly vulnerable. A force estimate derived from acceleration still rests on calibration and assumptions.\\n\\nSlide 10 is a useful experimental workflow. It should be presented as a scoped engineering choice, not a universal replacement for simulation. Slide 12\\u2019s transfer claim needs the condition from the outline: use this pattern where tests are authoritative, costly, the design space is mixed, and goals conflict.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey bypass a calibrated finite-element model and optimize directly against impact tests because the printed interfaces and transient response are difficult to model economically.\\u201d\\n\\nThat is close to the intended experiment-first rationale, but I had to infer it; the visible slides do not say it clearly.\\n\\n## (c) My top three Q&A questions\\n\\n1. What evidence shows that calibrating a useful simulation would cost more than it saves for this campaign, rather than merely being inconvenient?\\n2. How do you verify the measured peak transmitted force, including sensor bandwidth, fixture dynamics, filtering, and uncertainty?\\n3. Why exclude analytical or low-fidelity screening from a multi-fidelity BO strategy, especially for infeasible geometries and gross structural trends?\\n\\n## (d) My most pointed objection\\n\\n**The deck uses experiment-first optimization without stating a scoped cost-and-validity argument or acknowledging where simulation could still add value.**\\n\\nUnanswered, this is **highly damaging in Q&A**. It makes the methodology look ideologically anti-simulation rather than economically reasoned.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 4.** It should become the two-sided decision: exhaustive physical search is too costly, while campaign-grade calibration for the hardest responses is also costly; therefore choose physical tests adaptively.\\n- **Hidden-slide decision:** **Merge slide 6 into slide 4**, rather than unhide it as written. Keep a fuller evidence-backed simulation/experiment comparison as backup.\\n\\n---\\n\\n# P6. The friendly industry generalist\\n\\n## (a) My reaction in the room\\n\\nThe opening video is memorable, and I like the practical goal of shortening build\\u2013test iteration. Slide 4 is easy to understand. My attention slips on slide 7 because the counts and black-box symbol do not tell a story. I do not need the name qNEHVI, but I need to know that the software balances trying promising designs with learning from uncertain ones and respects a force limit.\\n\\nSlides 8\\u201310 are the strongest run. If the presenter shows one labeled specimen, one clean drop clip, and one loop diagram, I can see how I might use the pattern in another product-development problem. I want a cycle-time number and an indication of what software or human work is required.\\n\\nSlide 12 dilutes the message by offering several destinations before proving the present result. The ending is weak. A blank Q&A slide discards the chance to leave the audience with the workflow, the best specimen, and a measured improvement.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cPrint it, break or drop it, feed the measurements back, and let the optimizer pick the next batch instead of running a huge test matrix.\\u201d\\n\\nThat captures the intended practical pattern, but I would probably overgeneralize it beyond the specific conditions under which it is useful.\\n\\n## (c) My top three Q&A questions\\n\\n1. How many prints and calendar days did it take to reach a useful design compared with your previous workflow?\\n2. What parts of this system could another engineering team reuse without building the same drop tower or writing a custom optimizer?\\n3. What happens when a print fails or a test produces an outlier?\\n\\n## (d) My most pointed objection\\n\\n**I understand the loop, but the deck never gives me the one number or result that proves it was worth building.**\\n\\nUnanswered, this is **moderately to highly damaging**. I would remember the concept but not have a reason to adopt or sponsor it.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 13.** Replace the blank Q&A screen with the best measured result, the closed-loop diagram, and one deployment rule; retain a small \\u201cQuestions?\\u201d label.\\n- **Hidden-slide decision:** **Unhide slide 11** once populated. Results are more valuable than another explanatory slide.\\n\\n---\\n\\n# Synthesis\\n\\n## Cross-persona themes\\n\\n1. **The scope boundary is missing.** The Super Ball Bot is a good hook, but every persona risks interpreting a PLA\\u2013TPU proxy as a claim about flight hardware.\\n2. **Slide 7 is the weakest visible technical slide.** It serves neither experts nor novices. Counts such as \\u201c6 variables\\u201d and \\u201c2 objectives\\u201d are information, not a message.\\n3. **The main claim currently lacks evidence.** \\u201cSignificantly accelerate,\\u201d \\u201csupplies data fast,\\u201d and \\u201cdozens, not hundreds\\u201d require a campaign ledger and a budget-matched comparison.\\n4. **The results omission breaks the story.** The deck follows the outline through the workflow, then skips the outline\\u2019s largest time block and moves directly to future work.\\n5. **The physical system needs more definition.** The audience needs a labeled specimen, a clear explanation of \\u201ctensegrity-inspired,\\u201d interface design, remaining manual steps, and the measurement chain.\\n6. **Noise must be operational rather than rhetorical.** Saying the data are noisy is insufficient. Replicates, periodic controls, drift, failures, and uncertainty should be shown.\\n7. **Experiment-first needs a scoped rationale.** The deck should not dismiss FEA, but it should explain why adaptive physical testing is the economical source of objective values for this campaign.\\n8. **The close is not a conclusion.** Future applications and a blank Q&A slide do not substitute for the measured finding and its conditions of transfer.\\n9. **Media are a communication and schedule risk.** Short clips can work, but multiple looping videos violate the signal-to-noise principle and create failure modes.\\n10. **The project specification needs reconciliation.** The supplied materials differ on six versus seven variables, whether FDM process parameters are optimized, and whether peak force is a constraint or part of the Pareto trade-off.\\n\\n## Five highest-priority slide-level revisions\\n\\n### 1. Slide 11: replace one empty slot with a three-slide quantitative results sequence\\n\\nUse full-sentence titles tied to observed data:\\n\\n- **11A:** \\u201cThe campaign used **N** specimens in **B** adaptive batches, including **R** replicates and **F** failed prints.\\u201d\\n- **11B:** \\u201cAt the same physical-test budget, adaptive selection reached **[measured feasible-performance result]** faster than **[predeclared Sobol/random baseline]**.\\u201d\\n- **11C:** \\u201cThe measured feasible Pareto front trades specific energy absorption against compaction efficiency while respecting a **[value and unit]** peak-force cap.\\u201d\\n\\nShow uncertainty, replicate spread, infeasible points, and representative specimen photographs or traces. If the campaign cannot support the baseline claim, change the talk\\u2019s main message to: **\\u201cWe evaluate whether noise-aware multi-objective Bayesian optimization can reduce the physical-test budget.\\u201d**\\n\\n### 2. Slide 7: state the actual constrained optimization problem\\n\\nReplace the current black-box/count design with:\\n\\n- the named decision variables and their types;\\n- a small batch entering the printer/test loop;\\n- objectives: maximize specific energy absorption and compaction efficiency;\\n- constraint: peak transmitted force below a stated cap;\\n- qNEHVI\\u2019s role in one plain sentence: it chooses a small next batch likely to expand the feasible trade-off frontier while accounting for uncertainty and noisy tests;\\n- the declared test budget, initialization size, and batch size.\\n\\nResolve six versus seven variables and whether processing parameters are decision variables before finalizing this slide.\\n\\n### 3. Slide 2: put the proxy boundary beside the hook\\n\\nKeep the Super Ball Bot clip, but add an image of the actual specimen and the visible sentence:\\n\\n> **\\u201cHere, a printed PLA\\u2013TPU T3 prism is a proxy for developing the workflow, not flight hardware.\\u201d**\\n\\nChange or qualify \\u201crobust, reusable solutions\\u201d unless the present campaign includes repeat-impact evidence.\\n\\n### 4. Slide 4: turn the vague need into the two-sided engineering decision\\n\\nRetitle it:\\n\\n> **\\u201cBecause exhaustive testing and campaign-grade model calibration are both costly, each physical specimen must be chosen for its information value.\\u201d**\\n\\nAdd one quantified physical-search estimate and one scoped calibration challenge. Do not say simulation is untrustworthy. State that FEA and lower-fidelity models remain possible screening or future multi-fidelity tools.\\n\\n### 5. Slides 10 and 13: separate the demonstrated loop from the conclusion\\n\\n- **Slide 10:** retitle to **\\u201cThe closed loop turns each measured test into the next small batch of specimens.\\u201d** Remove \\u201csignificantly accelerate\\u201d until the results demonstrate it. Mark automated and manual stages.\\n- **Slide 13:** retain the closed-loop thumbnail, add the best quantitative result, and state the deployment rule: **use the loop when tests are authoritative, each test is costly, the design space is mixed, and performance goals conflict.** Add \\u201cQuestions?\\u201d without clearing the conclusion from the screen.\\n\\n## Hidden-slide adjudication\\n\\n| Hidden slide | Recommendation | Rationale |\\n|---|---|---|\\n| **3: tensegrity usefulness/mechanism** | **Merge into slide 8** | The generic elbow and squishing GIFs add noise and risk an inaccurate mechanism; an annotated actual specimen and deformation sequence are stronger. |\\n| **5: generic BO explanation** | **Merge into slide 7; keep original as backup or delete** | DAC experts do not need a generic advocacy slide, but novices need one plain-language sentence and a concrete acquisition visual. |\\n| **6: specimen information value/experiment-first** | **Merge into slide 4; keep evidence-rich version as backup** | The argument is structurally necessary, but the current title is vague and the visual does not support the scoped calibration-versus-campaign-cost decision. |\\n| **11: results** | **Unhide and expand to three slides** | Results are the evidentiary center of the talk and were allocated five minutes in the agreed outline. An empty or hidden results slot makes the main claim untenable. |\\n\\n## Visible-slide claims needing evidence, hedging, or backup\\n\\n| Slide | Claim | Required treatment |\\n|---|---|---|\\n| **1** | \\u201cBuild better tensegrity structures faster\\u201d | Define \\u201cbetter\\u201d with the two objectives and force constraint; support \\u201cfaster\\u201d with the results comparison. |\\n| **2** | Tensegrity is robust and reusable for planetary landings | Cite the Super Ball Bot source; distinguish concept evidence from this study; provide repeat-impact evidence or hedge reusability. |\\n| **2** | Parachutes and retrorockets struggle in the stated conditions | Cite an authoritative aerospace source if retained in narration; avoid implying universal inadequacy. |\\n| **4** | Current tensegrity design is slow and resource intensive | Quantify candidate count, print/test time, hand-assembly burden, or calibration effort. |\\n| **7** | BO makes experiments affordable with limited, noisy data | Show campaign budget, noise characterization, replicates, baseline, and performance versus tests. Prepare qNEHVI and Gaussian-process details in backup. |\\n| **7** | Six variables, two input-data types, two objectives | Reconcile with the apparent seven-variable abstract specification and identify every quantity. Clarify \\u201cinput-data types.\\u201d |\\n| **8** | Multi-material AM supplies data fast | Report print-to-result cycle time and failure rate. |\\n| **8** | Single-build co-fabrication with no joining | Show interface and post-processing; use \\u201cno joining of PLA and TPU members\\u201d if other manual operations remain. |\\n| **9** | Accelerometers gather the relevant impact data | Show sensor locations and the derivation of transmitted force, specific energy absorption, and compaction efficiency. Reconcile this wording with the abstract\\u2019s instrumented-tup description. |\\n| **10** | The loop significantly accelerates optimization | Remove \\u201csignificantly\\u201d unless a defined baseline and uncertainty support it. Statistical significance should not be implied casually. |\\n| **12** | Workflow will transfer to lattices and other applications | Present as conditional future work with transfer criteria, not an achieved result. Attribute any external crutch-tip image and claim. |\\n\\n### Prepared backup slides\\n\\n1. Full design-variable table, bounds, encoding, and fixed versus optimized process parameters.\\n2. qNEHVI implementation, Gaussian-process assumptions, constraint treatment, batch size, and acquisition settings.\\n3. Noise plan: replicates, periodic control, heteroscedasticity, drift, outlier policy, and uncertainty propagation.\\n4. Budget-matched baseline definition and evaluation metric.\\n5. Force-cap rationale and full objective definitions.\\n6. Sensor chain, calibration, sample rate, filtering, fixture dynamics, and metric extraction.\\n7. PLA\\u2013TPU interface, conditioning, build layout, dimensional/mass deviations, and print-failure modes.\\n8. Scoped FEA rationale with citations or internal validation; possible analytical or multi-fidelity extensions.\\n9. Automation map and cycle-time breakdown.\\n10. Repeated-impact behavior if reusability remains in the opening.\\n\\n## Merged, ordered presenter TODO list\\n\\n### Must fix before the talk\\n\\n1. **Freeze the scientific specification.** Reconcile the number and type of decision variables, whether FDM parameters are optimized or controlled, the two objectives, the force constraint, treatment of print failures, and the measurement chain. Use one formulation across slides, abstract, paper, and Q&A.\\n2. **Set a results completion plan.** Establish dates and owners for campaign completion, repeat tests, data quality review, baseline analysis, and slide freeze. Predeclare the fallback claim if the baseline does not support \\u201cdozens rather than hundreds.\\u201d\\n3. **Build the three-slide results sequence.** Campaign ledger \\u2192 budget-matched baseline \\u2192 measured feasible Pareto front. Allocate approximately five minutes, as the agreed outline requires.\\n4. **Add the proxy statement to slide 2.** State plainly that the PLA\\u2013TPU T3 specimen is a workflow proxy and not flight hardware.\\n5. **Redesign slide 7.** Show the exact constrained multi-objective problem, test budget, small batches, uncertainty, noisy observations, and the role of qNEHVI.\\n6. **Remove unsupported result language.** Until supported, delete \\u201csignificantly\\u201d from slide 10 and avoid \\u201cdozens rather than hundreds\\u201d in the title, narration, or close.\\n7. **Clarify experiment-first positioning.** Merge the information-value argument into slide 4 and prepare an evidence-backed FEA backup slide.\\n8. **Make the experiment auditable.** On slide 9 or backup, reconcile accelerometers and the instrumented tup; show how all three performance metrics are calculated and how uncertainty is handled.\\n9. **De-risk every video.** Store local trimmed files, disable distracting loops, test them on presentation hardware, and include static fallback frames.\\n10. **Rehearse to 13:30\\u201314:00.** Protect the results and conclusion from being cut. Practice once without slides, consistent with Doumont\\u2019s preparation advice.\\n\\n### Should fix\\n\\n11. **Replace slide 8\\u2019s generic print emphasis with mechanism and materials evidence.** Label PLA, TPU, interface strategy, prestress status, deformation, post-processing, and failure modes.\\n12. **Quantify slide 4.** Give at least one campaign-relevant cost, duration, or candidate-count estimate.\\n13. **Show automation boundaries on slide 10.** Candidate selection and surrogate updating may be automated; slicing, handling, test setup, and testing remain manual according to the abstract.\\n14. **Convert slide 12 to a gated roadmap.** Separate demonstrated T3-proxy results from future lattices, flight materials, and terrestrial applications.\\n15. **Turn slide 13 into the conclusion.** Leave the audience with the best result and transfer rule while taking questions.\\n16. **Prepare technical backup slides.** Prioritize baseline fairness, noise, force-cap rationale, print failures, materials repeatability, and FEA positioning.\\n\\n### Polish\\n\\n17. Replace the slide 1 title with a specific message, such as **\\u201cPhysical Bayesian optimization searches multi-material tensegrity absorbers one small test batch at a time.\\u201d**\\n18. Left-align titles and use intentional line breaks; check every slide at six-per-page scale.\\n19. Use one purposeful visual per slide. Avoid decorative loading or elbow animations.\\n20. Standardize terminology: \\u201cBayesian optimization,\\u201d not \\u201cBays Opt\\u201d; \\u201ctensegrity-inspired\\u201d where appropriate; define specific energy absorption once.\\n21. Add citations directly beside borrowed footage, images, and quantitative claims.\\n\\n## Who the deck serves best and worst\\n\\n- **Best served:** **P6, the friendly industry generalist.** The visible sequence is visually intuitive, practical, and easy to reduce to \\u201cprint \\u2192 test \\u2192 learn \\u2192 repeat.\\u201d\\n- **Worst served:** **P1, the skeptical BO insider.** The current deck omits nearly every item needed to judge a DAC-10 contribution: exact problem formulation, budget, baseline, noise model, constraint handling, and empirical adaptive advantage.\\n\\nThat is **not the right trade-off for DAC-10**. The deck should remain accessible to P4 and P6, but its technical center must satisfy P1 without turning into a BO lecture. One rigorous optimization slide, three quantitative results slides, and targeted backup material would achieve that balance.\\n\\n## Predicted reception\\n\\n- **As is: 4/10.** The physical workflow and application are memorable, but the absent scope statement, empty results section, vague BO slide, and unsupported acceleration claim leave the DAC contribution unproven.\\n- **After the must-fixes: 8/10.** A scoped proxy claim, reconciled methods, fair budget-matched comparison, feasible Pareto front, and measured conclusion would turn the same visual story into a credible design-optimization application talk.\\n\\n## Discretionary analytical decisions\\n\\n- Evaluated only the visible slide order for the simulated in-room reactions, while using hidden slides and presenter notes to judge revision options.\\n- Treated planned placeholder visuals as if implemented according to the presenter\\u2019s notes, but judged the selected visual concept and associated stage risk rather than absent graphic quality.\\n- Prioritized the Draft 3 outline over conflicting Draft 1 wording because the prompt identifies it as the agreed story arc, scope framing, results plan, and timing plan.\\n- Rated objections qualitatively as moderate, high, or critical rather than assigning unsupported numerical probabilities.\\n- Recommended three results slides rather than preserving one reserved slot because the agreed outline specifies three distinct evidentiary products and allocates five minutes to results.\\n- Recommended merging hidden slides 3, 5, and 6 rather than simply unhiding them because their necessary ideas can be conveyed with higher signal-to-noise on existing slides.\\n- Treated the six-versus-seven-variable and optimized-versus-fixed-process-parameter differences as unresolved specification conflicts requiring reconciliation, rather than inferring which document is correct.\\n- Assessed claims against the supplied deck extraction, Draft 3 outline, Doumont notes, and submitted abstract only; no independent validation of cited literature or unseen experimental data was attempted.\"}", + "name": "submit_answer" + } + } + ] + }, + "logprob": null + } + } + }, + "created_at": "2026-07-26T02:31:56.252974Z" + }, + { + "id": "a99c27a6-1c48-4c1a-9aba-8b9a51b9b20a", + "trajectory_id": "afade0b1-f49e-4031-a86d-cb5ec4652536", + "trajectory_timestep": 0, + "step": "after_env_step", + "state": { + "transition": { + "timestep": 0, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ], + "info": null + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Tensegrity Slides \u2014 Draft 1 (extracted content)\n 2\u2192\n 3\u2192Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4\u2192\n 5\u2192\n 6\u2192## Slide 1 \u2014 layout: Title Only\n 7\u2192\n 8\u2192**On-slide text:**\n 9\u2192- **Title 1**: Let\u2019s build better tensegrity structures faster\n 10\u2192- **TextBox 4**: Marcus Madsen\n 11\u2192Undergraduate researcher under Sterling Baird, Ph.D.\n 12\u2192- **TextBox 6**: IDETC 2026\n 13\u2192\n 14\u2192**Non-text shapes / media / placeholders:**\n 15\u2192- [PICTURE (13)] Picture 3\n 16\u2192- [PICTURE (13)] Picture 8\n 17\u2192\n 18\u2192**Presenter notes:**\n 19\u2192> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20\u2192> \n 21\u2192> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22\u2192\n 23\u2192\n 24\u2192## Slide 2 \u2014 layout: Title Only\n 25\u2192\n 26\u2192**On-slide text:**\n 27\u2192- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings\u2014and to problems closer to home.\n 28\u2192\n 29\u2192**Non-text shapes / media / placeholders:**\n 30\u2192- [MEDIA (16)] Online Media 5\n 31\u2192\n 32\u2192**Presenter notes:**\n 33\u2192> Replace this video with a downloaded and trimmed version of just the fall. \n 34\u2192> \n 35\u2192> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle \u2014 thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander\u00a0is\u00a0the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit\u00a0and get back up\u00a0\u2014 it survives multiple drops and keeps working.\n 36\u2192\n 37\u2192\n 38\u2192## Slide 3 (HIDDEN) \u2014 layout: Title Only\n 39\u2192\n 40\u2192**On-slide text:**\n 41\u2192- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42\u2192- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43\u2192- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44\u2192\n 45\u2192**Presenter notes:**\n 46\u2192> This might be better explained purely vocally while on the previous slide.\n 47\u2192\n 48\u2192\n 49\u2192## Slide 4 \u2014 layout: Title Only\n 50\u2192\n 51\u2192**On-slide text:**\n 52\u2192- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53\u2192- **TextBox 3**: Iteration\n 54\u2192- **TextBox 5**: Building\n 55\u2192- **TextBox 7**: Testing\n 56\u2192\n 57\u2192**Non-text shapes / media / placeholders:**\n 58\u2192- [MEDIA (16)] AdobeStock_207717222\n 59\u2192- [MEDIA (16)] Online Media 2\n 60\u2192- [PICTURE (13)] Picture 14\n 61\u2192- [PICTURE (13)] Picture 16\n 62\u2192- [PICTURE (13)] Picture 18\n 63\u2192\n 64\u2192**Presenter notes:**\n 65\u2192> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn\u2019t become distracting because it\u2019s fun to watch it spin.\n 66\u2192> \n 67\u2192> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68\u2192\n 69\u2192\n 70\u2192## Slide 5 (HIDDEN) \u2014 layout: Title Only\n 71\u2192\n 72\u2192**On-slide text:**\n 73\u2192- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74\u2192- **TextBox 14**: Baird et al. Matter (2022)\n 75\u2192\n 76\u2192**Non-text shapes / media / placeholders:**\n 77\u2192- [GROUP (6)] Group 2\n 78\u2192\n 79\u2192**Presenter notes:**\n 80\u2192> Consult Sterling on this slide. I don\u2019t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it\u2019s well enough known that it\u2019d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81\u2192\n 82\u2192\n 83\u2192## Slide 6 (HIDDEN) \u2014 layout: Title Only\n 84\u2192\n 85\u2192**On-slide text:**\n 86\u2192- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87\u2192- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88\u2192\n 89\u2192**Presenter notes:**\n 90\u2192> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91\u2192\n 92\u2192\n 93\u2192## Slide 7 \u2014 layout: Title Only\n 94\u2192\n 95\u2192**On-slide text:**\n 96\u2192- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97\u2192- **TextBox 5**: 6 variables\n 98\u2192- **TextBox 7**: 2 types of input data\n 99\u2192- **TextBox 9**: 2 objectives\n100\u2192- **TextBox 17**: *image of blackbox*\n101\u2192\n102\u2192**Non-text shapes / media / placeholders:**\n103\u2192- [LINE (9)] Connector: Curved 11\n104\u2192- [LINE (9)] Connector: Curved 13\n105\u2192- [LINE (9)] Connector: Curved 15\n106\u2192\n107\u2192**Presenter notes:**\n108\u2192> Very rough slide as I\u2019m not entirely sure what graphic or text to put on this, partially because I\u2019m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109\u2192\n110\u2192\n111\u2192## Slide 8 \u2014 layout: Title Only\n112\u2192\n113\u2192**On-slide text:**\n114\u2192- **Title 1**: Multi-material additive manufacturing supplies that data fast \u2014 single-build co-fabrication, with no joining of parts.\n115\u2192\n116\u2192**Non-text shapes / media / placeholders:**\n117\u2192- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118\u2192- [PICTURE (13)] Picture 4\n119\u2192\n120\u2192**Presenter notes:**\n121\u2192> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122\u2192\n123\u2192\n124\u2192## Slide 9 \u2014 layout: Title Only\n125\u2192\n126\u2192**On-slide text:**\n127\u2192- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128\u2192- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129\u2192- **TextBox 5**: *placeholder for slo motion of drop test*\n130\u2192\n131\u2192\n132\u2192## Slide 10 \u2014 layout: Title Only\n133\u2192\n134\u2192**On-slide text:**\n135\u2192- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136\u2192- **TextBox 3**: Printing\n137\u2192- **TextBox 5**: Testing\n138\u2192- **TextBox 7**: BO feed\n139\u2192\n140\u2192**Non-text shapes / media / placeholders:**\n141\u2192- [PICTURE (13)] Picture 14\n142\u2192- [PICTURE (13)] Picture 16\n143\u2192- [PICTURE (13)] Picture 18\n144\u2192- [PICTURE (13)] Graphic 8\n145\u2192\n146\u2192**Presenter notes:**\n147\u2192> I think it might be better if we replaced \u201cprinting\u201d with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148\u2192\n149\u2192\n150\u2192## Slide 11 (HIDDEN) \u2014 layout: Title Only\n151\u2192\n152\u2192**On-slide text:**\n153\u2192- **Title 1**: Reserved for results\n154\u2192\n155\u2192**Presenter notes:**\n156\u2192> Would be helpful if we could show that this process works in this case.\n157\u2192\n158\u2192\n159\u2192## Slide 12 \u2014 layout: Title Only\n160\u2192\n161\u2192**On-slide text:**\n162\u2192- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163\u2192- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164\u2192- **TextBox 3**: *image to show lattice structure\n165\u2192\n166\u2192\n167\u2192## Slide 13 \u2014 layout: Title Only\n168\u2192\n169\u2192**Non-text shapes / media / placeholders:**\n170\u2192- [PICTURE (13)] Picture 3\n171\u2192\n172\u2192**Presenter notes:**\n173\u2192> Slide for questions at the end\n174\u2192\n175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "info": { + "start_ts": 8147.330900226, + "end_ts": 8147.337548875 + } + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Presentation \u2014 Doumont Presentation Structure Template\n 2\u2192\n 3\u2192Transcribed from Jean-luc Doumont's presentation-structure template\n 4\u2192([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5\u2192adapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\n 6\u2192All rights reserved. Can be downloaded from .\n 7\u2192\n 8\u2192> To design your presentation, write down your ideas for each component below.\n 9\u2192> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10\u2192\n 11\u2192**Draft 3** \u2014 applies the inline review comments from @sgbaird on PR #84 and the\n 12\u2192priority revisions from the\n 13\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14\u2192Summaries of what changed are at the bottom of this file\n 15\u2192([Draft 2 \u2192 3](#changes-from-draft-2), [Draft 1 \u2192 2](#changes-from-draft-1)).\n 16\u2192\n 17\u2192**Scope note (frame everything below with this):** the study presented is the\n 18\u2192optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19\u2192processing parameters** (e.g., nozzle temperature, print speed). PLA\u2013TPU is a\n 20\u2192**proxy system** \u2014 not flight material \u2014 used to prototype a self-driving-lab\n 21\u2192style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22\u2192The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23\u2192beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24\u2192\n 25\u2192## Opening\n 26\u2192\n 27\u2192### Attention getter\n 28\u2192\n 29\u2192*A way to lead the audience to the need efficiently*\n 30\u2192\n 31\u2192Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32\u2192landings \u2014 and to problems closer to home.\n 33\u2192\n 34\u2192One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35\u2192clip) do the work:\n 36\u2192\n 37\u2192> Landing a payload on another planet means surviving an impact in conditions\n 38\u2192> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n 39\u2192> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40\u2192> structure: the lander *is* the energy absorber. And unlike crushable\n 41\u2192> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42\u2192> hit **and get back up** \u2014 it survives multiple drops and keeps working.\n 43\u2192\n 44\u2192Candidate Super Ball Bot videos to show (verified links):\n 45\u2192\n 46\u2192- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n 47\u2192 (official channel); the canonical overview.\n 48\u2192- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49\u2192 \u2014 Wall Street Journal; prototype drop footage.\n 50\u2192- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51\u2192 \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n 52\u2192\n 53\u2192Immediately after the hook, state the present study scope in one sentence\n 54\u2192(per the scope note above): *we study a printed PLA\u2013TPU T3 prism as a proxy\n 55\u2192system for prototyping an autonomous tensegrity-optimization workflow* \u2014 so\n 56\u2192the planetary framing never outruns the demonstrated scope.\n 57\u2192\n 58\u2192\n 60\u2192\n 61\u2192### Need\n 62\u2192\n 63\u2192*A difference between actual and desired situations*\n 64\u2192\n 65\u2192**Actual situation** (a two-sided pincer \u2014 stated as a scoped engineering\n 66\u2192judgment, not an absolute):\n 67\u2192\n 68\u2192- For these structures, obtaining trustworthy objective values from a\n 69\u2192 validated high-fidelity model would require substantial calibration of\n 70\u2192 interface, rate, contact, and defect behavior \u2014 and even heavy-duty\n 71\u2192 simulation falls short of the experiments we are running: peak transmitted\n 72\u2192 shock is a millisecond-scale transient that is notoriously difficult to\n 73\u2192 resolve numerically. This holds even for idealized cable\u2013strut tensegrities\n 74\u2192 in their actual engineering materials (steel or similar), not just for\n 75\u2192 printed PLA\u2013TPU.\n 76\u2192 \n 79\u2192- Exhaustive physical search fails on cost: a traditional design of\n 80\u2192 experiments over this relatively high-dimensional space (geometry + FDM\n 81\u2192 process parameters) would demand hundreds of specimens, each costing print\n 82\u2192 and test time.\n 83\u2192- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84\u2192 more complex structures \u2014 especially tensegrity *lattices* \u2014 become\n 85\u2192 extremely difficult and ultimately infeasible to hand-build.\n 86\u2192\n 87\u2192**Desired situation:**\n 88\u2192\n 89\u2192- Trustworthy performance data without hand assembly (easy creation of\n 90\u2192 tensegrity structures, scalable beyond a single prism).\n 91\u2192- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n 92\u2192 not the hundreds a traditional design of experiments would require.\n 93\u2192\n 94\u2192### Task\n 95\u2192\n 96\u2192*What I decided/was asked to do to address the need*\n 97\u2192\n 98\u2192Build a closed-loop design\u2013print\u2013test workflow that optimizes a **T3-prism\n 99\u2192tensegrity-inspired energy absorber together with its FDM processing\n100\u2192parameters** (e.g., temperature, print speed) directly from real, measured\n101\u2192impact data \u2014 maximizing energy absorbed per gram (specific energy\n102\u2192absorption) and compaction efficiency, subject to a cap on the peak force\n103\u2192transmitted to the payload. Extensions beyond the T3 prism are future work.\n104\u2192\n105\u2192### Main message\n106\u2192\n107\u2192*The one sentence I want my audience to remember*\n108\u2192\n109\u2192By closing the loop between multi-material 3D printing and Bayesian\n110\u2192optimization, we can optimize tensegrity-inspired energy absorbers directly\n111\u2192from real impact data \u2014 in dozens of prints, not the hundreds a traditional\n112\u2192design of experiments would demand.\n113\u2192\n114\u2192\n118\u2192\n119\u2192### Preview\n120\u2192\n121\u2192*A map of the body (ideally three points, max. five)*\n122\u2192\n123\u21921. When model calibration and exhaustive testing are both costly, each\n124\u2192 physical specimen must be chosen for its information value \u2014 so the\n125\u2192 measured experiment is our source of truth.\n126\u21922. Bayesian optimization makes physical experimentation affordable: it handles\n127\u2192 noisy measurements, small batches, and the constrained multi-objective\n128\u2192 trade-off \u2014 and Honegumi let us scaffold it in minutes.\n129\u21923. Multi-material additive manufacturing closes the loop: single-build PLA\u2013TPU\n130\u2192 co-fabrication turns each proposed design into test data in hours, with no\n131\u2192 joining of parts.\n132\u21924. *(Optional fourth point, if time allows)* The same closed loop points at\n133\u2192 what comes next: tensegrity lattices, flight-relevant materials, and\n134\u2192 safety hardware on Earth.\n135\u2192\n136\u2192## Body\n137\u2192\n138\u2192### Point 1\n139\u2192\n140\u2192When model calibration and exhaustive testing are both costly, each physical\n141\u2192specimen must be chosen for its information value.\n142\u2192\n143\u2192- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144\u2192 these structures, a sufficiently calibrated multiphysics model is expensive\n145\u2192 to build relative to the design campaign itself \u2014 and the hardest quantity,\n146\u2192 peak transmitted shock, is a millisecond-scale transient that even\n147\u2192 heavy-duty simulation struggles to resolve. The gap persists in the actual\n148\u2192 engineering materials a real lander would use (steel struts and cables),\n149\u2192 so this is not merely an artifact of printed PLA\u2013TPU.\n150\u2192- The alternative \u2014 sweeping the design space physically \u2014 fails on cost: a\n151\u2192 traditional design of experiments over geometry plus FDM process parameters\n152\u2192 would take hundreds of specimens, each needing print + test time.\n153\u2192- Therefore: treat the physical experiment as the authoritative source of\n154\u2192 objective values, and be ruthless about *which* experiments to run.\n155\u2192- Anticipate the FEA-veteran pushback (\"inconvenient \u2260 untrustworthy\"): frame\n156\u2192 it as a cost-and-dependence decision, and keep a backup slide with the\n157\u2192 literature (or our own validation) on simulation\u2013experiment discrepancy for\n158\u2192 impact transients.\n159\u2192\n160\u2192### Transition\n161\u2192\n162\u2192If every data point must be a real experiment, we need a method that extracts\n163\u2192the most from every specimen \u2014 we need to make the process radically more\n164\u2192sample-efficient.\n165\u2192\n166\u2192### Point 2\n167\u2192\n168\u2192Bayesian optimization makes physical experimentation affordable, even with\n169\u2192limited, noisy data.\n170\u2192\n171\u2192- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172\u2192 visual in the main talk; implementation detail goes to backup slides.\n173\u2192 Spoken version:\n174\u2192 > \"After each batch, the model estimates both expected performance and\n175\u2192 > uncertainty across the design space. The acquisition rule selects a small\n176\u2192 > next batch that is most likely to expand the feasible trade-off frontier,\n177\u2192 > while accounting for noisy tests and print failures.\"\n178\u2192- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179\u2192 motivate a noise-aware acquisition function (qNEHVI) rather than\n180\u2192 non-noise-aware alternatives. (Backup slide: how observation noise is\n181\u2192 actually modeled \u2014 replicates, noise identification \u2014 and budget-matched\n182\u2192 baselines such as Sobol/random sampling.)\n183\u2192- Small parallel batches: the surrogate proposes the next batch of specimens\n184\u2192 to print, balancing exploration and exploitation.\n185\u2192- Constrained multi-objective by construction: **objectives** = maximize\n186\u2192 specific energy absorption and compaction efficiency; **constraint** = peak\n187\u2192 transmitted force must stay under a cap; the deliverable is the *feasible*\n188\u2192 Pareto front between the two objectives among force-cap-satisfying designs.\n189\u2192 (Keep this formulation consistent everywhere \u2014 do not present \"SEA vs. peak\n190\u2192 force\" as the trade-off.)\n191\u2192- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192\u2192 this advanced task (noisy, batched, constrained, multi-objective) in\n193\u2192 minutes using [Honegumi](https://honegumi.readthedocs.io/) \u2014 the audience\n194\u2192 can do the same for their own problems; show the code-template picker\n195\u2192 briefly so they see how low the barrier is.\n196\u2192\n197\u2192### Transition\n198\u2192\n199\u2192The optimizer is no longer the bottleneck \u2014 the limit is now providing real\n200\u2192data quickly enough.\n201\u2192\n202\u2192### Point 3\n203\u2192\n204\u2192Multi-material additive manufacturing supplies that data fast \u2014 single-build\n205\u2192co-fabrication, with no joining of parts.\n206\u2192\n207\u2192- Single-build PLA\u2013TPU co-fabrication: rigid struts and flexible tension\n208\u2192 network in one print \u2014 no joining or hand assembly of members, which closes\n209\u2192 the \"easy creation\" gap from the Need and is what makes future lattices\n210\u2192 feasible at all. (Be precise: if support removal, trimming, or conditioning\n211\u2192 remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212\u2192 assembly.\")\n213\u2192- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214\u2192 as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215\u2192 pre-tension status) plus a short deformation sequence \u2014 so the EDL engineer\n216\u2192 doesn't file it as a \"crush structure\" and the grad student can tell it from\n217\u2192 an ordinary lattice.\n218\u2192- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219\u2192 tested specimen (quasi-static compression + instrumented drop-weight impact)\n220\u2192 in hours. (Backup slide: representative print-to-result time breakdown and\n221\u2192 which steps remain manual \u2014 candidate selection is automated; slicing,\n222\u2192 handling, and testing are not yet.)\n223\u2192- Now we have the data we need to complete the loop.\n224\u2192\n225\u2192### Point 4 (optional \u2014 future applications)\n226\u2192\n227\u2192*Include if timing allows; otherwise fold into the Conclusion.*\n228\u2192\n229\u2192The same closed loop transfers wherever tests are authoritative, each test is\n230\u2192costly, the design space is mixed, and several performance goals conflict:\n231\u2192\n232\u2192- Tensegrity *lattices* and more complex architectures \u2014 exactly where hand\n233\u2192 assembly becomes infeasible and single-build co-fabrication pays off.\n234\u2192- Migration from the PLA\u2013TPU proxy toward flight-relevant materials and\n235\u2192 impact regimes.\n236\u2192- Safety hardware on Earth (e.g., the tensegrity crutch tip \u2014 future work).\n237\u2192- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238\u2192 handling, testing) in the loop.\n239\u2192\n240\u2192### Evidence / results\n241\u2192\n242\u2192*What we found \u2014 ground the conclusion in shown data*\n243\u2192\n244\u2192Build the talk around a quantitative three-slide sequence (per the\n245\u2192mock-audience report; insert observed values once the campaign completes):\n246\u2192\n247\u21921. **Campaign ledger** \u2014 \"The campaign used N specimens across B adaptive\n248\u2192 batches under a fixed physical-test budget\": initialization vs. adaptive\n249\u2192 batches, replicates, print failures, cycle time, and the numerical value\n250\u2192 (and rationale) of the force cap.\n251\u21922. **Budget-matched baseline** \u2014 \"Adaptive selection improved the feasible\n252\u2192 design set faster than [predeclared baseline]\": performance vs. number of\n253\u2192 physical tests against Sobol/random sampling at the same budget, with\n254\u2192 uncertainty where available.\n255\u21923. **Measured feasible Pareto front** \u2014 specific energy absorption vs.\n256\u2192 compaction efficiency among designs satisfying the force cap, with\n257\u2192 replicate spread, marked infeasible designs, and specimen photos or\n258\u2192 force\u2013time traces for representative Pareto designs.\n259\u2192\n260\u2192Supporting raw-data candidates: drop-test force\u2013time traces, Sobol /\n261\u2192first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262\u2192\n263\u2192### Suggested 15-minute allocation\n264\u2192\n265\u2192*(From the mock-audience report \u2014 results get the largest block.)*\n266\u2192\n267\u2192| Time | Content |\n268\u2192|---|---|\n269\u2192| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270\u2192| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271\u2192| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272\u2192| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273\u2192| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274\u2192| 1 min | Limitations and transfer conditions |\n275\u2192| 1 min | Measured conclusion + bookend |\n276\u2192\n277\u2192Move kernel choices, encodings, and other implementation detail to backup\n278\u2192slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279\u2192[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280\u2192\n281\u2192## Closing\n282\u2192\n283\u2192### Review\n284\u2192\n285\u2192*A recap of the body, leading into the conclusion*\n286\u2192\n287\u2192Using real, measured impact data from additively manufactured\n288\u2192tensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\n289\u2192optimization routine \u2014 we rapidly iterate toward better energy absorbers.\n290\u2192This feedback loop makes tensegrity optimization dramatically faster: dozens\n291\u2192of prints instead of the hundreds a traditional design of experiments over\n292\u2192this design space would require.\n293\u2192\n294\u2192### Conclusion\n295\u2192\n296\u2192*What the above means to the audience in the end*\n297\u2192\n298\u2192Because the loop runs on physical measurements, it is useful precisely where\n299\u2192model calibration is not worth its cost \u2014 and the same closed-loop pattern\n300\u2192should transfer (a conditional claim, pending evidence) to other additively\n301\u2192manufactured architectures whose performance is dominated by\n302\u2192hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303\u2192*use this loop when tests are authoritative, each test is costly, the design\n304\u2192space is mixed, and several performance goals conflict.* For tensegrity\n305\u2192specifically, it opens a path from this T3 proxy study toward lattices,\n306\u2192flight-relevant materials, payload protection on other planets, and safety\n307\u2192hardware here on Earth (e.g., the crutch tip, as future work).\n308\u2192\n309\u2192### Close\n310\u2192\n311\u2192*A way to end the presentation clearly and elegantly*\n312\u2192\n313\u2192Return to the opening image: the next planetary lander's energy absorber \u2014 a\n314\u2192structure that has to survive not one impact but many \u2014 doesn't have to take\n315\u2192years of hand-built prototypes; with this loop, candidate designs can be\n316\u2192designed, printed, and tested against real impacts in weeks instead of months\n317\u2192or longer. End on the measured engineering result and its transfer condition,\n318\u2192then the bookend: every method that compresses design time moves our ventures\n319\u2192\u2014 on this planet and beyond \u2014 forward.\n320\u2192\n321\u2192---\n322\u2192\n323\u2192## Changes from Draft 2\n324\u2192\n325\u2192Applied from @sgbaird's inline review comments on PR #84 and the\n326\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327\u2192\n328\u2192**From the inline review comments:**\n329\u2192\n330\u21921. **Reusability in the hook** (line 22): the tensegrity lander survives\n331\u2192 multiple drops, unlike crushable/sacrificial alternatives.\n332\u21922. **Proxy-system framing** (lines 49, 87): PLA\u2013TPU would never fly; it is a\n333\u2192 proxy for prototyping a self-driving-lab-style workflow \u2014 stated in a\n334\u2192 scope note up front and re-stated right after the hook.\n335\u21923. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336\u2192 heavy-duty simulation falls short of the experiments (millisecond-scale\n337\u2192 peak shock), not that it merely costs too much.\n338\u21924. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339\u2192 complex structures and especially lattices are not.\n340\u21925. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341\u2192 (temperature, print speed, \u2026) only; the rest is explicitly future work.\n342\u21926. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343\u2192 literature search (or validation of experiments against simulation), plus\n344\u2192 the millisecond-resolution peak-shock argument.\n345\u21927. **Optional Point 4 added** (line 97): future applications, with a note it\n346\u2192 can fold into the Conclusion if time is short.\n347\u21928. **Steel-materials point** (line 104): simulations fall short for the\n348\u2192 actual engineering materials (steel struts/cables), not just printed\n349\u2192 PLA\u2013TPU.\n350\u21929. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351\u2192 in minutes, pitched so the audience sees they can use it too.\n352\u219210. **\"Thousands\" \u2192 \"hundreds\"** (line 173): baseline is now a traditional\n353\u2192 design of experiments over a high-dimensional space (hundreds), in the\n354\u2192 main message, Need, Point 1, and Review.\n355\u219211. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356\u2192 or longer\" for candidate designs.\n357\u2192\n358\u2192**From the mock-audience report's priority revisions:**\n359\u2192\n360\u219212. **Quantitative three-slide results sequence** (Priority 1): campaign\n361\u2192 ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front, with a\n362\u2192 fallback main-message wording if the campaign can't yet support the\n363\u2192 efficiency claim.\n364\u219213. **Anti-simulation premise replaced with a scoped cost decision**\n365\u2192 (Priority 2): Point 1 retitled to the information-value message;\n366\u2192 \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367\u2192 anticipated with a backup slide.\n368\u219214. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369\u2192 implementation detail moved to backup slides; Q&A prep pointed at the\n370\u2192 report's claims-vs-evidence table.\n371\u219215. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372\u2192 the main talk, machinery in backup slides; the misleading\n373\u2192 \"qNEHVI over standard expected improvement\" comparison replaced with\n374\u2192 \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375\u219216. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376\u2192 sequence in Point 3, so the structure isn't remembered as a generic crush\n377\u2192 structure or lattice.\n378\u219217. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379\u2192 co-fabrication / no joining of parts,\" with a note to list remaining\n380\u2192 manual steps and an automation-status caveat.\n381\u219218. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382\u2192 compaction efficiency, constraint = force cap, deliverable = *feasible*\n383\u2192 Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384\u219219. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385\u2192 transfer claim marked conditional, and the close ends on the measured\n386\u2192 result before the bookend.\n387\u2192\n388\u2192## Changes from Draft 1\n389\u2192\n390\u2192Feedback from the PR #84 review, applied in Draft 2:\n391\u2192\n392\u21921. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n393\u2192 dozens-not-thousands claim); body and message now carry the same story.\n394\u21922. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n395\u2192 manuscript's positioning (#75/#76) is that the physical experiment is the\n396\u2192 source of truth, explicitly avoiding calibrated FE simulation.\n397\u21923. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n398\u2192 parametric design space and avoid an over-claim to a design-automation\n399\u2192 audience.\n400\u21924. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401\u2192 energy absorption and compaction efficiency, cap peak transmitted force.\n402\u21925. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403\u2192 *and* the space is too big to brute-force \u2014 which makes BO + AM the\n404\u2192 inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405\u2192 above.)*\n406\u21926. **Point 1 given its own content** (the *why* behind the inefficiency)\n407\u2192 instead of restating the Need.\n408\u21927. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409\u2192 the specifics (noise-aware acquisition, small batches, multi-objective).\n410\u21928. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411\u2192 (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412\u2192 above.)*\n413\u21929. **Evidence/results slot added** before the Closing, so the Conclusion is\n414\u2192 grounded in shown data rather than three hedged \"may\"s.\n415\u219210. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n416\u2192 Ball Bot visual; verified video links included.\n417\u219211. **Close bookends the opening** (the next lander's absorber, designed in\n418\u2192 weeks) before the broader humankind line.\n419\u219212. **Spelling**: Doumont (was \"Duomont\").", + "name": "read", + "tool_call_id": "call_93haurOMDGiH2mQFFbaMaYKn", + "info": { + "start_ts": 8147.331065886, + "end_ts": 8147.336467845 + } + }, + { + "role": "tool", + "content": " 1\u2192# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n 2\u2192\n 3\u2192Video: (assigned in #83)\n 4\u2192\n 5\u2192Transcribed from @me-madsen's handwritten notes\n 6\u2192([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7\u2192dated 2026-07-15).\n 8\u2192\n 9\u2192## Overview\n10\u2192\n11\u2192A presentation is all about the right content, displayed the right way, at the\n12\u2192right time.\n13\u2192\n14\u2192- Verbal communication is sequential.\n15\u2192- Message is *interpretation* of the information.\n16\u2192 - Ensure the audience can get the most out of what you're doing.\n17\u2192 - Don't just give them information \u2014 help them understand it, interpret it,\n18\u2192 and internalize it.\n19\u2192\n20\u2192## Doumont's suggested rules for a presentation\n21\u2192\n22\u21920. Ensure you have **messages** for your audience, not just words.\n23\u21921. **Adapt to your audience.**\n24\u2192 - Think like the audience, not the speaker.\n25\u21922. **Maximize signal-to-noise ratio.**\n26\u2192 - Reduce noise (animations, laser pointers, anything that pulls attention\n27\u2192 away from your message).\n28\u21923. **Use effective redundancy.**\n29\u2192 - Tell your message in word *and* on slide: someone deaf should understand\n30\u2192 your messages just as well as someone blind.\n31\u2192 - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32\u2192 date).\n33\u2192\n34\u2192## When creating slides\n35\u2192\n36\u2192- If the audience doesn't need it, why put it on the slides?\n37\u2192- People shouldn't have to count the items on a slide to know how many are\n38\u2192 there.\n39\u2192- Don't use your slides as your \"presentation notes.\"\n40\u2192 - Don't show your notes to the audience.\n41\u2192 - **A bad slide is worse than no slide at all.**\n42\u2192- **One message per slide** is a good rule of thumb.\n43\u2192 - Prioritize the *so what* (or *therefore what*) of your message.\n44\u2192- Make it easy to interpret the information on the slide:\n45\u2192 - 3\u20135 items at most.\n46\u2192 - Align the title left.\n47\u2192 - Do not clutter slides with repetitive or unnecessary information: logos,\n48\u2192 affiliation, date, slide numbers.\n49\u2192- Doumont's suggested workflow:\n50\u2192 1. **Start with the title.**\n51\u2192 - Use a full sentence in the title.\n52\u2192 - The title is the main message of the slide.\n53\u2192 - Make it easy to read: left-aligned, with intentional line breaks.\n54\u2192 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55\u2192- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56\u2192\n57\u2192## General preparation\n58\u2192\n59\u2192- Planning for audience, content, and structure matters more than materials\n60\u2192 (i.e., slides).\n61\u2192- Try using one sheet of paper for presentation preparation.\n62\u2192- It's most important that your presentation is:\n63\u2192 - well planned, structured, and delivered;\n64\u2192 - **if you don't have a lot of time to prepare, don't focus on making\n65\u2192 slides.**\n66\u2192- Practice the presentation a few times. Practice at least once *without* your\n67\u2192 slideshow!\n68\u2192- If doing a handout, keep it separate from the slides you're preparing.\n69\u2192- Impress the audience with your body.\n70\u2192 - Use gestures, not laser pointers.\n71\u2192 - \"Control\" the audience with your body \u2014 keep their attention on your\n72\u2192 presentation.\n73\u2192\n74\u2192## Impressions / what to apply to the IDETC presentation\n75\u2192\n76\u2192\n77\u2192\n78\u2192## Questions / things to discuss\n79\u2192\n80\u2192", + "name": "read", + "tool_call_id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "info": { + "start_ts": 8147.331133416, + "end_ts": 8147.338304015 + } + }, + { + "role": "tool", + "content": " 1\u2192\\documentclass[10pt]{article}\n 2\u2192\n 3\u2192% --- Packages ---\n 4\u2192\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5\u2192\\usepackage[hidelinks]{hyperref}\n 6\u2192\\usepackage{parskip}\n 7\u2192\\usepackage{graphicx}\n 8\u2192\\usepackage{caption}\n 9\u2192\\usepackage{titlesec}\n 10\u2192\\usepackage{wrapfig}\n 11\u2192\\usepackage[numbers,sort&compress]{natbib}\n 12\u2192\n 13\u2192% --- Formatting ---\n 14\u2192\\setlength{\\parskip}{0.2em}\n 15\u2192\\setlength{\\parindent}{0em}\n 16\u2192\\titleformat*{\\section}{\\large\\bfseries}\n 17\u2192\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18\u2192\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19\u2192\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20\u2192\\setlength{\\emergencystretch}{2em}\n 21\u2192\\setlength{\\intextsep}{0.2em}\n 22\u2192\\bibliographystyle{abbrvnat}\n 23\u2192\n 24\u2192\\begin{document}\n 25\u2192\n 26\u2192\\begin{center}\n 27\u2192{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28\u21923D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29\u2192\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30\u2192\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31\u2192\\end{center}\n 32\u2192\n 33\u2192\\vspace{0.4em}\n 34\u2192\n 35\u2192\\section*{Abstract}\n 36\u2192Tensegrity-inspired architectures---rigid struts suspended within a\n 37\u2192continuous flexible network---can exhibit tunable nonlinear\n 38\u2192force--displacement responses and favorable energy absorption per unit\n 39\u2192mass, motivating their use in lightweight energy-absorbing and protective\n 40\u2192structures\n 41\u2192\\citep{skelton2009tensegrity,pajunen2019design}.\n 42\u2192Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43\u2192struts and flexible (TPU) elements in a single build\n 44\u2192\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45\u2192space---strut geometry, tension-element cross-section, connectivity\n 46\u2192topology, and unit-cell tiling---is too large to explore by trial and\n 47\u2192error. We present a closed-loop experimental campaign that uses\n 48\u2192Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49\u2192to drive a design--print--test workflow operating directly on physical\n 50\u2192measurements, without relying on calibrated finite-element simulation\n 51\u2192for objective evaluation.\n 52\u2192\n 53\u2192\\section{Introduction and Motivation}\n 54\u2192\n 55\u2192\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56\u2192\\centering\n 57\u2192\\vspace{-0.4em}\n 58\u2192\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59\u2192\\captionsetup{font=small}\n 60\u2192\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61\u2192Candidate tensegrity-inspired unit cells are instantiated from a\n 62\u2192parameter vector, fabricated by multi-material FDM, and tested under\n 63\u2192quasi-static compression and drop-weight impact. Measured peak force,\n 64\u2192specific energy absorption (SEA), and compaction efficiency update a\n 65\u2192Gaussian-process surrogate that proposes the next batch of designs.}\n 66\u2192\\label{fig:overview}\n 67\u2192\\vspace{-0.4em}\n 68\u2192\\end{wrapfigure}\n 69\u2192\n 70\u2192Tensegrity structures combine compression-only members with a\n 71\u2192pre-tensioned tension network to achieve stiffness without rigid\n 72\u2192joinery~\\citep{skelton2009tensegrity}. Their\n 73\u2192nonlinear, often load-limiting force--displacement response makes them\n 74\u2192attractive for impact mitigation, packaging, and wearable protective\n 75\u2192gear~\\citep{pajunen2019design}. Idealized\n 76\u2192cable--strut tensegrities are difficult to manufacture at scale, but\n 77\u2192\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78\u21923D printing reproduce key tensegrity behaviors---including\n 79\u2192post-buckling stability and load-limiting force--displacement\n 80\u2192response~\\citep{pajunen2019design}---motivating our extension to\n 81\u2192multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82\u2192elements. PLA provides rigid struts; TPU provides flexible,\n 83\u2192rate-dependent tension elements whose viscoelasticity is expected to\n 84\u2192contribute hysteretic energy dissipation under impact loading,\n 85\u2192consistent with the energy-absorbing behavior reported for rigid--TPU\n 86\u2192multi-material prints~\\citep{khatri2024energy}.\n 87\u2192\n 88\u2192The challenge is that small geometric and topological changes can\n 89\u2192produce large changes in the measured response, and high-fidelity\n 90\u2192simulation of FDM specimens is computationally expensive and often\n 91\u2192struggles to capture physical defects such as interfacial slip and\n 92\u2192TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93\u2192trial-and-error sweeps over the joint design space are also infeasible:\n 94\u2192even modest parameter resolutions yield thousands of candidates, each\n 95\u2192requiring print and test time. We instead treat the physical\n 96\u2192experiment as the source of truth and use BO to select which specimens\n 97\u2192are most informative to fabricate next---an approach used in related\n 98\u2192closed-loop materials and structures\n 99\u2192campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100\u2192Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101\u2192set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102\u2192designs using parallel, noisy multi-objective BO driven by measured\n103\u2192$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104\u2192\n105\u2192\\section{Approach}\n106\u2192\\subsection{Closed-Loop Workflow}\n107\u2192Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108\u2192proceeds in four steps: (i)~a parameterized unit-cell design is\n109\u2192instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110\u2192is sliced and printed on a multi-material FDM system; (iii)~it is\n111\u2192tested under quasi-static compression and instrumented drop-weight\n112\u2192impact; and (iv)~the resulting performance metrics update a\n113\u2192Gaussian-process (GP) surrogate, which a batch acquisition function\n114\u2192queries to recommend the next set of candidates. Surrogate updating\n115\u2192and experiment selection are automated; specimen handling, slicing,\n116\u2192and test setup remain manual.\n117\u2192\n118\u2192\\subsection{Design Space}\n119\u2192We parameterize a family of unit cells using a core-wrapping\n120\u2192architecture in which rigid PLA struts are enclosed by continuous TPU\n121\u2192skins; the wrapping rationale follows multi-material FDM strategies in\n122\u2192which rigid panels are wrapped by stretchable soft material to prevent\n123\u2192delamination and improve interfacial bonding at the rigid--flexible\n124\u2192interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125\u2192comprises continuous geometric variables (strut diameter and length,\n126\u2192tension-element width and thickness), an integer strut count per unit\n127\u2192cell, and two categorical variables (connectivity topology, unit-cell\n128\u2192tiling pattern). Categorical choices are encoded explicitly rather\n129\u2192than embedded in a continuous metric, and bounds on the continuous\n130\u2192variables are chosen to remain within the printer's resolution and\n131\u2192overhang limits and to keep specimens in a common form factor for the\n132\u2192test fixtures.\n133\u2192\n134\u2192\\subsection{Surrogate and Acquisition}\n135\u2192The GP surrogate is trained on three measured performance metrics:\n136\u2192peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137\u2192(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138\u2192objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139\u2192likelihood with priors to avoid pathological length scales at small $n$.\n140\u2192Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141\u2192interfacial defects motivates our use of q-noisy expected hypervolume\n142\u2192improvement (qNEHVI), which explicitly accounts for observation noise\n143\u2192in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144\u2192expected-improvement variants. Print failures are treated as a\n145\u2192probabilistic feasibility constraint: the acquisition score for each\n146\u2192candidate is its qNEHVI improvement weighted by the GP-estimated\n147\u2192probability of successful fabrication, following recent constrained\n148\u2192multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149\u2192Where applicable, we use numerically stable log-space variants of\n150\u2192improvement-based acquisitions to mitigate vanishing-gradient\n151\u2192pathologies as data and constraints\n152\u2192accumulate~\\citep{ament2023logei}.\n153\u2192\n154\u2192\\section{Methods}\n155\u2192\\subsection{Fabrication}\n156\u2192Specimens are fabricated on a multi-material FDM system capable of\n157\u2192co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158\u2192Print parameters (temperature, line width, infill, retraction) are\n159\u2192held fixed within a batch to isolate the effect of the design\n160\u2192variables. Each specimen is weighed and dimensionally inspected prior\n161\u2192to testing so that mass and geometric deviations are available as\n162\u2192covariates.\n163\u2192\n164\u2192\\subsection{Mechanical Testing}\n165\u2192Quasi-static compression is performed on a screw-driven load frame at\n166\u2192a fixed nominal strain rate, with force--displacement recorded to\n167\u2192densification. Drop-weight impact testing uses an instrumented tup at\n168\u2192a fixed impact energy; transmitted force is captured at high sample\n169\u2192rate, and specimens are imaged before and after impact to document\n170\u2192failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171\u2192unit mass, and compaction efficiency $\\eta$, defined consistently\n172\u2192across the campaign. A control specimen from an earlier batch is\n173\u2192retested periodically to monitor TPU batch-to-batch and printer drift.\n174\u2192\n175\u2192\\section{Expected Outcomes and Discussion}\n176\u2192We will report how BO-selected designs evolve across iterations, how\n177\u2192GP surrogate predictions and uncertainties change as data accumulate,\n178\u2192and which Pareto-efficient trade-offs emerge between SEA and peak\n179\u2192transmitted force within the explored design space. We will also\n180\u2192discuss practical lessons for operating the closed-loop workflow,\n181\u2192including handling of print failures, batch-to-batch variability in\n182\u2192TPU response, the influence of the rigid--flexible interface on\n183\u2192energy absorption, and the exploration--exploitation balance of the\n184\u2192acquisition function under realistic experimental noise. We expect\n185\u2192the campaign to clarify where physical experimentation is genuinely\n186\u2192required versus where simpler analytical or multi-fidelity surrogate\n187\u2192shortcuts~\\citep{mo2023accelerated} could be\n188\u2192substituted in future work.\n189\u2192\n190\u2192\\section{Conclusion}\n191\u2192This work demonstrates a partially automated, experiment-driven\n192\u2192closed-loop pipeline for designing multi-material 3D-printed\n193\u2192tensegrity-inspired energy absorbers, with surrogate updating and\n194\u2192candidate selection automated and fabrication and testing kept manual.\n195\u2192By updating a GP surrogate directly from physical measurements, the\n196\u2192workflow avoids direct dependence on calibrated finite-element\n197\u2192simulation for objective evaluation while still providing principled,\n198\u2192multi-objective design recommendations. The same closed-loop pattern is\n199\u2192expected to transfer to other multi-material additively manufactured\n200\u2192architectures whose performance is dominated by hard-to-simulate\n201\u2192effects.\n202\u2192\n203\u2192{\\tiny\n204\u2192\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205\u2192\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206\u2192% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207\u2192% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208\u2192\\providecommand{\\doi}[1]{}\n209\u2192\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210\u2192\\bibliography{references}\n211\u2192}\n212\u2192\n213\u2192\\end{document}", + "name": "read", + "tool_call_id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "info": { + "start_ts": 8147.332633466, + "end_ts": 8147.339043095 + } + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\", \"offset\": 174, \"limit\": 100}", + "name": "read" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ], + "info": null + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Tensegrity Slides \u2014 Draft 1 (extracted content)\n 2\u2192\n 3\u2192Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4\u2192\n 5\u2192\n 6\u2192## Slide 1 \u2014 layout: Title Only\n 7\u2192\n 8\u2192**On-slide text:**\n 9\u2192- **Title 1**: Let\u2019s build better tensegrity structures faster\n 10\u2192- **TextBox 4**: Marcus Madsen\n 11\u2192Undergraduate researcher under Sterling Baird, Ph.D.\n 12\u2192- **TextBox 6**: IDETC 2026\n 13\u2192\n 14\u2192**Non-text shapes / media / placeholders:**\n 15\u2192- [PICTURE (13)] Picture 3\n 16\u2192- [PICTURE (13)] Picture 8\n 17\u2192\n 18\u2192**Presenter notes:**\n 19\u2192> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20\u2192> \n 21\u2192> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22\u2192\n 23\u2192\n 24\u2192## Slide 2 \u2014 layout: Title Only\n 25\u2192\n 26\u2192**On-slide text:**\n 27\u2192- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings\u2014and to problems closer to home.\n 28\u2192\n 29\u2192**Non-text shapes / media / placeholders:**\n 30\u2192- [MEDIA (16)] Online Media 5\n 31\u2192\n 32\u2192**Presenter notes:**\n 33\u2192> Replace this video with a downloaded and trimmed version of just the fall. \n 34\u2192> \n 35\u2192> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle \u2014 thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander\u00a0is\u00a0the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit\u00a0and get back up\u00a0\u2014 it survives multiple drops and keeps working.\n 36\u2192\n 37\u2192\n 38\u2192## Slide 3 (HIDDEN) \u2014 layout: Title Only\n 39\u2192\n 40\u2192**On-slide text:**\n 41\u2192- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42\u2192- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43\u2192- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44\u2192\n 45\u2192**Presenter notes:**\n 46\u2192> This might be better explained purely vocally while on the previous slide.\n 47\u2192\n 48\u2192\n 49\u2192## Slide 4 \u2014 layout: Title Only\n 50\u2192\n 51\u2192**On-slide text:**\n 52\u2192- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53\u2192- **TextBox 3**: Iteration\n 54\u2192- **TextBox 5**: Building\n 55\u2192- **TextBox 7**: Testing\n 56\u2192\n 57\u2192**Non-text shapes / media / placeholders:**\n 58\u2192- [MEDIA (16)] AdobeStock_207717222\n 59\u2192- [MEDIA (16)] Online Media 2\n 60\u2192- [PICTURE (13)] Picture 14\n 61\u2192- [PICTURE (13)] Picture 16\n 62\u2192- [PICTURE (13)] Picture 18\n 63\u2192\n 64\u2192**Presenter notes:**\n 65\u2192> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn\u2019t become distracting because it\u2019s fun to watch it spin.\n 66\u2192> \n 67\u2192> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68\u2192\n 69\u2192\n 70\u2192## Slide 5 (HIDDEN) \u2014 layout: Title Only\n 71\u2192\n 72\u2192**On-slide text:**\n 73\u2192- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74\u2192- **TextBox 14**: Baird et al. Matter (2022)\n 75\u2192\n 76\u2192**Non-text shapes / media / placeholders:**\n 77\u2192- [GROUP (6)] Group 2\n 78\u2192\n 79\u2192**Presenter notes:**\n 80\u2192> Consult Sterling on this slide. I don\u2019t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it\u2019s well enough known that it\u2019d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81\u2192\n 82\u2192\n 83\u2192## Slide 6 (HIDDEN) \u2014 layout: Title Only\n 84\u2192\n 85\u2192**On-slide text:**\n 86\u2192- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87\u2192- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88\u2192\n 89\u2192**Presenter notes:**\n 90\u2192> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91\u2192\n 92\u2192\n 93\u2192## Slide 7 \u2014 layout: Title Only\n 94\u2192\n 95\u2192**On-slide text:**\n 96\u2192- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97\u2192- **TextBox 5**: 6 variables\n 98\u2192- **TextBox 7**: 2 types of input data\n 99\u2192- **TextBox 9**: 2 objectives\n100\u2192- **TextBox 17**: *image of blackbox*\n101\u2192\n102\u2192**Non-text shapes / media / placeholders:**\n103\u2192- [LINE (9)] Connector: Curved 11\n104\u2192- [LINE (9)] Connector: Curved 13\n105\u2192- [LINE (9)] Connector: Curved 15\n106\u2192\n107\u2192**Presenter notes:**\n108\u2192> Very rough slide as I\u2019m not entirely sure what graphic or text to put on this, partially because I\u2019m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109\u2192\n110\u2192\n111\u2192## Slide 8 \u2014 layout: Title Only\n112\u2192\n113\u2192**On-slide text:**\n114\u2192- **Title 1**: Multi-material additive manufacturing supplies that data fast \u2014 single-build co-fabrication, with no joining of parts.\n115\u2192\n116\u2192**Non-text shapes / media / placeholders:**\n117\u2192- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118\u2192- [PICTURE (13)] Picture 4\n119\u2192\n120\u2192**Presenter notes:**\n121\u2192> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122\u2192\n123\u2192\n124\u2192## Slide 9 \u2014 layout: Title Only\n125\u2192\n126\u2192**On-slide text:**\n127\u2192- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128\u2192- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129\u2192- **TextBox 5**: *placeholder for slo motion of drop test*\n130\u2192\n131\u2192\n132\u2192## Slide 10 \u2014 layout: Title Only\n133\u2192\n134\u2192**On-slide text:**\n135\u2192- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136\u2192- **TextBox 3**: Printing\n137\u2192- **TextBox 5**: Testing\n138\u2192- **TextBox 7**: BO feed\n139\u2192\n140\u2192**Non-text shapes / media / placeholders:**\n141\u2192- [PICTURE (13)] Picture 14\n142\u2192- [PICTURE (13)] Picture 16\n143\u2192- [PICTURE (13)] Picture 18\n144\u2192- [PICTURE (13)] Graphic 8\n145\u2192\n146\u2192**Presenter notes:**\n147\u2192> I think it might be better if we replaced \u201cprinting\u201d with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148\u2192\n149\u2192\n150\u2192## Slide 11 (HIDDEN) \u2014 layout: Title Only\n151\u2192\n152\u2192**On-slide text:**\n153\u2192- **Title 1**: Reserved for results\n154\u2192\n155\u2192**Presenter notes:**\n156\u2192> Would be helpful if we could show that this process works in this case.\n157\u2192\n158\u2192\n159\u2192## Slide 12 \u2014 layout: Title Only\n160\u2192\n161\u2192**On-slide text:**\n162\u2192- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163\u2192- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164\u2192- **TextBox 3**: *image to show lattice structure\n165\u2192\n166\u2192\n167\u2192## Slide 13 \u2014 layout: Title Only\n168\u2192\n169\u2192**Non-text shapes / media / placeholders:**\n170\u2192- [PICTURE (13)] Picture 3\n171\u2192\n172\u2192**Presenter notes:**\n173\u2192> Slide for questions at the end\n174\u2192\n175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "info": { + "start_ts": 8147.330900226, + "end_ts": 8147.337548875 + } + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Presentation \u2014 Doumont Presentation Structure Template\n 2\u2192\n 3\u2192Transcribed from Jean-luc Doumont's presentation-structure template\n 4\u2192([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5\u2192adapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\n 6\u2192All rights reserved. Can be downloaded from .\n 7\u2192\n 8\u2192> To design your presentation, write down your ideas for each component below.\n 9\u2192> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10\u2192\n 11\u2192**Draft 3** \u2014 applies the inline review comments from @sgbaird on PR #84 and the\n 12\u2192priority revisions from the\n 13\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14\u2192Summaries of what changed are at the bottom of this file\n 15\u2192([Draft 2 \u2192 3](#changes-from-draft-2), [Draft 1 \u2192 2](#changes-from-draft-1)).\n 16\u2192\n 17\u2192**Scope note (frame everything below with this):** the study presented is the\n 18\u2192optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19\u2192processing parameters** (e.g., nozzle temperature, print speed). PLA\u2013TPU is a\n 20\u2192**proxy system** \u2014 not flight material \u2014 used to prototype a self-driving-lab\n 21\u2192style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22\u2192The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23\u2192beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24\u2192\n 25\u2192## Opening\n 26\u2192\n 27\u2192### Attention getter\n 28\u2192\n 29\u2192*A way to lead the audience to the need efficiently*\n 30\u2192\n 31\u2192Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32\u2192landings \u2014 and to problems closer to home.\n 33\u2192\n 34\u2192One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35\u2192clip) do the work:\n 36\u2192\n 37\u2192> Landing a payload on another planet means surviving an impact in conditions\n 38\u2192> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n 39\u2192> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40\u2192> structure: the lander *is* the energy absorber. And unlike crushable\n 41\u2192> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42\u2192> hit **and get back up** \u2014 it survives multiple drops and keeps working.\n 43\u2192\n 44\u2192Candidate Super Ball Bot videos to show (verified links):\n 45\u2192\n 46\u2192- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n 47\u2192 (official channel); the canonical overview.\n 48\u2192- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49\u2192 \u2014 Wall Street Journal; prototype drop footage.\n 50\u2192- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51\u2192 \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n 52\u2192\n 53\u2192Immediately after the hook, state the present study scope in one sentence\n 54\u2192(per the scope note above): *we study a printed PLA\u2013TPU T3 prism as a proxy\n 55\u2192system for prototyping an autonomous tensegrity-optimization workflow* \u2014 so\n 56\u2192the planetary framing never outruns the demonstrated scope.\n 57\u2192\n 58\u2192\n 60\u2192\n 61\u2192### Need\n 62\u2192\n 63\u2192*A difference between actual and desired situations*\n 64\u2192\n 65\u2192**Actual situation** (a two-sided pincer \u2014 stated as a scoped engineering\n 66\u2192judgment, not an absolute):\n 67\u2192\n 68\u2192- For these structures, obtaining trustworthy objective values from a\n 69\u2192 validated high-fidelity model would require substantial calibration of\n 70\u2192 interface, rate, contact, and defect behavior \u2014 and even heavy-duty\n 71\u2192 simulation falls short of the experiments we are running: peak transmitted\n 72\u2192 shock is a millisecond-scale transient that is notoriously difficult to\n 73\u2192 resolve numerically. This holds even for idealized cable\u2013strut tensegrities\n 74\u2192 in their actual engineering materials (steel or similar), not just for\n 75\u2192 printed PLA\u2013TPU.\n 76\u2192 \n 79\u2192- Exhaustive physical search fails on cost: a traditional design of\n 80\u2192 experiments over this relatively high-dimensional space (geometry + FDM\n 81\u2192 process parameters) would demand hundreds of specimens, each costing print\n 82\u2192 and test time.\n 83\u2192- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84\u2192 more complex structures \u2014 especially tensegrity *lattices* \u2014 become\n 85\u2192 extremely difficult and ultimately infeasible to hand-build.\n 86\u2192\n 87\u2192**Desired situation:**\n 88\u2192\n 89\u2192- Trustworthy performance data without hand assembly (easy creation of\n 90\u2192 tensegrity structures, scalable beyond a single prism).\n 91\u2192- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n 92\u2192 not the hundreds a traditional design of experiments would require.\n 93\u2192\n 94\u2192### Task\n 95\u2192\n 96\u2192*What I decided/was asked to do to address the need*\n 97\u2192\n 98\u2192Build a closed-loop design\u2013print\u2013test workflow that optimizes a **T3-prism\n 99\u2192tensegrity-inspired energy absorber together with its FDM processing\n100\u2192parameters** (e.g., temperature, print speed) directly from real, measured\n101\u2192impact data \u2014 maximizing energy absorbed per gram (specific energy\n102\u2192absorption) and compaction efficiency, subject to a cap on the peak force\n103\u2192transmitted to the payload. Extensions beyond the T3 prism are future work.\n104\u2192\n105\u2192### Main message\n106\u2192\n107\u2192*The one sentence I want my audience to remember*\n108\u2192\n109\u2192By closing the loop between multi-material 3D printing and Bayesian\n110\u2192optimization, we can optimize tensegrity-inspired energy absorbers directly\n111\u2192from real impact data \u2014 in dozens of prints, not the hundreds a traditional\n112\u2192design of experiments would demand.\n113\u2192\n114\u2192\n118\u2192\n119\u2192### Preview\n120\u2192\n121\u2192*A map of the body (ideally three points, max. five)*\n122\u2192\n123\u21921. When model calibration and exhaustive testing are both costly, each\n124\u2192 physical specimen must be chosen for its information value \u2014 so the\n125\u2192 measured experiment is our source of truth.\n126\u21922. Bayesian optimization makes physical experimentation affordable: it handles\n127\u2192 noisy measurements, small batches, and the constrained multi-objective\n128\u2192 trade-off \u2014 and Honegumi let us scaffold it in minutes.\n129\u21923. Multi-material additive manufacturing closes the loop: single-build PLA\u2013TPU\n130\u2192 co-fabrication turns each proposed design into test data in hours, with no\n131\u2192 joining of parts.\n132\u21924. *(Optional fourth point, if time allows)* The same closed loop points at\n133\u2192 what comes next: tensegrity lattices, flight-relevant materials, and\n134\u2192 safety hardware on Earth.\n135\u2192\n136\u2192## Body\n137\u2192\n138\u2192### Point 1\n139\u2192\n140\u2192When model calibration and exhaustive testing are both costly, each physical\n141\u2192specimen must be chosen for its information value.\n142\u2192\n143\u2192- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144\u2192 these structures, a sufficiently calibrated multiphysics model is expensive\n145\u2192 to build relative to the design campaign itself \u2014 and the hardest quantity,\n146\u2192 peak transmitted shock, is a millisecond-scale transient that even\n147\u2192 heavy-duty simulation struggles to resolve. The gap persists in the actual\n148\u2192 engineering materials a real lander would use (steel struts and cables),\n149\u2192 so this is not merely an artifact of printed PLA\u2013TPU.\n150\u2192- The alternative \u2014 sweeping the design space physically \u2014 fails on cost: a\n151\u2192 traditional design of experiments over geometry plus FDM process parameters\n152\u2192 would take hundreds of specimens, each needing print + test time.\n153\u2192- Therefore: treat the physical experiment as the authoritative source of\n154\u2192 objective values, and be ruthless about *which* experiments to run.\n155\u2192- Anticipate the FEA-veteran pushback (\"inconvenient \u2260 untrustworthy\"): frame\n156\u2192 it as a cost-and-dependence decision, and keep a backup slide with the\n157\u2192 literature (or our own validation) on simulation\u2013experiment discrepancy for\n158\u2192 impact transients.\n159\u2192\n160\u2192### Transition\n161\u2192\n162\u2192If every data point must be a real experiment, we need a method that extracts\n163\u2192the most from every specimen \u2014 we need to make the process radically more\n164\u2192sample-efficient.\n165\u2192\n166\u2192### Point 2\n167\u2192\n168\u2192Bayesian optimization makes physical experimentation affordable, even with\n169\u2192limited, noisy data.\n170\u2192\n171\u2192- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172\u2192 visual in the main talk; implementation detail goes to backup slides.\n173\u2192 Spoken version:\n174\u2192 > \"After each batch, the model estimates both expected performance and\n175\u2192 > uncertainty across the design space. The acquisition rule selects a small\n176\u2192 > next batch that is most likely to expand the feasible trade-off frontier,\n177\u2192 > while accounting for noisy tests and print failures.\"\n178\u2192- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179\u2192 motivate a noise-aware acquisition function (qNEHVI) rather than\n180\u2192 non-noise-aware alternatives. (Backup slide: how observation noise is\n181\u2192 actually modeled \u2014 replicates, noise identification \u2014 and budget-matched\n182\u2192 baselines such as Sobol/random sampling.)\n183\u2192- Small parallel batches: the surrogate proposes the next batch of specimens\n184\u2192 to print, balancing exploration and exploitation.\n185\u2192- Constrained multi-objective by construction: **objectives** = maximize\n186\u2192 specific energy absorption and compaction efficiency; **constraint** = peak\n187\u2192 transmitted force must stay under a cap; the deliverable is the *feasible*\n188\u2192 Pareto front between the two objectives among force-cap-satisfying designs.\n189\u2192 (Keep this formulation consistent everywhere \u2014 do not present \"SEA vs. peak\n190\u2192 force\" as the trade-off.)\n191\u2192- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192\u2192 this advanced task (noisy, batched, constrained, multi-objective) in\n193\u2192 minutes using [Honegumi](https://honegumi.readthedocs.io/) \u2014 the audience\n194\u2192 can do the same for their own problems; show the code-template picker\n195\u2192 briefly so they see how low the barrier is.\n196\u2192\n197\u2192### Transition\n198\u2192\n199\u2192The optimizer is no longer the bottleneck \u2014 the limit is now providing real\n200\u2192data quickly enough.\n201\u2192\n202\u2192### Point 3\n203\u2192\n204\u2192Multi-material additive manufacturing supplies that data fast \u2014 single-build\n205\u2192co-fabrication, with no joining of parts.\n206\u2192\n207\u2192- Single-build PLA\u2013TPU co-fabrication: rigid struts and flexible tension\n208\u2192 network in one print \u2014 no joining or hand assembly of members, which closes\n209\u2192 the \"easy creation\" gap from the Need and is what makes future lattices\n210\u2192 feasible at all. (Be precise: if support removal, trimming, or conditioning\n211\u2192 remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212\u2192 assembly.\")\n213\u2192- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214\u2192 as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215\u2192 pre-tension status) plus a short deformation sequence \u2014 so the EDL engineer\n216\u2192 doesn't file it as a \"crush structure\" and the grad student can tell it from\n217\u2192 an ordinary lattice.\n218\u2192- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219\u2192 tested specimen (quasi-static compression + instrumented drop-weight impact)\n220\u2192 in hours. (Backup slide: representative print-to-result time breakdown and\n221\u2192 which steps remain manual \u2014 candidate selection is automated; slicing,\n222\u2192 handling, and testing are not yet.)\n223\u2192- Now we have the data we need to complete the loop.\n224\u2192\n225\u2192### Point 4 (optional \u2014 future applications)\n226\u2192\n227\u2192*Include if timing allows; otherwise fold into the Conclusion.*\n228\u2192\n229\u2192The same closed loop transfers wherever tests are authoritative, each test is\n230\u2192costly, the design space is mixed, and several performance goals conflict:\n231\u2192\n232\u2192- Tensegrity *lattices* and more complex architectures \u2014 exactly where hand\n233\u2192 assembly becomes infeasible and single-build co-fabrication pays off.\n234\u2192- Migration from the PLA\u2013TPU proxy toward flight-relevant materials and\n235\u2192 impact regimes.\n236\u2192- Safety hardware on Earth (e.g., the tensegrity crutch tip \u2014 future work).\n237\u2192- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238\u2192 handling, testing) in the loop.\n239\u2192\n240\u2192### Evidence / results\n241\u2192\n242\u2192*What we found \u2014 ground the conclusion in shown data*\n243\u2192\n244\u2192Build the talk around a quantitative three-slide sequence (per the\n245\u2192mock-audience report; insert observed values once the campaign completes):\n246\u2192\n247\u21921. **Campaign ledger** \u2014 \"The campaign used N specimens across B adaptive\n248\u2192 batches under a fixed physical-test budget\": initialization vs. adaptive\n249\u2192 batches, replicates, print failures, cycle time, and the numerical value\n250\u2192 (and rationale) of the force cap.\n251\u21922. **Budget-matched baseline** \u2014 \"Adaptive selection improved the feasible\n252\u2192 design set faster than [predeclared baseline]\": performance vs. number of\n253\u2192 physical tests against Sobol/random sampling at the same budget, with\n254\u2192 uncertainty where available.\n255\u21923. **Measured feasible Pareto front** \u2014 specific energy absorption vs.\n256\u2192 compaction efficiency among designs satisfying the force cap, with\n257\u2192 replicate spread, marked infeasible designs, and specimen photos or\n258\u2192 force\u2013time traces for representative Pareto designs.\n259\u2192\n260\u2192Supporting raw-data candidates: drop-test force\u2013time traces, Sobol /\n261\u2192first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262\u2192\n263\u2192### Suggested 15-minute allocation\n264\u2192\n265\u2192*(From the mock-audience report \u2014 results get the largest block.)*\n266\u2192\n267\u2192| Time | Content |\n268\u2192|---|---|\n269\u2192| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270\u2192| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271\u2192| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272\u2192| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273\u2192| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274\u2192| 1 min | Limitations and transfer conditions |\n275\u2192| 1 min | Measured conclusion + bookend |\n276\u2192\n277\u2192Move kernel choices, encodings, and other implementation detail to backup\n278\u2192slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279\u2192[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280\u2192\n281\u2192## Closing\n282\u2192\n283\u2192### Review\n284\u2192\n285\u2192*A recap of the body, leading into the conclusion*\n286\u2192\n287\u2192Using real, measured impact data from additively manufactured\n288\u2192tensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\n289\u2192optimization routine \u2014 we rapidly iterate toward better energy absorbers.\n290\u2192This feedback loop makes tensegrity optimization dramatically faster: dozens\n291\u2192of prints instead of the hundreds a traditional design of experiments over\n292\u2192this design space would require.\n293\u2192\n294\u2192### Conclusion\n295\u2192\n296\u2192*What the above means to the audience in the end*\n297\u2192\n298\u2192Because the loop runs on physical measurements, it is useful precisely where\n299\u2192model calibration is not worth its cost \u2014 and the same closed-loop pattern\n300\u2192should transfer (a conditional claim, pending evidence) to other additively\n301\u2192manufactured architectures whose performance is dominated by\n302\u2192hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303\u2192*use this loop when tests are authoritative, each test is costly, the design\n304\u2192space is mixed, and several performance goals conflict.* For tensegrity\n305\u2192specifically, it opens a path from this T3 proxy study toward lattices,\n306\u2192flight-relevant materials, payload protection on other planets, and safety\n307\u2192hardware here on Earth (e.g., the crutch tip, as future work).\n308\u2192\n309\u2192### Close\n310\u2192\n311\u2192*A way to end the presentation clearly and elegantly*\n312\u2192\n313\u2192Return to the opening image: the next planetary lander's energy absorber \u2014 a\n314\u2192structure that has to survive not one impact but many \u2014 doesn't have to take\n315\u2192years of hand-built prototypes; with this loop, candidate designs can be\n316\u2192designed, printed, and tested against real impacts in weeks instead of months\n317\u2192or longer. End on the measured engineering result and its transfer condition,\n318\u2192then the bookend: every method that compresses design time moves our ventures\n319\u2192\u2014 on this planet and beyond \u2014 forward.\n320\u2192\n321\u2192---\n322\u2192\n323\u2192## Changes from Draft 2\n324\u2192\n325\u2192Applied from @sgbaird's inline review comments on PR #84 and the\n326\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327\u2192\n328\u2192**From the inline review comments:**\n329\u2192\n330\u21921. **Reusability in the hook** (line 22): the tensegrity lander survives\n331\u2192 multiple drops, unlike crushable/sacrificial alternatives.\n332\u21922. **Proxy-system framing** (lines 49, 87): PLA\u2013TPU would never fly; it is a\n333\u2192 proxy for prototyping a self-driving-lab-style workflow \u2014 stated in a\n334\u2192 scope note up front and re-stated right after the hook.\n335\u21923. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336\u2192 heavy-duty simulation falls short of the experiments (millisecond-scale\n337\u2192 peak shock), not that it merely costs too much.\n338\u21924. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339\u2192 complex structures and especially lattices are not.\n340\u21925. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341\u2192 (temperature, print speed, \u2026) only; the rest is explicitly future work.\n342\u21926. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343\u2192 literature search (or validation of experiments against simulation), plus\n344\u2192 the millisecond-resolution peak-shock argument.\n345\u21927. **Optional Point 4 added** (line 97): future applications, with a note it\n346\u2192 can fold into the Conclusion if time is short.\n347\u21928. **Steel-materials point** (line 104): simulations fall short for the\n348\u2192 actual engineering materials (steel struts/cables), not just printed\n349\u2192 PLA\u2013TPU.\n350\u21929. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351\u2192 in minutes, pitched so the audience sees they can use it too.\n352\u219210. **\"Thousands\" \u2192 \"hundreds\"** (line 173): baseline is now a traditional\n353\u2192 design of experiments over a high-dimensional space (hundreds), in the\n354\u2192 main message, Need, Point 1, and Review.\n355\u219211. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356\u2192 or longer\" for candidate designs.\n357\u2192\n358\u2192**From the mock-audience report's priority revisions:**\n359\u2192\n360\u219212. **Quantitative three-slide results sequence** (Priority 1): campaign\n361\u2192 ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front, with a\n362\u2192 fallback main-message wording if the campaign can't yet support the\n363\u2192 efficiency claim.\n364\u219213. **Anti-simulation premise replaced with a scoped cost decision**\n365\u2192 (Priority 2): Point 1 retitled to the information-value message;\n366\u2192 \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367\u2192 anticipated with a backup slide.\n368\u219214. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369\u2192 implementation detail moved to backup slides; Q&A prep pointed at the\n370\u2192 report's claims-vs-evidence table.\n371\u219215. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372\u2192 the main talk, machinery in backup slides; the misleading\n373\u2192 \"qNEHVI over standard expected improvement\" comparison replaced with\n374\u2192 \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375\u219216. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376\u2192 sequence in Point 3, so the structure isn't remembered as a generic crush\n377\u2192 structure or lattice.\n378\u219217. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379\u2192 co-fabrication / no joining of parts,\" with a note to list remaining\n380\u2192 manual steps and an automation-status caveat.\n381\u219218. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382\u2192 compaction efficiency, constraint = force cap, deliverable = *feasible*\n383\u2192 Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384\u219219. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385\u2192 transfer claim marked conditional, and the close ends on the measured\n386\u2192 result before the bookend.\n387\u2192\n388\u2192## Changes from Draft 1\n389\u2192\n390\u2192Feedback from the PR #84 review, applied in Draft 2:\n391\u2192\n392\u21921. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n393\u2192 dozens-not-thousands claim); body and message now carry the same story.\n394\u21922. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n395\u2192 manuscript's positioning (#75/#76) is that the physical experiment is the\n396\u2192 source of truth, explicitly avoiding calibrated FE simulation.\n397\u21923. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n398\u2192 parametric design space and avoid an over-claim to a design-automation\n399\u2192 audience.\n400\u21924. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401\u2192 energy absorption and compaction efficiency, cap peak transmitted force.\n402\u21925. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403\u2192 *and* the space is too big to brute-force \u2014 which makes BO + AM the\n404\u2192 inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405\u2192 above.)*\n406\u21926. **Point 1 given its own content** (the *why* behind the inefficiency)\n407\u2192 instead of restating the Need.\n408\u21927. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409\u2192 the specifics (noise-aware acquisition, small batches, multi-objective).\n410\u21928. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411\u2192 (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412\u2192 above.)*\n413\u21929. **Evidence/results slot added** before the Closing, so the Conclusion is\n414\u2192 grounded in shown data rather than three hedged \"may\"s.\n415\u219210. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n416\u2192 Ball Bot visual; verified video links included.\n417\u219211. **Close bookends the opening** (the next lander's absorber, designed in\n418\u2192 weeks) before the broader humankind line.\n419\u219212. **Spelling**: Doumont (was \"Duomont\").", + "name": "read", + "tool_call_id": "call_93haurOMDGiH2mQFFbaMaYKn", + "info": { + "start_ts": 8147.331065886, + "end_ts": 8147.336467845 + } + }, + { + "role": "tool", + "content": " 1\u2192# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n 2\u2192\n 3\u2192Video: (assigned in #83)\n 4\u2192\n 5\u2192Transcribed from @me-madsen's handwritten notes\n 6\u2192([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7\u2192dated 2026-07-15).\n 8\u2192\n 9\u2192## Overview\n10\u2192\n11\u2192A presentation is all about the right content, displayed the right way, at the\n12\u2192right time.\n13\u2192\n14\u2192- Verbal communication is sequential.\n15\u2192- Message is *interpretation* of the information.\n16\u2192 - Ensure the audience can get the most out of what you're doing.\n17\u2192 - Don't just give them information \u2014 help them understand it, interpret it,\n18\u2192 and internalize it.\n19\u2192\n20\u2192## Doumont's suggested rules for a presentation\n21\u2192\n22\u21920. Ensure you have **messages** for your audience, not just words.\n23\u21921. **Adapt to your audience.**\n24\u2192 - Think like the audience, not the speaker.\n25\u21922. **Maximize signal-to-noise ratio.**\n26\u2192 - Reduce noise (animations, laser pointers, anything that pulls attention\n27\u2192 away from your message).\n28\u21923. **Use effective redundancy.**\n29\u2192 - Tell your message in word *and* on slide: someone deaf should understand\n30\u2192 your messages just as well as someone blind.\n31\u2192 - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32\u2192 date).\n33\u2192\n34\u2192## When creating slides\n35\u2192\n36\u2192- If the audience doesn't need it, why put it on the slides?\n37\u2192- People shouldn't have to count the items on a slide to know how many are\n38\u2192 there.\n39\u2192- Don't use your slides as your \"presentation notes.\"\n40\u2192 - Don't show your notes to the audience.\n41\u2192 - **A bad slide is worse than no slide at all.**\n42\u2192- **One message per slide** is a good rule of thumb.\n43\u2192 - Prioritize the *so what* (or *therefore what*) of your message.\n44\u2192- Make it easy to interpret the information on the slide:\n45\u2192 - 3\u20135 items at most.\n46\u2192 - Align the title left.\n47\u2192 - Do not clutter slides with repetitive or unnecessary information: logos,\n48\u2192 affiliation, date, slide numbers.\n49\u2192- Doumont's suggested workflow:\n50\u2192 1. **Start with the title.**\n51\u2192 - Use a full sentence in the title.\n52\u2192 - The title is the main message of the slide.\n53\u2192 - Make it easy to read: left-aligned, with intentional line breaks.\n54\u2192 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55\u2192- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56\u2192\n57\u2192## General preparation\n58\u2192\n59\u2192- Planning for audience, content, and structure matters more than materials\n60\u2192 (i.e., slides).\n61\u2192- Try using one sheet of paper for presentation preparation.\n62\u2192- It's most important that your presentation is:\n63\u2192 - well planned, structured, and delivered;\n64\u2192 - **if you don't have a lot of time to prepare, don't focus on making\n65\u2192 slides.**\n66\u2192- Practice the presentation a few times. Practice at least once *without* your\n67\u2192 slideshow!\n68\u2192- If doing a handout, keep it separate from the slides you're preparing.\n69\u2192- Impress the audience with your body.\n70\u2192 - Use gestures, not laser pointers.\n71\u2192 - \"Control\" the audience with your body \u2014 keep their attention on your\n72\u2192 presentation.\n73\u2192\n74\u2192## Impressions / what to apply to the IDETC presentation\n75\u2192\n76\u2192\n77\u2192\n78\u2192## Questions / things to discuss\n79\u2192\n80\u2192", + "name": "read", + "tool_call_id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "info": { + "start_ts": 8147.331133416, + "end_ts": 8147.338304015 + } + }, + { + "role": "tool", + "content": " 1\u2192\\documentclass[10pt]{article}\n 2\u2192\n 3\u2192% --- Packages ---\n 4\u2192\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5\u2192\\usepackage[hidelinks]{hyperref}\n 6\u2192\\usepackage{parskip}\n 7\u2192\\usepackage{graphicx}\n 8\u2192\\usepackage{caption}\n 9\u2192\\usepackage{titlesec}\n 10\u2192\\usepackage{wrapfig}\n 11\u2192\\usepackage[numbers,sort&compress]{natbib}\n 12\u2192\n 13\u2192% --- Formatting ---\n 14\u2192\\setlength{\\parskip}{0.2em}\n 15\u2192\\setlength{\\parindent}{0em}\n 16\u2192\\titleformat*{\\section}{\\large\\bfseries}\n 17\u2192\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18\u2192\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19\u2192\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20\u2192\\setlength{\\emergencystretch}{2em}\n 21\u2192\\setlength{\\intextsep}{0.2em}\n 22\u2192\\bibliographystyle{abbrvnat}\n 23\u2192\n 24\u2192\\begin{document}\n 25\u2192\n 26\u2192\\begin{center}\n 27\u2192{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28\u21923D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29\u2192\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30\u2192\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31\u2192\\end{center}\n 32\u2192\n 33\u2192\\vspace{0.4em}\n 34\u2192\n 35\u2192\\section*{Abstract}\n 36\u2192Tensegrity-inspired architectures---rigid struts suspended within a\n 37\u2192continuous flexible network---can exhibit tunable nonlinear\n 38\u2192force--displacement responses and favorable energy absorption per unit\n 39\u2192mass, motivating their use in lightweight energy-absorbing and protective\n 40\u2192structures\n 41\u2192\\citep{skelton2009tensegrity,pajunen2019design}.\n 42\u2192Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43\u2192struts and flexible (TPU) elements in a single build\n 44\u2192\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45\u2192space---strut geometry, tension-element cross-section, connectivity\n 46\u2192topology, and unit-cell tiling---is too large to explore by trial and\n 47\u2192error. We present a closed-loop experimental campaign that uses\n 48\u2192Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49\u2192to drive a design--print--test workflow operating directly on physical\n 50\u2192measurements, without relying on calibrated finite-element simulation\n 51\u2192for objective evaluation.\n 52\u2192\n 53\u2192\\section{Introduction and Motivation}\n 54\u2192\n 55\u2192\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56\u2192\\centering\n 57\u2192\\vspace{-0.4em}\n 58\u2192\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59\u2192\\captionsetup{font=small}\n 60\u2192\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61\u2192Candidate tensegrity-inspired unit cells are instantiated from a\n 62\u2192parameter vector, fabricated by multi-material FDM, and tested under\n 63\u2192quasi-static compression and drop-weight impact. Measured peak force,\n 64\u2192specific energy absorption (SEA), and compaction efficiency update a\n 65\u2192Gaussian-process surrogate that proposes the next batch of designs.}\n 66\u2192\\label{fig:overview}\n 67\u2192\\vspace{-0.4em}\n 68\u2192\\end{wrapfigure}\n 69\u2192\n 70\u2192Tensegrity structures combine compression-only members with a\n 71\u2192pre-tensioned tension network to achieve stiffness without rigid\n 72\u2192joinery~\\citep{skelton2009tensegrity}. Their\n 73\u2192nonlinear, often load-limiting force--displacement response makes them\n 74\u2192attractive for impact mitigation, packaging, and wearable protective\n 75\u2192gear~\\citep{pajunen2019design}. Idealized\n 76\u2192cable--strut tensegrities are difficult to manufacture at scale, but\n 77\u2192\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78\u21923D printing reproduce key tensegrity behaviors---including\n 79\u2192post-buckling stability and load-limiting force--displacement\n 80\u2192response~\\citep{pajunen2019design}---motivating our extension to\n 81\u2192multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82\u2192elements. PLA provides rigid struts; TPU provides flexible,\n 83\u2192rate-dependent tension elements whose viscoelasticity is expected to\n 84\u2192contribute hysteretic energy dissipation under impact loading,\n 85\u2192consistent with the energy-absorbing behavior reported for rigid--TPU\n 86\u2192multi-material prints~\\citep{khatri2024energy}.\n 87\u2192\n 88\u2192The challenge is that small geometric and topological changes can\n 89\u2192produce large changes in the measured response, and high-fidelity\n 90\u2192simulation of FDM specimens is computationally expensive and often\n 91\u2192struggles to capture physical defects such as interfacial slip and\n 92\u2192TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93\u2192trial-and-error sweeps over the joint design space are also infeasible:\n 94\u2192even modest parameter resolutions yield thousands of candidates, each\n 95\u2192requiring print and test time. We instead treat the physical\n 96\u2192experiment as the source of truth and use BO to select which specimens\n 97\u2192are most informative to fabricate next---an approach used in related\n 98\u2192closed-loop materials and structures\n 99\u2192campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100\u2192Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101\u2192set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102\u2192designs using parallel, noisy multi-objective BO driven by measured\n103\u2192$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104\u2192\n105\u2192\\section{Approach}\n106\u2192\\subsection{Closed-Loop Workflow}\n107\u2192Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108\u2192proceeds in four steps: (i)~a parameterized unit-cell design is\n109\u2192instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110\u2192is sliced and printed on a multi-material FDM system; (iii)~it is\n111\u2192tested under quasi-static compression and instrumented drop-weight\n112\u2192impact; and (iv)~the resulting performance metrics update a\n113\u2192Gaussian-process (GP) surrogate, which a batch acquisition function\n114\u2192queries to recommend the next set of candidates. Surrogate updating\n115\u2192and experiment selection are automated; specimen handling, slicing,\n116\u2192and test setup remain manual.\n117\u2192\n118\u2192\\subsection{Design Space}\n119\u2192We parameterize a family of unit cells using a core-wrapping\n120\u2192architecture in which rigid PLA struts are enclosed by continuous TPU\n121\u2192skins; the wrapping rationale follows multi-material FDM strategies in\n122\u2192which rigid panels are wrapped by stretchable soft material to prevent\n123\u2192delamination and improve interfacial bonding at the rigid--flexible\n124\u2192interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125\u2192comprises continuous geometric variables (strut diameter and length,\n126\u2192tension-element width and thickness), an integer strut count per unit\n127\u2192cell, and two categorical variables (connectivity topology, unit-cell\n128\u2192tiling pattern). Categorical choices are encoded explicitly rather\n129\u2192than embedded in a continuous metric, and bounds on the continuous\n130\u2192variables are chosen to remain within the printer's resolution and\n131\u2192overhang limits and to keep specimens in a common form factor for the\n132\u2192test fixtures.\n133\u2192\n134\u2192\\subsection{Surrogate and Acquisition}\n135\u2192The GP surrogate is trained on three measured performance metrics:\n136\u2192peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137\u2192(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138\u2192objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139\u2192likelihood with priors to avoid pathological length scales at small $n$.\n140\u2192Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141\u2192interfacial defects motivates our use of q-noisy expected hypervolume\n142\u2192improvement (qNEHVI), which explicitly accounts for observation noise\n143\u2192in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144\u2192expected-improvement variants. Print failures are treated as a\n145\u2192probabilistic feasibility constraint: the acquisition score for each\n146\u2192candidate is its qNEHVI improvement weighted by the GP-estimated\n147\u2192probability of successful fabrication, following recent constrained\n148\u2192multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149\u2192Where applicable, we use numerically stable log-space variants of\n150\u2192improvement-based acquisitions to mitigate vanishing-gradient\n151\u2192pathologies as data and constraints\n152\u2192accumulate~\\citep{ament2023logei}.\n153\u2192\n154\u2192\\section{Methods}\n155\u2192\\subsection{Fabrication}\n156\u2192Specimens are fabricated on a multi-material FDM system capable of\n157\u2192co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158\u2192Print parameters (temperature, line width, infill, retraction) are\n159\u2192held fixed within a batch to isolate the effect of the design\n160\u2192variables. Each specimen is weighed and dimensionally inspected prior\n161\u2192to testing so that mass and geometric deviations are available as\n162\u2192covariates.\n163\u2192\n164\u2192\\subsection{Mechanical Testing}\n165\u2192Quasi-static compression is performed on a screw-driven load frame at\n166\u2192a fixed nominal strain rate, with force--displacement recorded to\n167\u2192densification. Drop-weight impact testing uses an instrumented tup at\n168\u2192a fixed impact energy; transmitted force is captured at high sample\n169\u2192rate, and specimens are imaged before and after impact to document\n170\u2192failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171\u2192unit mass, and compaction efficiency $\\eta$, defined consistently\n172\u2192across the campaign. A control specimen from an earlier batch is\n173\u2192retested periodically to monitor TPU batch-to-batch and printer drift.\n174\u2192\n175\u2192\\section{Expected Outcomes and Discussion}\n176\u2192We will report how BO-selected designs evolve across iterations, how\n177\u2192GP surrogate predictions and uncertainties change as data accumulate,\n178\u2192and which Pareto-efficient trade-offs emerge between SEA and peak\n179\u2192transmitted force within the explored design space. We will also\n180\u2192discuss practical lessons for operating the closed-loop workflow,\n181\u2192including handling of print failures, batch-to-batch variability in\n182\u2192TPU response, the influence of the rigid--flexible interface on\n183\u2192energy absorption, and the exploration--exploitation balance of the\n184\u2192acquisition function under realistic experimental noise. We expect\n185\u2192the campaign to clarify where physical experimentation is genuinely\n186\u2192required versus where simpler analytical or multi-fidelity surrogate\n187\u2192shortcuts~\\citep{mo2023accelerated} could be\n188\u2192substituted in future work.\n189\u2192\n190\u2192\\section{Conclusion}\n191\u2192This work demonstrates a partially automated, experiment-driven\n192\u2192closed-loop pipeline for designing multi-material 3D-printed\n193\u2192tensegrity-inspired energy absorbers, with surrogate updating and\n194\u2192candidate selection automated and fabrication and testing kept manual.\n195\u2192By updating a GP surrogate directly from physical measurements, the\n196\u2192workflow avoids direct dependence on calibrated finite-element\n197\u2192simulation for objective evaluation while still providing principled,\n198\u2192multi-objective design recommendations. The same closed-loop pattern is\n199\u2192expected to transfer to other multi-material additively manufactured\n200\u2192architectures whose performance is dominated by hard-to-simulate\n201\u2192effects.\n202\u2192\n203\u2192{\\tiny\n204\u2192\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205\u2192\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206\u2192% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207\u2192% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208\u2192\\providecommand{\\doi}[1]{}\n209\u2192\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210\u2192\\bibliography{references}\n211\u2192}\n212\u2192\n213\u2192\\end{document}", + "name": "read", + "tool_call_id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "info": { + "start_ts": 8147.332633466, + "end_ts": 8147.339043095 + } + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\", \"offset\": 174, \"limit\": 100}", + "name": "read" + } + } + ], + "info": null + }, + { + "role": "tool", + "content": "175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "info": { + "start_ts": 8153.825818486, + "end_ts": 8153.828437556 + } + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8In1tJq32AD2tTyHX3tmIlLf", + "type": "function", + "function": { + "arguments": "{\"answer\": \"# Mock audience report: IDETC-CIE 2026 slide deck, Draft 1\\n\\n## Executive read\\n\\nThe visible deck has a clear visual spine: **motivation \\u2192 slow iteration \\u2192 Bayesian optimization \\u2192 multi-material printing \\u2192 impact testing \\u2192 closed loop \\u2192 future applications**. The strongest section is slides 8\\u201310, where fabrication, testing, and adaptive selection finally appear as one workflow.\\n\\nBut Draft 1 does not yet deliver the technical case promised by the agreed outline. The visible deck omits four things a DAC-10 audience will need:\\n\\n1. **Honest scope:** no visible slide says that the PLA\\u2013TPU T3 prism is a proxy system rather than flight hardware.\\n2. **A precise optimization problem:** the six variables, two objectives, force constraint, noise treatment, and fabrication failures are not intelligible on slide 7.\\n3. **Quantitative evidence:** the sole results slot is hidden and empty, yet slides 10 and 12 already claim acceleration and transfer.\\n4. **A measured conclusion:** the talk jumps from the workflow to speculative applications without showing a campaign ledger, comparison against a budget-matched baseline, or feasible Pareto front.\\n\\nThe slide titles generally follow Doumont\\u2019s full-sentence-message rule, but several messages outrun the evidence. The deck also departs substantially from the Draft 3 timing plan, which reserves **5 of 15 minutes for results**. As built, results receive zero visible slides.\\n\\n---\\n\\n# P0. The program manager in the audience\\n\\n## (a) My reaction in the room\\n\\nSlide 1 tells me the team wants to work faster, but it does not tell me what is being optimized or what has actually been built. The title sounds aspirational rather than traceable to a project deliverable. I immediately want the formal title from the abstract or a shorter version of it.\\n\\nThe Super Ball Bot footage on slide 2 is an effective hook. I understand why reusable impact attenuation matters. But the slide creates a scope risk: I am looking at planetary landing hardware while the actual work is a printed PLA\\u2013TPU T3 proxy. Because that distinction is not stated immediately, I may spend the rest of the talk evaluating the work against flight-readiness criteria the project never intended to meet.\\n\\nSlide 4 gives me the business problem, but not a baseline. \\u201cSlow and resource intensive\\u201d needs a number: hours per print-test cycle, number of factors, or the candidate count under a declared design of experiments. Without that, I cannot assess whether the proposed loop attacks the dominant schedule driver.\\n\\nSlide 7 is where I expect the project definition. Instead, \\u201c6 variables,\\u201d \\u201c2 types of input data,\\u201d and \\u201c2 objectives\\u201d read like inventory labels. I cannot reconstruct the statement of work from them. I need to see what is automated, what remains manual, what the force cap is, and what constitutes a successful campaign.\\n\\nSlides 8 and 9 make the project tangible. A labeled specimen and an actual drop test would build confidence. Yet \\u201csingle-build\\u201d and \\u201cno joining\\u201d need a process qualification caveat, and I need the measurement chain to be explicit. The abstract refers to an instrumented tup and force\\u2013time data; slide 9 says accelerometers. Those may be compatible, but the deck presently leaves the relationship unclear.\\n\\nSlide 10 is the right management view of the system, but \\u201csignificantly accelerate\\u201d is a result claim placed on a workflow diagram. At this point I ask, \\u201cCompared with what, by how much, and over how many specimens?\\u201d Then the deck skips the hidden results slide and goes directly to future applications. That is the point where my confidence drops sharply. The project is asking me to extrapolate before closing the evidence loop.\\n\\nSlide 12 is too early and too broad. A crutch tip and lattice can be legitimate next phases, but only after the team shows what the present campaign established and what technical risks remain. The blank questions slide gives me no durable take-home message.\\n\\n### PM stage-risk register\\n\\n| Risk | Likelihood | Consequence | Mitigation |\\n|---|---:|---:|---|\\n| Results are incomplete by conference lock | Medium to high | Critical: central claim becomes unsupported | Set a dated campaign freeze; predeclare a fallback message and minimum publishable result package |\\n| Embedded Super Ball Bot or drop-test video fails | Medium | Moderate: weak opening or broken pacing | Use local, trimmed files; retain a still-frame fallback on every video slide |\\n| Talk overruns because several videos and explanations are improvised | High | High: results or conclusion gets cut | Script videos to 10\\u201315 seconds total each; rehearse to 13:30; assign 5 minutes to results |\\n| Scope is mistaken for flight-hardware validation | High | High: credibility damage in Q&A | Put \\u201cPLA\\u2013TPU T3 proxy; not flight hardware\\u201d on slide 2 or the next slide |\\n| Acceleration claim lacks a budget-matched comparator | High | Critical for DAC review audience | Show Sobol/random or another predeclared baseline at the same physical-test budget |\\n| Measurement chain is challenged | Medium | High | Add a methods schematic and backup slide on calibration, sampling, filtering, and metric extraction |\\n| \\u201cNo joining\\u201d or \\u201cnear autonomous\\u201d is read as full automation | Medium | Moderate | List manual steps explicitly: slicing, handling, setup, and testing |\\n| Animations distract or fail to communicate without narration | Medium | Moderate | Prefer one short clip plus static annotated frames; follow Doumont\\u2019s signal-to-noise rule |\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey are building a loop that prints PLA\\u2013TPU tensegrity-like absorbers, drop-tests them, and lets Bayesian optimization choose the next print.\\u201d\\n\\nThat retains the basic workflow but loses the intended **dozens rather than hundreds** claim because the deck never demonstrates it. I would also probably describe the work as \\u201clander research,\\u201d which is a distortion caused by the missing proxy-system statement.\\n\\n## (c) My top three Q&A questions\\n\\n1. What is the dated plan to complete the campaign, baseline comparison, and repeat testing before the conference slide freeze?\\n2. Which exact operations are automated today, which remain manual, and what is the measured end-to-end cycle time per adaptive batch?\\n3. What evidence will justify \\u201cdozens rather than hundreds,\\u201d and what will you claim if the budget-matched baseline does not show an advantage?\\n\\n## (d) My most pointed objection\\n\\n**You are presenting a claimed acceleration without showing a completed campaign, a comparator, or even a visible results slide.**\\n\\nUnanswered, this is **critical**. I would not fund a scale-up phase from this deck. I might fund completion of the present campaign if the team showed a disciplined schedule, predefined success criteria, and a credible fallback.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 11.** It must become a real, scheduled results package, preferably three slides: campaign ledger, budget-matched baseline, and feasible Pareto front.\\n- **Hidden-slide decision:** **Unhide slide 11**, but not in its current empty form. The talk cannot make its principal claim without it.\\n\\n### PM slide-level punch list, ordered by schedule criticality\\n\\n**Must be fixed first**\\n\\n1. **Slide 11/results sequence:** assign owners and dates for data freeze, quality control, baseline computation, replicate analysis, and final plots. Define fallback wording now.\\n2. **Slide 2:** add the explicit scope line, \\u201cThis study uses a printed PLA\\u2013TPU T3 prism as a proxy for developing the workflow; it is not flight hardware.\\u201d\\n3. **Slide 7:** replace category counts with the actual optimization problem, test budget, batch size, objectives, constraint, and noise/failure treatment.\\n4. **Slide 10:** remove \\u201csignificantly\\u201d until slide 11 demonstrates a quantitative improvement; show automation boundaries and the loop\\u2019s measured cycle time.\\n5. **Slides 2 and 9:** download and trim media, test on the conference laptop, and put a static fallback image on each slide.\\n\\n**Then fix**\\n\\n6. **Slide 4:** quantify the current cost or candidate count and identify the bottleneck.\\n7. **Slide 8:** label PLA, TPU, interface, and any post-processing; qualify \\u201cno joining.\\u201d\\n8. **Slide 9:** reconcile accelerometers with the instrumented-tup description and show how force, energy absorption, and compaction efficiency are obtained.\\n9. **Slide 12:** convert the application collage into a conditional next-phase roadmap with gates.\\n10. **Slide 13:** end on the measured result and deployment rule, not a blank Q&A screen.\\n\\n**Nice to have**\\n\\n11. Replace decorative or looping media on slide 4 with one static process image unless it communicates a measured delay.\\n12. Add backup slides on Bayesian optimization implementation, finite-element-analysis positioning, repeatability, force-cap rationale, and print failures.\\n\\n---\\n\\n# P1. The skeptical Bayesian-optimization insider\\n\\n## (a) My reaction in the room\\n\\nThe opening is competent but generic for a design-automation conference. Slides 1\\u20134 set up an expensive physical experiment, which is a valid application class for Bayesian optimization (BO). I become interested when the presenter reaches slide 7, because that is where methodological seriousness should appear.\\n\\nInstead, slide 7 says only \\u201c6 variables,\\u201d \\u201c2 types of input data,\\u201d and \\u201c2 objectives.\\u201d I do not learn the variables, their types, how categorical choices are encoded, whether process parameters are optimized, how noisy observations enter the Gaussian processes, how replicates identify noise, how failures are modeled, or how the force cap enters the acquisition. The title says BO handles noisy data, but the visual does not substantiate that message.\\n\\nThere is also an unresolved specification conflict across the supplied materials. The agreed outline says the study jointly optimizes T3 geometry and FDM processing parameters. The abstract\\u2019s design vector lists four continuous geometric variables, one integer, and two categorical variables, apparently **seven variables**, while its fabrication section says print parameters are held fixed within a batch. The slide says **six variables**. A DAC audience will notice if the spoken details do not reconcile these versions.\\n\\nSlides 8\\u201310 explain why physical feedback is useful, but they do not establish why qNEHVI is the right algorithm or whether adaptive selection helped. The visible deck never names qNEHVI, the feasible Pareto front, the peak-force constraint, batch size, initialization, or test budget. Hiding slide 5 is reasonable if it is only a generic \\u201cwhat is BO?\\u201d slide copied from another talk. But slide 7 must then carry the application-specific methodological content.\\n\\nWhen slide 10 says the workflow \\u201csignificantly accelerate[s]\\u201d optimization, I wait for a performance-versus-budget curve. It never arrives. Slide 12 then generalizes to other applications. My default conclusion is exactly the skeptical one posed in the prompt: this appears to be off-the-shelf qNEHVI attached to an interesting physical apparatus, with no demonstrated algorithmic or empirical advantage yet.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cIt was an experimental self-driving-lab-style application of batch multi-objective BO to printed impact absorbers, but the talk did not show whether BO beat space-filling sampling.\\u201d\\n\\nThat differs from the intended message because I would not repeat \\u201cdozens, not hundreds\\u201d without a budget-matched baseline.\\n\\n## (c) My top three Q&A questions\\n\\n1. What are the exact decision variables and encodings, and why do the deck, scope statement, and abstract appear to disagree on whether there are six or seven variables and whether FDM parameters are optimized?\\n2. How is observation noise estimated: fixed noise, learned heteroscedastic noise, technical replicates, or repeated control specimens? What is the replicate allocation?\\n3. Against what budget-matched baseline do you evaluate qNEHVI, and what metric do you report: feasible hypervolume, best feasible objective values, or attainment probability versus physical tests?\\n\\n## (d) My most pointed objection\\n\\n**Nothing visible distinguishes a validated BO contribution from an untested application of standard qNEHVI.**\\n\\nUnanswered, this is **critical for DAC-10 reception**. Application novelty can be enough, but only if the physical campaign is rigorous and the adaptive method is compared fairly under the same test budget.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 7.** Replace the black-box inventory with a visual of posterior mean plus uncertainty \\u2192 constrained batch acquisition \\u2192 next physical tests, accompanied by the exact objectives and force constraint.\\n- **Hidden-slide decision:** **Keep slide 5 hidden** as a standalone generic BO tutorial. Merge one plain-language BO sentence into slide 7 and put qNEHVI details in backup.\\n\\n---\\n\\n# P2. The aerospace practitioner\\n\\n## (a) My reaction in the room\\n\\nThe Super Ball Bot hook works because I know the lineage. Reusable landing structures are interesting. But the title\\u2019s claim that tensegrity provides \\u201crobust, reusable solutions\\u201d is broader than what this deck demonstrates. A lab drop of a polymer proxy is not evidence of robustness in thermal extremes, vacuum, dust, radiation, repeated impacts, or off-axis terrain contact.\\n\\nThe deck never immediately tells me that the actual specimen is **tensegrity-inspired**, lacks ideal compression-only and tension-only membership, and is not intended to fly. That makes slide 2 vulnerable to looking like borrowed aerospace relevance.\\n\\nSlide 4 is plausible but too broad. Tensegrity design can be difficult, but aerospace development is not made slow only by design-space search. Verification, environmental qualification, uncertainty, packaging, guidance, and system integration matter. A faster specimen loop is useful, but it is one subsystem tool.\\n\\nI am not helped much by slide 7. I need BO translated into engineering behavior: it chooses the next small batch based on predicted performance and uncertainty while rejecting designs expected to violate a payload-force limit. The slide should name that limit and show the actual trade-off.\\n\\nSlides 8 and 9 are the first ones that answer \\u201cwhat did you build and how did you hit it?\\u201d A labeled specimen, deformation sequence, drop mass, speed or energy, orientation, and force measurement would hold my attention. Two simultaneous looping videos may not. I also want to know whether the structure rebounds, survives repeated drops, and retains its response, because the opening emphasizes reuse.\\n\\nSlide 10 is understandable. Slide 12 loses me because it jumps from a T3 polymer coupon to lattices and crutch tips without stating transfer conditions or technology gates.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey use an optimizer to choose which 3D-printed soft\\u2013rigid impact absorber to drop-test next, with the eventual motivation of reusable landing structures.\\u201d\\n\\nThat is fairly close, but I would deliberately downgrade \\u201ctensegrity\\u201d to \\u201ctensegrity-inspired absorber\\u201d and treat planetary landing as motivation only.\\n\\n## (c) My top three Q&A questions\\n\\n1. What makes the printed T3 specimen mechanically tensegrity-inspired rather than an ordinary compliant crush structure, and is it actually prestressed?\\n2. What impact energy, mass, orientation, and transmitted-force cap are used, and how do they relate to a payload-protection requirement?\\n3. How does performance change over repeated impacts, and what failure modes or permanent set limit the claimed reusability?\\n\\n## (d) My most pointed objection\\n\\n**The opening borrows flight relevance and reusability, but the visible deck never establishes the proxy boundary or shows repeated-impact evidence.**\\n\\nUnanswered, this is **highly damaging** to credibility with aerospace practitioners, though it does not invalidate the optimization workflow itself.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 2.** Keep the hook, but place a photograph of the actual T3 specimen beside the Super Ball Bot and state \\u201cmotivation, not demonstrated flight readiness.\\u201d\\n- **Hidden-slide decision:** **Keep slide 3 hidden.** A generic elbow-friction GIF would distract and oversimplify. Merge a precise tensegrity-mechanism annotation into slide 8 instead.\\n\\n---\\n\\n# P3. The additive-manufacturing/materials researcher\\n\\n## (a) My reaction in the room\\n\\nSlides 1\\u20134 give me a recognizable motivation for rapid experimental iteration. My interest rises on slide 8 because multi-material fused deposition modeling is where the claimed loop can succeed or fail.\\n\\nThe message \\u201csingle-build co-fabrication, with no joining of parts\\u201d is attractive, but it needs exact boundaries. Is there support removal, trimming, conditioning, manual tensioning, or fixture insertion? Does \\u201csingle build\\u201d mean one tool-change sequence on one machine, or truly co-deposited interfaces throughout the architecture? Are PLA and thermoplastic polyurethane (TPU) mechanically interlocked, chemically bonded, or merely in contact?\\n\\nThe abstract provides more useful information than the slide: a core-wrapping architecture is intended to resist delamination, specimens are weighed and dimensionally inspected, and a control is retested to monitor drift. Those are exactly the details the talk should surface. Slide 8\\u2019s planned printing video will not replace a labeled cross-section or interface image.\\n\\nSlide 9 raises repeatability questions. TPU is rate-, temperature-, moisture-, and history-dependent. \\u201cLimited, noisy data\\u201d on slide 7 acknowledges noise but does not show the control plan. I need batch identity, drying/conditioning, build position, print orientation, machine state, interface failures, and replicates.\\n\\nThe deck also appears inconsistent about process parameters. The agreed scope says FDM processing parameters are optimized with structure, but the abstract says temperature, line width, infill, and retraction are held fixed within a batch. This could mean they vary between batches or are controlled rather than optimized, but the deck must say which.\\n\\nI like the physical loop on slide 10. I do not yet accept that multi-material printing \\u201csupplies data fast\\u201d until cycle time and failure rate are shown.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey co-print PLA and TPU impact specimens and use each mechanical test to guide the next design batch.\\u201d\\n\\nI would probably omit \\u201cno joining\\u201d and \\u201cdozens rather than hundreds\\u201d until I saw interface qualification, failure accounting, and campaign data.\\n\\n## (c) My top three Q&A questions\\n\\n1. How is the PLA\\u2013TPU interface designed and qualified, and what fraction of specimens fail by delamination or print defects?\\n2. Which material, machine, conditioning, and build-position variables are controlled, measured as covariates, or optimized?\\n3. How many true replicates and repeated control specimens are used to separate BO improvement from printer drift and TPU batch variation?\\n\\n## (d) My most pointed objection\\n\\n**The deck treats co-fabrication as an enabling fact without showing interface integrity, repeatability, or the remaining manual processing steps.**\\n\\nUnanswered, this is **highly damaging** because print variation is part of both the physical mechanism and the statistical noise model.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 8.** Replace or supplement the printing clip with an annotated specimen and interface detail: PLA struts, continuous TPU network, deposition/wrapping strategy, prestress status, post-processing, and observed failure modes.\\n- **Hidden-slide decision:** **Keep slide 3 hidden.** Its proposed generic GIFs do not answer the materials questions. Put the relevant deformation mechanism on slide 8.\\n\\n---\\n\\n# P4. The first-year graduate student\\n\\n## (a) My reaction in the room\\n\\nSlide 1 sounds positive, and slide 2 gives me a memorable reason to care. I understand that the lander survives an impact because the structure itself deforms. But I do not yet know what \\u201ctensegrity\\u201d means, and the hidden slide 3 would not necessarily fix that: an elbow GIF and a squishing GIF could leave me with an inaccurate friction-versus-elasticity story.\\n\\nSlide 4 is easy to follow. The three words \\u201cIteration, Building, Testing\\u201d form a simple problem statement, although the images need to make their relationship obvious.\\n\\nI get lost on slide 7. Six variables of what? What are the two input-data types? Are the two objectives \\u201cabsorb energy\\u201d and \\u201creduce force,\\u201d or something else? I do not know what a black box means in this setting. The title says Bayesian optimization works with noisy data, but I have not been shown how it decides anything.\\n\\nSlide 8 helps because I can see printing. Still, I need labels that distinguish the rigid and flexible parts and one sentence explaining why the object is \\u201ctensegrity-inspired.\\u201d Slide 9 is intuitive if the clips are synchronized and annotated. I would understand even better if the slide drew the metric extraction directly on a force\\u2013time or force\\u2013displacement trace.\\n\\nSlide 10 is the clearest slide in the deck. Printing \\u2192 testing \\u2192 optimizer \\u2192 next print is the idea I will remember. But it arrives after the technical terms rather than before them. I would benefit from seeing a version of this loop before slide 7 and then revisiting it after the methods.\\n\\nSlide 12 feels like a new presentation. I have not seen whether the current system worked, so the crutch tip and lattice do not feel earned. Then the talk ends on a blank question slide rather than telling me the answer.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey repeatedly print and drop-test flexible 3D structures, and a machine-learning model decides what to print next.\\u201d\\n\\nMy retelling loses the two objectives, the force constraint, and the \\u201cdozens rather than hundreds\\u201d quantitative point. I might incorrectly call the method generic machine learning rather than Bayesian optimization.\\n\\n## (c) My top three Q&A questions\\n\\n1. What exactly is tensegrity, and which parts of your printed specimen are in tension and compression?\\n2. What are the six things the optimizer can change, and what are the two scores it is trying to improve?\\n3. How does the optimizer know whether to try something uncertain rather than the design it currently thinks is best?\\n\\n## (d) My most pointed objection\\n\\n**The deck names the ingredients before giving me a plain-language map of how they work together.**\\n\\nUnanswered, this is **moderately damaging**. I retain the loop but lose the actual research question and technical contribution.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 7.** It needs one concrete candidate, predicted performance plus uncertainty, and the next selected batch, not abstract counts around a black box.\\n- **Hidden-slide decision:** **Unhide neither slide 3 nor slide 5 as written.** If forced to choose one, **keep slide 5 hidden** and integrate its one-sentence concept into a redesigned slide 7. Generic explanation would add time without restoring the missing optimization details.\\n\\n---\\n\\n# P5. The finite-element-analysis veteran\\n\\n## (a) My reaction in the room\\n\\nThe opening is fine until slide 4 implies that physical iteration is the central route around slow design. I start waiting for the argument about simulation. Because slide 6 is hidden, the deck never states the agreed, defensible position: a sufficiently calibrated model may cost more than it saves for this campaign, particularly when interfaces, defects, rate dependence, contact, and millisecond shock transients dominate.\\n\\nThat omission avoids an overt attack on finite-element analysis (FEA), which is good, but it also creates a logical hole. Why is physical BO the chosen source of objective values? Why not a hybrid strategy, reduced-order model, analytical screening, or multi-fidelity optimization? The deck merely proceeds from \\u201cdesign is slow\\u201d to \\u201cBO makes experimentation affordable.\\u201d\\n\\nI object to any suggestion that experimental data are automatically \\u201creal\\u201d and simulations are not. Measurements also involve sensor bandwidth, filtering, fixture dynamics, uncertainty, and model-based transformations. Slide 9\\u2019s \\u201caccelerometers gather real data\\u201d wording is particularly vulnerable. A force estimate derived from acceleration still rests on calibration and assumptions.\\n\\nSlide 10 is a useful experimental workflow. It should be presented as a scoped engineering choice, not a universal replacement for simulation. Slide 12\\u2019s transfer claim needs the condition from the outline: use this pattern where tests are authoritative, costly, the design space is mixed, and goals conflict.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey bypass a calibrated finite-element model and optimize directly against impact tests because the printed interfaces and transient response are difficult to model economically.\\u201d\\n\\nThat is close to the intended experiment-first rationale, but I had to infer it; the visible slides do not say it clearly.\\n\\n## (c) My top three Q&A questions\\n\\n1. What evidence shows that calibrating a useful simulation would cost more than it saves for this campaign, rather than merely being inconvenient?\\n2. How do you verify the measured peak transmitted force, including sensor bandwidth, fixture dynamics, filtering, and uncertainty?\\n3. Why exclude analytical or low-fidelity screening from a multi-fidelity BO strategy, especially for infeasible geometries and gross structural trends?\\n\\n## (d) My most pointed objection\\n\\n**The deck uses experiment-first optimization without stating a scoped cost-and-validity argument or acknowledging where simulation could still add value.**\\n\\nUnanswered, this is **highly damaging in Q&A**. It makes the methodology look ideologically anti-simulation rather than economically reasoned.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 4.** It should become the two-sided decision: exhaustive physical search is too costly, while campaign-grade calibration for the hardest responses is also costly; therefore choose physical tests adaptively.\\n- **Hidden-slide decision:** **Merge slide 6 into slide 4**, rather than unhide it as written. Keep a fuller evidence-backed simulation/experiment comparison as backup.\\n\\n---\\n\\n# P6. The friendly industry generalist\\n\\n## (a) My reaction in the room\\n\\nThe opening video is memorable, and I like the practical goal of shortening build\\u2013test iteration. Slide 4 is easy to understand. My attention slips on slide 7 because the counts and black-box symbol do not tell a story. I do not need the name qNEHVI, but I need to know that the software balances trying promising designs with learning from uncertain ones and respects a force limit.\\n\\nSlides 8\\u201310 are the strongest run. If the presenter shows one labeled specimen, one clean drop clip, and one loop diagram, I can see how I might use the pattern in another product-development problem. I want a cycle-time number and an indication of what software or human work is required.\\n\\nSlide 12 dilutes the message by offering several destinations before proving the present result. The ending is weak. A blank Q&A slide discards the chance to leave the audience with the workflow, the best specimen, and a measured improvement.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cPrint it, break or drop it, feed the measurements back, and let the optimizer pick the next batch instead of running a huge test matrix.\\u201d\\n\\nThat captures the intended practical pattern, but I would probably overgeneralize it beyond the specific conditions under which it is useful.\\n\\n## (c) My top three Q&A questions\\n\\n1. How many prints and calendar days did it take to reach a useful design compared with your previous workflow?\\n2. What parts of this system could another engineering team reuse without building the same drop tower or writing a custom optimizer?\\n3. What happens when a print fails or a test produces an outlier?\\n\\n## (d) My most pointed objection\\n\\n**I understand the loop, but the deck never gives me the one number or result that proves it was worth building.**\\n\\nUnanswered, this is **moderately to highly damaging**. I would remember the concept but not have a reason to adopt or sponsor it.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 13.** Replace the blank Q&A screen with the best measured result, the closed-loop diagram, and one deployment rule; retain a small \\u201cQuestions?\\u201d label.\\n- **Hidden-slide decision:** **Unhide slide 11** once populated. Results are more valuable than another explanatory slide.\\n\\n---\\n\\n# Synthesis\\n\\n## Cross-persona themes\\n\\n1. **The scope boundary is missing.** The Super Ball Bot is a good hook, but every persona risks interpreting a PLA\\u2013TPU proxy as a claim about flight hardware.\\n2. **Slide 7 is the weakest visible technical slide.** It serves neither experts nor novices. Counts such as \\u201c6 variables\\u201d and \\u201c2 objectives\\u201d are information, not a message.\\n3. **The main claim currently lacks evidence.** \\u201cSignificantly accelerate,\\u201d \\u201csupplies data fast,\\u201d and \\u201cdozens, not hundreds\\u201d require a campaign ledger and a budget-matched comparison.\\n4. **The results omission breaks the story.** The deck follows the outline through the workflow, then skips the outline\\u2019s largest time block and moves directly to future work.\\n5. **The physical system needs more definition.** The audience needs a labeled specimen, a clear explanation of \\u201ctensegrity-inspired,\\u201d interface design, remaining manual steps, and the measurement chain.\\n6. **Noise must be operational rather than rhetorical.** Saying the data are noisy is insufficient. Replicates, periodic controls, drift, failures, and uncertainty should be shown.\\n7. **Experiment-first needs a scoped rationale.** The deck should not dismiss FEA, but it should explain why adaptive physical testing is the economical source of objective values for this campaign.\\n8. **The close is not a conclusion.** Future applications and a blank Q&A slide do not substitute for the measured finding and its conditions of transfer.\\n9. **Media are a communication and schedule risk.** Short clips can work, but multiple looping videos violate the signal-to-noise principle and create failure modes.\\n10. **The project specification needs reconciliation.** The supplied materials differ on six versus seven variables, whether FDM process parameters are optimized, and whether peak force is a constraint or part of the Pareto trade-off.\\n\\n## Five highest-priority slide-level revisions\\n\\n### 1. Slide 11: replace one empty slot with a three-slide quantitative results sequence\\n\\nUse full-sentence titles tied to observed data:\\n\\n- **11A:** \\u201cThe campaign used **N** specimens in **B** adaptive batches, including **R** replicates and **F** failed prints.\\u201d\\n- **11B:** \\u201cAt the same physical-test budget, adaptive selection reached **[measured feasible-performance result]** faster than **[predeclared Sobol/random baseline]**.\\u201d\\n- **11C:** \\u201cThe measured feasible Pareto front trades specific energy absorption against compaction efficiency while respecting a **[value and unit]** peak-force cap.\\u201d\\n\\nShow uncertainty, replicate spread, infeasible points, and representative specimen photographs or traces. If the campaign cannot support the baseline claim, change the talk\\u2019s main message to: **\\u201cWe evaluate whether noise-aware multi-objective Bayesian optimization can reduce the physical-test budget.\\u201d**\\n\\n### 2. Slide 7: state the actual constrained optimization problem\\n\\nReplace the current black-box/count design with:\\n\\n- the named decision variables and their types;\\n- a small batch entering the printer/test loop;\\n- objectives: maximize specific energy absorption and compaction efficiency;\\n- constraint: peak transmitted force below a stated cap;\\n- qNEHVI\\u2019s role in one plain sentence: it chooses a small next batch likely to expand the feasible trade-off frontier while accounting for uncertainty and noisy tests;\\n- the declared test budget, initialization size, and batch size.\\n\\nResolve six versus seven variables and whether processing parameters are decision variables before finalizing this slide.\\n\\n### 3. Slide 2: put the proxy boundary beside the hook\\n\\nKeep the Super Ball Bot clip, but add an image of the actual specimen and the visible sentence:\\n\\n> **\\u201cHere, a printed PLA\\u2013TPU T3 prism is a proxy for developing the workflow, not flight hardware.\\u201d**\\n\\nChange or qualify \\u201crobust, reusable solutions\\u201d unless the present campaign includes repeat-impact evidence.\\n\\n### 4. Slide 4: turn the vague need into the two-sided engineering decision\\n\\nRetitle it:\\n\\n> **\\u201cBecause exhaustive testing and campaign-grade model calibration are both costly, each physical specimen must be chosen for its information value.\\u201d**\\n\\nAdd one quantified physical-search estimate and one scoped calibration challenge. Do not say simulation is untrustworthy. State that FEA and lower-fidelity models remain possible screening or future multi-fidelity tools.\\n\\n### 5. Slides 10 and 13: separate the demonstrated loop from the conclusion\\n\\n- **Slide 10:** retitle to **\\u201cThe closed loop turns each measured test into the next small batch of specimens.\\u201d** Remove \\u201csignificantly accelerate\\u201d until the results demonstrate it. Mark automated and manual stages.\\n- **Slide 13:** retain the closed-loop thumbnail, add the best quantitative result, and state the deployment rule: **use the loop when tests are authoritative, each test is costly, the design space is mixed, and performance goals conflict.** Add \\u201cQuestions?\\u201d without clearing the conclusion from the screen.\\n\\n## Hidden-slide adjudication\\n\\n| Hidden slide | Recommendation | Rationale |\\n|---|---|---|\\n| **3: tensegrity usefulness/mechanism** | **Merge into slide 8** | The generic elbow and squishing GIFs add noise and risk an inaccurate mechanism; an annotated actual specimen and deformation sequence are stronger. |\\n| **5: generic BO explanation** | **Merge into slide 7; keep original as backup or delete** | DAC experts do not need a generic advocacy slide, but novices need one plain-language sentence and a concrete acquisition visual. |\\n| **6: specimen information value/experiment-first** | **Merge into slide 4; keep evidence-rich version as backup** | The argument is structurally necessary, but the current title is vague and the visual does not support the scoped calibration-versus-campaign-cost decision. |\\n| **11: results** | **Unhide and expand to three slides** | Results are the evidentiary center of the talk and were allocated five minutes in the agreed outline. An empty or hidden results slot makes the main claim untenable. |\\n\\n## Visible-slide claims needing evidence, hedging, or backup\\n\\n| Slide | Claim | Required treatment |\\n|---|---|---|\\n| **1** | \\u201cBuild better tensegrity structures faster\\u201d | Define \\u201cbetter\\u201d with the two objectives and force constraint; support \\u201cfaster\\u201d with the results comparison. |\\n| **2** | Tensegrity is robust and reusable for planetary landings | Cite the Super Ball Bot source; distinguish concept evidence from this study; provide repeat-impact evidence or hedge reusability. |\\n| **2** | Parachutes and retrorockets struggle in the stated conditions | Cite an authoritative aerospace source if retained in narration; avoid implying universal inadequacy. |\\n| **4** | Current tensegrity design is slow and resource intensive | Quantify candidate count, print/test time, hand-assembly burden, or calibration effort. |\\n| **7** | BO makes experiments affordable with limited, noisy data | Show campaign budget, noise characterization, replicates, baseline, and performance versus tests. Prepare qNEHVI and Gaussian-process details in backup. |\\n| **7** | Six variables, two input-data types, two objectives | Reconcile with the apparent seven-variable abstract specification and identify every quantity. Clarify \\u201cinput-data types.\\u201d |\\n| **8** | Multi-material AM supplies data fast | Report print-to-result cycle time and failure rate. |\\n| **8** | Single-build co-fabrication with no joining | Show interface and post-processing; use \\u201cno joining of PLA and TPU members\\u201d if other manual operations remain. |\\n| **9** | Accelerometers gather the relevant impact data | Show sensor locations and the derivation of transmitted force, specific energy absorption, and compaction efficiency. Reconcile this wording with the abstract\\u2019s instrumented-tup description. |\\n| **10** | The loop significantly accelerates optimization | Remove \\u201csignificantly\\u201d unless a defined baseline and uncertainty support it. Statistical significance should not be implied casually. |\\n| **12** | Workflow will transfer to lattices and other applications | Present as conditional future work with transfer criteria, not an achieved result. Attribute any external crutch-tip image and claim. |\\n\\n### Prepared backup slides\\n\\n1. Full design-variable table, bounds, encoding, and fixed versus optimized process parameters.\\n2. qNEHVI implementation, Gaussian-process assumptions, constraint treatment, batch size, and acquisition settings.\\n3. Noise plan: replicates, periodic control, heteroscedasticity, drift, outlier policy, and uncertainty propagation.\\n4. Budget-matched baseline definition and evaluation metric.\\n5. Force-cap rationale and full objective definitions.\\n6. Sensor chain, calibration, sample rate, filtering, fixture dynamics, and metric extraction.\\n7. PLA\\u2013TPU interface, conditioning, build layout, dimensional/mass deviations, and print-failure modes.\\n8. Scoped FEA rationale with citations or internal validation; possible analytical or multi-fidelity extensions.\\n9. Automation map and cycle-time breakdown.\\n10. Repeated-impact behavior if reusability remains in the opening.\\n\\n## Merged, ordered presenter TODO list\\n\\n### Must fix before the talk\\n\\n1. **Freeze the scientific specification.** Reconcile the number and type of decision variables, whether FDM parameters are optimized or controlled, the two objectives, the force constraint, treatment of print failures, and the measurement chain. Use one formulation across slides, abstract, paper, and Q&A.\\n2. **Set a results completion plan.** Establish dates and owners for campaign completion, repeat tests, data quality review, baseline analysis, and slide freeze. Predeclare the fallback claim if the baseline does not support \\u201cdozens rather than hundreds.\\u201d\\n3. **Build the three-slide results sequence.** Campaign ledger \\u2192 budget-matched baseline \\u2192 measured feasible Pareto front. Allocate approximately five minutes, as the agreed outline requires.\\n4. **Add the proxy statement to slide 2.** State plainly that the PLA\\u2013TPU T3 specimen is a workflow proxy and not flight hardware.\\n5. **Redesign slide 7.** Show the exact constrained multi-objective problem, test budget, small batches, uncertainty, noisy observations, and the role of qNEHVI.\\n6. **Remove unsupported result language.** Until supported, delete \\u201csignificantly\\u201d from slide 10 and avoid \\u201cdozens rather than hundreds\\u201d in the title, narration, or close.\\n7. **Clarify experiment-first positioning.** Merge the information-value argument into slide 4 and prepare an evidence-backed FEA backup slide.\\n8. **Make the experiment auditable.** On slide 9 or backup, reconcile accelerometers and the instrumented tup; show how all three performance metrics are calculated and how uncertainty is handled.\\n9. **De-risk every video.** Store local trimmed files, disable distracting loops, test them on presentation hardware, and include static fallback frames.\\n10. **Rehearse to 13:30\\u201314:00.** Protect the results and conclusion from being cut. Practice once without slides, consistent with Doumont\\u2019s preparation advice.\\n\\n### Should fix\\n\\n11. **Replace slide 8\\u2019s generic print emphasis with mechanism and materials evidence.** Label PLA, TPU, interface strategy, prestress status, deformation, post-processing, and failure modes.\\n12. **Quantify slide 4.** Give at least one campaign-relevant cost, duration, or candidate-count estimate.\\n13. **Show automation boundaries on slide 10.** Candidate selection and surrogate updating may be automated; slicing, handling, test setup, and testing remain manual according to the abstract.\\n14. **Convert slide 12 to a gated roadmap.** Separate demonstrated T3-proxy results from future lattices, flight materials, and terrestrial applications.\\n15. **Turn slide 13 into the conclusion.** Leave the audience with the best result and transfer rule while taking questions.\\n16. **Prepare technical backup slides.** Prioritize baseline fairness, noise, force-cap rationale, print failures, materials repeatability, and FEA positioning.\\n\\n### Polish\\n\\n17. Replace the slide 1 title with a specific message, such as **\\u201cPhysical Bayesian optimization searches multi-material tensegrity absorbers one small test batch at a time.\\u201d**\\n18. Left-align titles and use intentional line breaks; check every slide at six-per-page scale.\\n19. Use one purposeful visual per slide. Avoid decorative loading or elbow animations.\\n20. Standardize terminology: \\u201cBayesian optimization,\\u201d not \\u201cBays Opt\\u201d; \\u201ctensegrity-inspired\\u201d where appropriate; define specific energy absorption once.\\n21. Add citations directly beside borrowed footage, images, and quantitative claims.\\n\\n## Who the deck serves best and worst\\n\\n- **Best served:** **P6, the friendly industry generalist.** The visible sequence is visually intuitive, practical, and easy to reduce to \\u201cprint \\u2192 test \\u2192 learn \\u2192 repeat.\\u201d\\n- **Worst served:** **P1, the skeptical BO insider.** The current deck omits nearly every item needed to judge a DAC-10 contribution: exact problem formulation, budget, baseline, noise model, constraint handling, and empirical adaptive advantage.\\n\\nThat is **not the right trade-off for DAC-10**. The deck should remain accessible to P4 and P6, but its technical center must satisfy P1 without turning into a BO lecture. One rigorous optimization slide, three quantitative results slides, and targeted backup material would achieve that balance.\\n\\n## Predicted reception\\n\\n- **As is: 4/10.** The physical workflow and application are memorable, but the absent scope statement, empty results section, vague BO slide, and unsupported acceleration claim leave the DAC contribution unproven.\\n- **After the must-fixes: 8/10.** A scoped proxy claim, reconciled methods, fair budget-matched comparison, feasible Pareto front, and measured conclusion would turn the same visual story into a credible design-optimization application talk.\\n\\n## Discretionary analytical decisions\\n\\n- Evaluated only the visible slide order for the simulated in-room reactions, while using hidden slides and presenter notes to judge revision options.\\n- Treated planned placeholder visuals as if implemented according to the presenter\\u2019s notes, but judged the selected visual concept and associated stage risk rather than absent graphic quality.\\n- Prioritized the Draft 3 outline over conflicting Draft 1 wording because the prompt identifies it as the agreed story arc, scope framing, results plan, and timing plan.\\n- Rated objections qualitatively as moderate, high, or critical rather than assigning unsupported numerical probabilities.\\n- Recommended three results slides rather than preserving one reserved slot because the agreed outline specifies three distinct evidentiary products and allocates five minutes to results.\\n- Recommended merging hidden slides 3, 5, and 6 rather than simply unhiding them because their necessary ideas can be conveyed with higher signal-to-noise on existing slides.\\n- Treated the six-versus-seven-variable and optimized-versus-fixed-process-parameter differences as unresolved specification conflicts requiring reconciliation, rather than inferring which document is correct.\\n- Assessed claims against the supplied deck extraction, Draft 3 outline, Doumont notes, and submitted abstract only; no independent validation of cited literature or unseen experimental data was attempted.\"}", + "name": "submit_answer" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "tool", + "content": "175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "info": { + "start_ts": 8153.825818486, + "end_ts": 8153.828437556 + } + } + ], + "next_observation": [ + { + "role": "tool", + "content": "# Mock audience report: IDETC-CIE 2026 slide deck, Draft 1\n\n## Executive read\n\nThe visible deck has a clear visual spine: **motivation \u2192 slow iteration \u2192 Bayesian optimization \u2192 multi-material printing \u2192 impact testing \u2192 closed loop \u2192 future applications**. The strongest section is slides 8\u201310, where fabrication, testing, and adaptive selection finally appear as one workflow.\n\nBut Draft 1 does not yet deliver the technical case promised by the agreed outline. The visible deck omits four things a DAC-10 audience will need:\n\n1. **Honest scope:** no visible slide says that the PLA\u2013TPU T3 prism is a proxy system rather than flight hardware.\n2. **A precise optimization problem:** the six variables, two objectives, force constraint, noise treatment, and fabrication failures are not intelligible on slide 7.\n3. **Quantitative evidence:** the sole results slot is hidden and empty, yet slides 10 and 12 already claim acceleration and transfer.\n4. **A measured conclusion:** the talk jumps from the workflow to speculative applications without showing a campaign ledger, comparison against a budget-matched baseline, or feasible Pareto front.\n\nThe slide titles generally follow Doumont\u2019s full-sentence-message rule, but several messages outrun the evidence. The deck also departs substantially from the Draft 3 timing plan, which reserves **5 of 15 minutes for results**. As built, results receive zero visible slides.\n\n---\n\n# P0. The program manager in the audience\n\n## (a) My reaction in the room\n\nSlide 1 tells me the team wants to work faster, but it does not tell me what is being optimized or what has actually been built. The title sounds aspirational rather than traceable to a project deliverable. I immediately want the formal title from the abstract or a shorter version of it.\n\nThe Super Ball Bot footage on slide 2 is an effective hook. I understand why reusable impact attenuation matters. But the slide creates a scope risk: I am looking at planetary landing hardware while the actual work is a printed PLA\u2013TPU T3 proxy. Because that distinction is not stated immediately, I may spend the rest of the talk evaluating the work against flight-readiness criteria the project never intended to meet.\n\nSlide 4 gives me the business problem, but not a baseline. \u201cSlow and resource intensive\u201d needs a number: hours per print-test cycle, number of factors, or the candidate count under a declared design of experiments. Without that, I cannot assess whether the proposed loop attacks the dominant schedule driver.\n\nSlide 7 is where I expect the project definition. Instead, \u201c6 variables,\u201d \u201c2 types of input data,\u201d and \u201c2 objectives\u201d read like inventory labels. I cannot reconstruct the statement of work from them. I need to see what is automated, what remains manual, what the force cap is, and what constitutes a successful campaign.\n\nSlides 8 and 9 make the project tangible. A labeled specimen and an actual drop test would build confidence. Yet \u201csingle-build\u201d and \u201cno joining\u201d need a process qualification caveat, and I need the measurement chain to be explicit. The abstract refers to an instrumented tup and force\u2013time data; slide 9 says accelerometers. Those may be compatible, but the deck presently leaves the relationship unclear.\n\nSlide 10 is the right management view of the system, but \u201csignificantly accelerate\u201d is a result claim placed on a workflow diagram. At this point I ask, \u201cCompared with what, by how much, and over how many specimens?\u201d Then the deck skips the hidden results slide and goes directly to future applications. That is the point where my confidence drops sharply. The project is asking me to extrapolate before closing the evidence loop.\n\nSlide 12 is too early and too broad. A crutch tip and lattice can be legitimate next phases, but only after the team shows what the present campaign established and what technical risks remain. The blank questions slide gives me no durable take-home message.\n\n### PM stage-risk register\n\n| Risk | Likelihood | Consequence | Mitigation |\n|---|---:|---:|---|\n| Results are incomplete by conference lock | Medium to high | Critical: central claim becomes unsupported | Set a dated campaign freeze; predeclare a fallback message and minimum publishable result package |\n| Embedded Super Ball Bot or drop-test video fails | Medium | Moderate: weak opening or broken pacing | Use local, trimmed files; retain a still-frame fallback on every video slide |\n| Talk overruns because several videos and explanations are improvised | High | High: results or conclusion gets cut | Script videos to 10\u201315 seconds total each; rehearse to 13:30; assign 5 minutes to results |\n| Scope is mistaken for flight-hardware validation | High | High: credibility damage in Q&A | Put \u201cPLA\u2013TPU T3 proxy; not flight hardware\u201d on slide 2 or the next slide |\n| Acceleration claim lacks a budget-matched comparator | High | Critical for DAC review audience | Show Sobol/random or another predeclared baseline at the same physical-test budget |\n| Measurement chain is challenged | Medium | High | Add a methods schematic and backup slide on calibration, sampling, filtering, and metric extraction |\n| \u201cNo joining\u201d or \u201cnear autonomous\u201d is read as full automation | Medium | Moderate | List manual steps explicitly: slicing, handling, setup, and testing |\n| Animations distract or fail to communicate without narration | Medium | Moderate | Prefer one short clip plus static annotated frames; follow Doumont\u2019s signal-to-noise rule |\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey are building a loop that prints PLA\u2013TPU tensegrity-like absorbers, drop-tests them, and lets Bayesian optimization choose the next print.\u201d\n\nThat retains the basic workflow but loses the intended **dozens rather than hundreds** claim because the deck never demonstrates it. I would also probably describe the work as \u201clander research,\u201d which is a distortion caused by the missing proxy-system statement.\n\n## (c) My top three Q&A questions\n\n1. What is the dated plan to complete the campaign, baseline comparison, and repeat testing before the conference slide freeze?\n2. Which exact operations are automated today, which remain manual, and what is the measured end-to-end cycle time per adaptive batch?\n3. What evidence will justify \u201cdozens rather than hundreds,\u201d and what will you claim if the budget-matched baseline does not show an advantage?\n\n## (d) My most pointed objection\n\n**You are presenting a claimed acceleration without showing a completed campaign, a comparator, or even a visible results slide.**\n\nUnanswered, this is **critical**. I would not fund a scale-up phase from this deck. I might fund completion of the present campaign if the team showed a disciplined schedule, predefined success criteria, and a credible fallback.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 11.** It must become a real, scheduled results package, preferably three slides: campaign ledger, budget-matched baseline, and feasible Pareto front.\n- **Hidden-slide decision:** **Unhide slide 11**, but not in its current empty form. The talk cannot make its principal claim without it.\n\n### PM slide-level punch list, ordered by schedule criticality\n\n**Must be fixed first**\n\n1. **Slide 11/results sequence:** assign owners and dates for data freeze, quality control, baseline computation, replicate analysis, and final plots. Define fallback wording now.\n2. **Slide 2:** add the explicit scope line, \u201cThis study uses a printed PLA\u2013TPU T3 prism as a proxy for developing the workflow; it is not flight hardware.\u201d\n3. **Slide 7:** replace category counts with the actual optimization problem, test budget, batch size, objectives, constraint, and noise/failure treatment.\n4. **Slide 10:** remove \u201csignificantly\u201d until slide 11 demonstrates a quantitative improvement; show automation boundaries and the loop\u2019s measured cycle time.\n5. **Slides 2 and 9:** download and trim media, test on the conference laptop, and put a static fallback image on each slide.\n\n**Then fix**\n\n6. **Slide 4:** quantify the current cost or candidate count and identify the bottleneck.\n7. **Slide 8:** label PLA, TPU, interface, and any post-processing; qualify \u201cno joining.\u201d\n8. **Slide 9:** reconcile accelerometers with the instrumented-tup description and show how force, energy absorption, and compaction efficiency are obtained.\n9. **Slide 12:** convert the application collage into a conditional next-phase roadmap with gates.\n10. **Slide 13:** end on the measured result and deployment rule, not a blank Q&A screen.\n\n**Nice to have**\n\n11. Replace decorative or looping media on slide 4 with one static process image unless it communicates a measured delay.\n12. Add backup slides on Bayesian optimization implementation, finite-element-analysis positioning, repeatability, force-cap rationale, and print failures.\n\n---\n\n# P1. The skeptical Bayesian-optimization insider\n\n## (a) My reaction in the room\n\nThe opening is competent but generic for a design-automation conference. Slides 1\u20134 set up an expensive physical experiment, which is a valid application class for Bayesian optimization (BO). I become interested when the presenter reaches slide 7, because that is where methodological seriousness should appear.\n\nInstead, slide 7 says only \u201c6 variables,\u201d \u201c2 types of input data,\u201d and \u201c2 objectives.\u201d I do not learn the variables, their types, how categorical choices are encoded, whether process parameters are optimized, how noisy observations enter the Gaussian processes, how replicates identify noise, how failures are modeled, or how the force cap enters the acquisition. The title says BO handles noisy data, but the visual does not substantiate that message.\n\nThere is also an unresolved specification conflict across the supplied materials. The agreed outline says the study jointly optimizes T3 geometry and FDM processing parameters. The abstract\u2019s design vector lists four continuous geometric variables, one integer, and two categorical variables, apparently **seven variables**, while its fabrication section says print parameters are held fixed within a batch. The slide says **six variables**. A DAC audience will notice if the spoken details do not reconcile these versions.\n\nSlides 8\u201310 explain why physical feedback is useful, but they do not establish why qNEHVI is the right algorithm or whether adaptive selection helped. The visible deck never names qNEHVI, the feasible Pareto front, the peak-force constraint, batch size, initialization, or test budget. Hiding slide 5 is reasonable if it is only a generic \u201cwhat is BO?\u201d slide copied from another talk. But slide 7 must then carry the application-specific methodological content.\n\nWhen slide 10 says the workflow \u201csignificantly accelerate[s]\u201d optimization, I wait for a performance-versus-budget curve. It never arrives. Slide 12 then generalizes to other applications. My default conclusion is exactly the skeptical one posed in the prompt: this appears to be off-the-shelf qNEHVI attached to an interesting physical apparatus, with no demonstrated algorithmic or empirical advantage yet.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cIt was an experimental self-driving-lab-style application of batch multi-objective BO to printed impact absorbers, but the talk did not show whether BO beat space-filling sampling.\u201d\n\nThat differs from the intended message because I would not repeat \u201cdozens, not hundreds\u201d without a budget-matched baseline.\n\n## (c) My top three Q&A questions\n\n1. What are the exact decision variables and encodings, and why do the deck, scope statement, and abstract appear to disagree on whether there are six or seven variables and whether FDM parameters are optimized?\n2. How is observation noise estimated: fixed noise, learned heteroscedastic noise, technical replicates, or repeated control specimens? What is the replicate allocation?\n3. Against what budget-matched baseline do you evaluate qNEHVI, and what metric do you report: feasible hypervolume, best feasible objective values, or attainment probability versus physical tests?\n\n## (d) My most pointed objection\n\n**Nothing visible distinguishes a validated BO contribution from an untested application of standard qNEHVI.**\n\nUnanswered, this is **critical for DAC-10 reception**. Application novelty can be enough, but only if the physical campaign is rigorous and the adaptive method is compared fairly under the same test budget.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 7.** Replace the black-box inventory with a visual of posterior mean plus uncertainty \u2192 constrained batch acquisition \u2192 next physical tests, accompanied by the exact objectives and force constraint.\n- **Hidden-slide decision:** **Keep slide 5 hidden** as a standalone generic BO tutorial. Merge one plain-language BO sentence into slide 7 and put qNEHVI details in backup.\n\n---\n\n# P2. The aerospace practitioner\n\n## (a) My reaction in the room\n\nThe Super Ball Bot hook works because I know the lineage. Reusable landing structures are interesting. But the title\u2019s claim that tensegrity provides \u201crobust, reusable solutions\u201d is broader than what this deck demonstrates. A lab drop of a polymer proxy is not evidence of robustness in thermal extremes, vacuum, dust, radiation, repeated impacts, or off-axis terrain contact.\n\nThe deck never immediately tells me that the actual specimen is **tensegrity-inspired**, lacks ideal compression-only and tension-only membership, and is not intended to fly. That makes slide 2 vulnerable to looking like borrowed aerospace relevance.\n\nSlide 4 is plausible but too broad. Tensegrity design can be difficult, but aerospace development is not made slow only by design-space search. Verification, environmental qualification, uncertainty, packaging, guidance, and system integration matter. A faster specimen loop is useful, but it is one subsystem tool.\n\nI am not helped much by slide 7. I need BO translated into engineering behavior: it chooses the next small batch based on predicted performance and uncertainty while rejecting designs expected to violate a payload-force limit. The slide should name that limit and show the actual trade-off.\n\nSlides 8 and 9 are the first ones that answer \u201cwhat did you build and how did you hit it?\u201d A labeled specimen, deformation sequence, drop mass, speed or energy, orientation, and force measurement would hold my attention. Two simultaneous looping videos may not. I also want to know whether the structure rebounds, survives repeated drops, and retains its response, because the opening emphasizes reuse.\n\nSlide 10 is understandable. Slide 12 loses me because it jumps from a T3 polymer coupon to lattices and crutch tips without stating transfer conditions or technology gates.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey use an optimizer to choose which 3D-printed soft\u2013rigid impact absorber to drop-test next, with the eventual motivation of reusable landing structures.\u201d\n\nThat is fairly close, but I would deliberately downgrade \u201ctensegrity\u201d to \u201ctensegrity-inspired absorber\u201d and treat planetary landing as motivation only.\n\n## (c) My top three Q&A questions\n\n1. What makes the printed T3 specimen mechanically tensegrity-inspired rather than an ordinary compliant crush structure, and is it actually prestressed?\n2. What impact energy, mass, orientation, and transmitted-force cap are used, and how do they relate to a payload-protection requirement?\n3. How does performance change over repeated impacts, and what failure modes or permanent set limit the claimed reusability?\n\n## (d) My most pointed objection\n\n**The opening borrows flight relevance and reusability, but the visible deck never establishes the proxy boundary or shows repeated-impact evidence.**\n\nUnanswered, this is **highly damaging** to credibility with aerospace practitioners, though it does not invalidate the optimization workflow itself.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 2.** Keep the hook, but place a photograph of the actual T3 specimen beside the Super Ball Bot and state \u201cmotivation, not demonstrated flight readiness.\u201d\n- **Hidden-slide decision:** **Keep slide 3 hidden.** A generic elbow-friction GIF would distract and oversimplify. Merge a precise tensegrity-mechanism annotation into slide 8 instead.\n\n---\n\n# P3. The additive-manufacturing/materials researcher\n\n## (a) My reaction in the room\n\nSlides 1\u20134 give me a recognizable motivation for rapid experimental iteration. My interest rises on slide 8 because multi-material fused deposition modeling is where the claimed loop can succeed or fail.\n\nThe message \u201csingle-build co-fabrication, with no joining of parts\u201d is attractive, but it needs exact boundaries. Is there support removal, trimming, conditioning, manual tensioning, or fixture insertion? Does \u201csingle build\u201d mean one tool-change sequence on one machine, or truly co-deposited interfaces throughout the architecture? Are PLA and thermoplastic polyurethane (TPU) mechanically interlocked, chemically bonded, or merely in contact?\n\nThe abstract provides more useful information than the slide: a core-wrapping architecture is intended to resist delamination, specimens are weighed and dimensionally inspected, and a control is retested to monitor drift. Those are exactly the details the talk should surface. Slide 8\u2019s planned printing video will not replace a labeled cross-section or interface image.\n\nSlide 9 raises repeatability questions. TPU is rate-, temperature-, moisture-, and history-dependent. \u201cLimited, noisy data\u201d on slide 7 acknowledges noise but does not show the control plan. I need batch identity, drying/conditioning, build position, print orientation, machine state, interface failures, and replicates.\n\nThe deck also appears inconsistent about process parameters. The agreed scope says FDM processing parameters are optimized with structure, but the abstract says temperature, line width, infill, and retraction are held fixed within a batch. This could mean they vary between batches or are controlled rather than optimized, but the deck must say which.\n\nI like the physical loop on slide 10. I do not yet accept that multi-material printing \u201csupplies data fast\u201d until cycle time and failure rate are shown.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey co-print PLA and TPU impact specimens and use each mechanical test to guide the next design batch.\u201d\n\nI would probably omit \u201cno joining\u201d and \u201cdozens rather than hundreds\u201d until I saw interface qualification, failure accounting, and campaign data.\n\n## (c) My top three Q&A questions\n\n1. How is the PLA\u2013TPU interface designed and qualified, and what fraction of specimens fail by delamination or print defects?\n2. Which material, machine, conditioning, and build-position variables are controlled, measured as covariates, or optimized?\n3. How many true replicates and repeated control specimens are used to separate BO improvement from printer drift and TPU batch variation?\n\n## (d) My most pointed objection\n\n**The deck treats co-fabrication as an enabling fact without showing interface integrity, repeatability, or the remaining manual processing steps.**\n\nUnanswered, this is **highly damaging** because print variation is part of both the physical mechanism and the statistical noise model.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 8.** Replace or supplement the printing clip with an annotated specimen and interface detail: PLA struts, continuous TPU network, deposition/wrapping strategy, prestress status, post-processing, and observed failure modes.\n- **Hidden-slide decision:** **Keep slide 3 hidden.** Its proposed generic GIFs do not answer the materials questions. Put the relevant deformation mechanism on slide 8.\n\n---\n\n# P4. The first-year graduate student\n\n## (a) My reaction in the room\n\nSlide 1 sounds positive, and slide 2 gives me a memorable reason to care. I understand that the lander survives an impact because the structure itself deforms. But I do not yet know what \u201ctensegrity\u201d means, and the hidden slide 3 would not necessarily fix that: an elbow GIF and a squishing GIF could leave me with an inaccurate friction-versus-elasticity story.\n\nSlide 4 is easy to follow. The three words \u201cIteration, Building, Testing\u201d form a simple problem statement, although the images need to make their relationship obvious.\n\nI get lost on slide 7. Six variables of what? What are the two input-data types? Are the two objectives \u201cabsorb energy\u201d and \u201creduce force,\u201d or something else? I do not know what a black box means in this setting. The title says Bayesian optimization works with noisy data, but I have not been shown how it decides anything.\n\nSlide 8 helps because I can see printing. Still, I need labels that distinguish the rigid and flexible parts and one sentence explaining why the object is \u201ctensegrity-inspired.\u201d Slide 9 is intuitive if the clips are synchronized and annotated. I would understand even better if the slide drew the metric extraction directly on a force\u2013time or force\u2013displacement trace.\n\nSlide 10 is the clearest slide in the deck. Printing \u2192 testing \u2192 optimizer \u2192 next print is the idea I will remember. But it arrives after the technical terms rather than before them. I would benefit from seeing a version of this loop before slide 7 and then revisiting it after the methods.\n\nSlide 12 feels like a new presentation. I have not seen whether the current system worked, so the crutch tip and lattice do not feel earned. Then the talk ends on a blank question slide rather than telling me the answer.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey repeatedly print and drop-test flexible 3D structures, and a machine-learning model decides what to print next.\u201d\n\nMy retelling loses the two objectives, the force constraint, and the \u201cdozens rather than hundreds\u201d quantitative point. I might incorrectly call the method generic machine learning rather than Bayesian optimization.\n\n## (c) My top three Q&A questions\n\n1. What exactly is tensegrity, and which parts of your printed specimen are in tension and compression?\n2. What are the six things the optimizer can change, and what are the two scores it is trying to improve?\n3. How does the optimizer know whether to try something uncertain rather than the design it currently thinks is best?\n\n## (d) My most pointed objection\n\n**The deck names the ingredients before giving me a plain-language map of how they work together.**\n\nUnanswered, this is **moderately damaging**. I retain the loop but lose the actual research question and technical contribution.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 7.** It needs one concrete candidate, predicted performance plus uncertainty, and the next selected batch, not abstract counts around a black box.\n- **Hidden-slide decision:** **Unhide neither slide 3 nor slide 5 as written.** If forced to choose one, **keep slide 5 hidden** and integrate its one-sentence concept into a redesigned slide 7. Generic explanation would add time without restoring the missing optimization details.\n\n---\n\n# P5. The finite-element-analysis veteran\n\n## (a) My reaction in the room\n\nThe opening is fine until slide 4 implies that physical iteration is the central route around slow design. I start waiting for the argument about simulation. Because slide 6 is hidden, the deck never states the agreed, defensible position: a sufficiently calibrated model may cost more than it saves for this campaign, particularly when interfaces, defects, rate dependence, contact, and millisecond shock transients dominate.\n\nThat omission avoids an overt attack on finite-element analysis (FEA), which is good, but it also creates a logical hole. Why is physical BO the chosen source of objective values? Why not a hybrid strategy, reduced-order model, analytical screening, or multi-fidelity optimization? The deck merely proceeds from \u201cdesign is slow\u201d to \u201cBO makes experimentation affordable.\u201d\n\nI object to any suggestion that experimental data are automatically \u201creal\u201d and simulations are not. Measurements also involve sensor bandwidth, filtering, fixture dynamics, uncertainty, and model-based transformations. Slide 9\u2019s \u201caccelerometers gather real data\u201d wording is particularly vulnerable. A force estimate derived from acceleration still rests on calibration and assumptions.\n\nSlide 10 is a useful experimental workflow. It should be presented as a scoped engineering choice, not a universal replacement for simulation. Slide 12\u2019s transfer claim needs the condition from the outline: use this pattern where tests are authoritative, costly, the design space is mixed, and goals conflict.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey bypass a calibrated finite-element model and optimize directly against impact tests because the printed interfaces and transient response are difficult to model economically.\u201d\n\nThat is close to the intended experiment-first rationale, but I had to infer it; the visible slides do not say it clearly.\n\n## (c) My top three Q&A questions\n\n1. What evidence shows that calibrating a useful simulation would cost more than it saves for this campaign, rather than merely being inconvenient?\n2. How do you verify the measured peak transmitted force, including sensor bandwidth, fixture dynamics, filtering, and uncertainty?\n3. Why exclude analytical or low-fidelity screening from a multi-fidelity BO strategy, especially for infeasible geometries and gross structural trends?\n\n## (d) My most pointed objection\n\n**The deck uses experiment-first optimization without stating a scoped cost-and-validity argument or acknowledging where simulation could still add value.**\n\nUnanswered, this is **highly damaging in Q&A**. It makes the methodology look ideologically anti-simulation rather than economically reasoned.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 4.** It should become the two-sided decision: exhaustive physical search is too costly, while campaign-grade calibration for the hardest responses is also costly; therefore choose physical tests adaptively.\n- **Hidden-slide decision:** **Merge slide 6 into slide 4**, rather than unhide it as written. Keep a fuller evidence-backed simulation/experiment comparison as backup.\n\n---\n\n# P6. The friendly industry generalist\n\n## (a) My reaction in the room\n\nThe opening video is memorable, and I like the practical goal of shortening build\u2013test iteration. Slide 4 is easy to understand. My attention slips on slide 7 because the counts and black-box symbol do not tell a story. I do not need the name qNEHVI, but I need to know that the software balances trying promising designs with learning from uncertain ones and respects a force limit.\n\nSlides 8\u201310 are the strongest run. If the presenter shows one labeled specimen, one clean drop clip, and one loop diagram, I can see how I might use the pattern in another product-development problem. I want a cycle-time number and an indication of what software or human work is required.\n\nSlide 12 dilutes the message by offering several destinations before proving the present result. The ending is weak. A blank Q&A slide discards the chance to leave the audience with the workflow, the best specimen, and a measured improvement.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cPrint it, break or drop it, feed the measurements back, and let the optimizer pick the next batch instead of running a huge test matrix.\u201d\n\nThat captures the intended practical pattern, but I would probably overgeneralize it beyond the specific conditions under which it is useful.\n\n## (c) My top three Q&A questions\n\n1. How many prints and calendar days did it take to reach a useful design compared with your previous workflow?\n2. What parts of this system could another engineering team reuse without building the same drop tower or writing a custom optimizer?\n3. What happens when a print fails or a test produces an outlier?\n\n## (d) My most pointed objection\n\n**I understand the loop, but the deck never gives me the one number or result that proves it was worth building.**\n\nUnanswered, this is **moderately to highly damaging**. I would remember the concept but not have a reason to adopt or sponsor it.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 13.** Replace the blank Q&A screen with the best measured result, the closed-loop diagram, and one deployment rule; retain a small \u201cQuestions?\u201d label.\n- **Hidden-slide decision:** **Unhide slide 11** once populated. Results are more valuable than another explanatory slide.\n\n---\n\n# Synthesis\n\n## Cross-persona themes\n\n1. **The scope boundary is missing.** The Super Ball Bot is a good hook, but every persona risks interpreting a PLA\u2013TPU proxy as a claim about flight hardware.\n2. **Slide 7 is the weakest visible technical slide.** It serves neither experts nor novices. Counts such as \u201c6 variables\u201d and \u201c2 objectives\u201d are information, not a message.\n3. **The main claim currently lacks evidence.** \u201cSignificantly accelerate,\u201d \u201csupplies data fast,\u201d and \u201cdozens, not hundreds\u201d require a campaign ledger and a budget-matched comparison.\n4. **The results omission breaks the story.** The deck follows the outline through the workflow, then skips the outline\u2019s largest time block and moves directly to future work.\n5. **The physical system needs more definition.** The audience needs a labeled specimen, a clear explanation of \u201ctensegrity-inspired,\u201d interface design, remaining manual steps, and the measurement chain.\n6. **Noise must be operational rather than rhetorical.** Saying the data are noisy is insufficient. Replicates, periodic controls, drift, failures, and uncertainty should be shown.\n7. **Experiment-first needs a scoped rationale.** The deck should not dismiss FEA, but it should explain why adaptive physical testing is the economical source of objective values for this campaign.\n8. **The close is not a conclusion.** Future applications and a blank Q&A slide do not substitute for the measured finding and its conditions of transfer.\n9. **Media are a communication and schedule risk.** Short clips can work, but multiple looping videos violate the signal-to-noise principle and create failure modes.\n10. **The project specification needs reconciliation.** The supplied materials differ on six versus seven variables, whether FDM process parameters are optimized, and whether peak force is a constraint or part of the Pareto trade-off.\n\n## Five highest-priority slide-level revisions\n\n### 1. Slide 11: replace one empty slot with a three-slide quantitative results sequence\n\nUse full-sentence titles tied to observed data:\n\n- **11A:** \u201cThe campaign used **N** specimens in **B** adaptive batches, including **R** replicates and **F** failed prints.\u201d\n- **11B:** \u201cAt the same physical-test budget, adaptive selection reached **[measured feasible-performance result]** faster than **[predeclared Sobol/random baseline]**.\u201d\n- **11C:** \u201cThe measured feasible Pareto front trades specific energy absorption against compaction efficiency while respecting a **[value and unit]** peak-force cap.\u201d\n\nShow uncertainty, replicate spread, infeasible points, and representative specimen photographs or traces. If the campaign cannot support the baseline claim, change the talk\u2019s main message to: **\u201cWe evaluate whether noise-aware multi-objective Bayesian optimization can reduce the physical-test budget.\u201d**\n\n### 2. Slide 7: state the actual constrained optimization problem\n\nReplace the current black-box/count design with:\n\n- the named decision variables and their types;\n- a small batch entering the printer/test loop;\n- objectives: maximize specific energy absorption and compaction efficiency;\n- constraint: peak transmitted force below a stated cap;\n- qNEHVI\u2019s role in one plain sentence: it chooses a small next batch likely to expand the feasible trade-off frontier while accounting for uncertainty and noisy tests;\n- the declared test budget, initialization size, and batch size.\n\nResolve six versus seven variables and whether processing parameters are decision variables before finalizing this slide.\n\n### 3. Slide 2: put the proxy boundary beside the hook\n\nKeep the Super Ball Bot clip, but add an image of the actual specimen and the visible sentence:\n\n> **\u201cHere, a printed PLA\u2013TPU T3 prism is a proxy for developing the workflow, not flight hardware.\u201d**\n\nChange or qualify \u201crobust, reusable solutions\u201d unless the present campaign includes repeat-impact evidence.\n\n### 4. Slide 4: turn the vague need into the two-sided engineering decision\n\nRetitle it:\n\n> **\u201cBecause exhaustive testing and campaign-grade model calibration are both costly, each physical specimen must be chosen for its information value.\u201d**\n\nAdd one quantified physical-search estimate and one scoped calibration challenge. Do not say simulation is untrustworthy. State that FEA and lower-fidelity models remain possible screening or future multi-fidelity tools.\n\n### 5. Slides 10 and 13: separate the demonstrated loop from the conclusion\n\n- **Slide 10:** retitle to **\u201cThe closed loop turns each measured test into the next small batch of specimens.\u201d** Remove \u201csignificantly accelerate\u201d until the results demonstrate it. Mark automated and manual stages.\n- **Slide 13:** retain the closed-loop thumbnail, add the best quantitative result, and state the deployment rule: **use the loop when tests are authoritative, each test is costly, the design space is mixed, and performance goals conflict.** Add \u201cQuestions?\u201d without clearing the conclusion from the screen.\n\n## Hidden-slide adjudication\n\n| Hidden slide | Recommendation | Rationale |\n|---|---|---|\n| **3: tensegrity usefulness/mechanism** | **Merge into slide 8** | The generic elbow and squishing GIFs add noise and risk an inaccurate mechanism; an annotated actual specimen and deformation sequence are stronger. |\n| **5: generic BO explanation** | **Merge into slide 7; keep original as backup or delete** | DAC experts do not need a generic advocacy slide, but novices need one plain-language sentence and a concrete acquisition visual. |\n| **6: specimen information value/experiment-first** | **Merge into slide 4; keep evidence-rich version as backup** | The argument is structurally necessary, but the current title is vague and the visual does not support the scoped calibration-versus-campaign-cost decision. |\n| **11: results** | **Unhide and expand to three slides** | Results are the evidentiary center of the talk and were allocated five minutes in the agreed outline. An empty or hidden results slot makes the main claim untenable. |\n\n## Visible-slide claims needing evidence, hedging, or backup\n\n| Slide | Claim | Required treatment |\n|---|---|---|\n| **1** | \u201cBuild better tensegrity structures faster\u201d | Define \u201cbetter\u201d with the two objectives and force constraint; support \u201cfaster\u201d with the results comparison. |\n| **2** | Tensegrity is robust and reusable for planetary landings | Cite the Super Ball Bot source; distinguish concept evidence from this study; provide repeat-impact evidence or hedge reusability. |\n| **2** | Parachutes and retrorockets struggle in the stated conditions | Cite an authoritative aerospace source if retained in narration; avoid implying universal inadequacy. |\n| **4** | Current tensegrity design is slow and resource intensive | Quantify candidate count, print/test time, hand-assembly burden, or calibration effort. |\n| **7** | BO makes experiments affordable with limited, noisy data | Show campaign budget, noise characterization, replicates, baseline, and performance versus tests. Prepare qNEHVI and Gaussian-process details in backup. |\n| **7** | Six variables, two input-data types, two objectives | Reconcile with the apparent seven-variable abstract specification and identify every quantity. Clarify \u201cinput-data types.\u201d |\n| **8** | Multi-material AM supplies data fast | Report print-to-result cycle time and failure rate. |\n| **8** | Single-build co-fabrication with no joining | Show interface and post-processing; use \u201cno joining of PLA and TPU members\u201d if other manual operations remain. |\n| **9** | Accelerometers gather the relevant impact data | Show sensor locations and the derivation of transmitted force, specific energy absorption, and compaction efficiency. Reconcile this wording with the abstract\u2019s instrumented-tup description. |\n| **10** | The loop significantly accelerates optimization | Remove \u201csignificantly\u201d unless a defined baseline and uncertainty support it. Statistical significance should not be implied casually. |\n| **12** | Workflow will transfer to lattices and other applications | Present as conditional future work with transfer criteria, not an achieved result. Attribute any external crutch-tip image and claim. |\n\n### Prepared backup slides\n\n1. Full design-variable table, bounds, encoding, and fixed versus optimized process parameters.\n2. qNEHVI implementation, Gaussian-process assumptions, constraint treatment, batch size, and acquisition settings.\n3. Noise plan: replicates, periodic control, heteroscedasticity, drift, outlier policy, and uncertainty propagation.\n4. Budget-matched baseline definition and evaluation metric.\n5. Force-cap rationale and full objective definitions.\n6. Sensor chain, calibration, sample rate, filtering, fixture dynamics, and metric extraction.\n7. PLA\u2013TPU interface, conditioning, build layout, dimensional/mass deviations, and print-failure modes.\n8. Scoped FEA rationale with citations or internal validation; possible analytical or multi-fidelity extensions.\n9. Automation map and cycle-time breakdown.\n10. Repeated-impact behavior if reusability remains in the opening.\n\n## Merged, ordered presenter TODO list\n\n### Must fix before the talk\n\n1. **Freeze the scientific specification.** Reconcile the number and type of decision variables, whether FDM parameters are optimized or controlled, the two objectives, the force constraint, treatment of print failures, and the measurement chain. Use one formulation across slides, abstract, paper, and Q&A.\n2. **Set a results completion plan.** Establish dates and owners for campaign completion, repeat tests, data quality review, baseline analysis, and slide freeze. Predeclare the fallback claim if the baseline does not support \u201cdozens rather than hundreds.\u201d\n3. **Build the three-slide results sequence.** Campaign ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front. Allocate approximately five minutes, as the agreed outline requires.\n4. **Add the proxy statement to slide 2.** State plainly that the PLA\u2013TPU T3 specimen is a workflow proxy and not flight hardware.\n5. **Redesign slide 7.** Show the exact constrained multi-objective problem, test budget, small batches, uncertainty, noisy observations, and the role of qNEHVI.\n6. **Remove unsupported result language.** Until supported, delete \u201csignificantly\u201d from slide 10 and avoid \u201cdozens rather than hundreds\u201d in the title, narration, or close.\n7. **Clarify experiment-first positioning.** Merge the information-value argument into slide 4 and prepare an evidence-backed FEA backup slide.\n8. **Make the experiment auditable.** On slide 9 or backup, reconcile accelerometers and the instrumented tup; show how all three performance metrics are calculated and how uncertainty is handled.\n9. **De-risk every video.** Store local trimmed files, disable distracting loops, test them on presentation hardware, and include static fallback frames.\n10. **Rehearse to 13:30\u201314:00.** Protect the results and conclusion from being cut. Practice once without slides, consistent with Doumont\u2019s preparation advice.\n\n### Should fix\n\n11. **Replace slide 8\u2019s generic print emphasis with mechanism and materials evidence.** Label PLA, TPU, interface strategy, prestress status, deformation, post-processing, and failure modes.\n12. **Quantify slide 4.** Give at least one campaign-relevant cost, duration, or candidate-count estimate.\n13. **Show automation boundaries on slide 10.** Candidate selection and surrogate updating may be automated; slicing, handling, test setup, and testing remain manual according to the abstract.\n14. **Convert slide 12 to a gated roadmap.** Separate demonstrated T3-proxy results from future lattices, flight materials, and terrestrial applications.\n15. **Turn slide 13 into the conclusion.** Leave the audience with the best result and transfer rule while taking questions.\n16. **Prepare technical backup slides.** Prioritize baseline fairness, noise, force-cap rationale, print failures, materials repeatability, and FEA positioning.\n\n### Polish\n\n17. Replace the slide 1 title with a specific message, such as **\u201cPhysical Bayesian optimization searches multi-material tensegrity absorbers one small test batch at a time.\u201d**\n18. Left-align titles and use intentional line breaks; check every slide at six-per-page scale.\n19. Use one purposeful visual per slide. Avoid decorative loading or elbow animations.\n20. Standardize terminology: \u201cBayesian optimization,\u201d not \u201cBays Opt\u201d; \u201ctensegrity-inspired\u201d where appropriate; define specific energy absorption once.\n21. Add citations directly beside borrowed footage, images, and quantitative claims.\n\n## Who the deck serves best and worst\n\n- **Best served:** **P6, the friendly industry generalist.** The visible sequence is visually intuitive, practical, and easy to reduce to \u201cprint \u2192 test \u2192 learn \u2192 repeat.\u201d\n- **Worst served:** **P1, the skeptical BO insider.** The current deck omits nearly every item needed to judge a DAC-10 contribution: exact problem formulation, budget, baseline, noise model, constraint handling, and empirical adaptive advantage.\n\nThat is **not the right trade-off for DAC-10**. The deck should remain accessible to P4 and P6, but its technical center must satisfy P1 without turning into a BO lecture. One rigorous optimization slide, three quantitative results slides, and targeted backup material would achieve that balance.\n\n## Predicted reception\n\n- **As is: 4/10.** The physical workflow and application are memorable, but the absent scope statement, empty results section, vague BO slide, and unsupported acceleration claim leave the DAC contribution unproven.\n- **After the must-fixes: 8/10.** A scoped proxy claim, reconciled methods, fair budget-matched comparison, feasible Pareto front, and measured conclusion would turn the same visual story into a credible design-optimization application talk.\n\n## Discretionary analytical decisions\n\n- Evaluated only the visible slide order for the simulated in-room reactions, while using hidden slides and presenter notes to judge revision options.\n- Treated planned placeholder visuals as if implemented according to the presenter\u2019s notes, but judged the selected visual concept and associated stage risk rather than absent graphic quality.\n- Prioritized the Draft 3 outline over conflicting Draft 1 wording because the prompt identifies it as the agreed story arc, scope framing, results plan, and timing plan.\n- Rated objections qualitatively as moderate, high, or critical rather than assigning unsupported numerical probabilities.\n- Recommended three results slides rather than preserving one reserved slot because the agreed outline specifies three distinct evidentiary products and allocates five minutes to results.\n- Recommended merging hidden slides 3, 5, and 6 rather than simply unhiding them because their necessary ideas can be conveyed with higher signal-to-noise on existing slides.\n- Treated the six-versus-seven-variable and optimized-versus-fixed-process-parameter differences as unresolved specification conflicts requiring reconciliation, rather than inferring which document is correct.\n- Assessed claims against the supplied deck extraction, Draft 3 outline, Doumont notes, and submitted abstract only; no independent validation of cited literature or unseen experimental data was attempted.", + "name": "submit_answer", + "tool_call_id": "call_8In1tJq32AD2tTyHX3tmIlLf", + "info": { + "start_ts": 8302.413890092, + "end_ts": 8302.416029592 + } + } + ], + "reward": 0.0, + "truncated": false, + "done": true, + "value": 0.0, + "metadata": { + "callback_type": "after_env_step" + }, + "action": { + "call_id": { + "run_id": "4566fd2d-f49d-4655-ac55-692f306b1db0", + "fwd_id": "f6b2ecbb-a1f2-438d-9b88-1a216eb20c49" + }, + "op_name": "IdentityOp-3b7eacd87007", + "op_class_name": "ldp.graph.common_ops.IdentityOp", + "value": { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8In1tJq32AD2tTyHX3tmIlLf", + "type": "function", + "function": { + "arguments": "{\"answer\": \"# Mock audience report: IDETC-CIE 2026 slide deck, Draft 1\\n\\n## Executive read\\n\\nThe visible deck has a clear visual spine: **motivation \\u2192 slow iteration \\u2192 Bayesian optimization \\u2192 multi-material printing \\u2192 impact testing \\u2192 closed loop \\u2192 future applications**. The strongest section is slides 8\\u201310, where fabrication, testing, and adaptive selection finally appear as one workflow.\\n\\nBut Draft 1 does not yet deliver the technical case promised by the agreed outline. The visible deck omits four things a DAC-10 audience will need:\\n\\n1. **Honest scope:** no visible slide says that the PLA\\u2013TPU T3 prism is a proxy system rather than flight hardware.\\n2. **A precise optimization problem:** the six variables, two objectives, force constraint, noise treatment, and fabrication failures are not intelligible on slide 7.\\n3. **Quantitative evidence:** the sole results slot is hidden and empty, yet slides 10 and 12 already claim acceleration and transfer.\\n4. **A measured conclusion:** the talk jumps from the workflow to speculative applications without showing a campaign ledger, comparison against a budget-matched baseline, or feasible Pareto front.\\n\\nThe slide titles generally follow Doumont\\u2019s full-sentence-message rule, but several messages outrun the evidence. The deck also departs substantially from the Draft 3 timing plan, which reserves **5 of 15 minutes for results**. As built, results receive zero visible slides.\\n\\n---\\n\\n# P0. The program manager in the audience\\n\\n## (a) My reaction in the room\\n\\nSlide 1 tells me the team wants to work faster, but it does not tell me what is being optimized or what has actually been built. The title sounds aspirational rather than traceable to a project deliverable. I immediately want the formal title from the abstract or a shorter version of it.\\n\\nThe Super Ball Bot footage on slide 2 is an effective hook. I understand why reusable impact attenuation matters. But the slide creates a scope risk: I am looking at planetary landing hardware while the actual work is a printed PLA\\u2013TPU T3 proxy. Because that distinction is not stated immediately, I may spend the rest of the talk evaluating the work against flight-readiness criteria the project never intended to meet.\\n\\nSlide 4 gives me the business problem, but not a baseline. \\u201cSlow and resource intensive\\u201d needs a number: hours per print-test cycle, number of factors, or the candidate count under a declared design of experiments. Without that, I cannot assess whether the proposed loop attacks the dominant schedule driver.\\n\\nSlide 7 is where I expect the project definition. Instead, \\u201c6 variables,\\u201d \\u201c2 types of input data,\\u201d and \\u201c2 objectives\\u201d read like inventory labels. I cannot reconstruct the statement of work from them. I need to see what is automated, what remains manual, what the force cap is, and what constitutes a successful campaign.\\n\\nSlides 8 and 9 make the project tangible. A labeled specimen and an actual drop test would build confidence. Yet \\u201csingle-build\\u201d and \\u201cno joining\\u201d need a process qualification caveat, and I need the measurement chain to be explicit. The abstract refers to an instrumented tup and force\\u2013time data; slide 9 says accelerometers. Those may be compatible, but the deck presently leaves the relationship unclear.\\n\\nSlide 10 is the right management view of the system, but \\u201csignificantly accelerate\\u201d is a result claim placed on a workflow diagram. At this point I ask, \\u201cCompared with what, by how much, and over how many specimens?\\u201d Then the deck skips the hidden results slide and goes directly to future applications. That is the point where my confidence drops sharply. The project is asking me to extrapolate before closing the evidence loop.\\n\\nSlide 12 is too early and too broad. A crutch tip and lattice can be legitimate next phases, but only after the team shows what the present campaign established and what technical risks remain. The blank questions slide gives me no durable take-home message.\\n\\n### PM stage-risk register\\n\\n| Risk | Likelihood | Consequence | Mitigation |\\n|---|---:|---:|---|\\n| Results are incomplete by conference lock | Medium to high | Critical: central claim becomes unsupported | Set a dated campaign freeze; predeclare a fallback message and minimum publishable result package |\\n| Embedded Super Ball Bot or drop-test video fails | Medium | Moderate: weak opening or broken pacing | Use local, trimmed files; retain a still-frame fallback on every video slide |\\n| Talk overruns because several videos and explanations are improvised | High | High: results or conclusion gets cut | Script videos to 10\\u201315 seconds total each; rehearse to 13:30; assign 5 minutes to results |\\n| Scope is mistaken for flight-hardware validation | High | High: credibility damage in Q&A | Put \\u201cPLA\\u2013TPU T3 proxy; not flight hardware\\u201d on slide 2 or the next slide |\\n| Acceleration claim lacks a budget-matched comparator | High | Critical for DAC review audience | Show Sobol/random or another predeclared baseline at the same physical-test budget |\\n| Measurement chain is challenged | Medium | High | Add a methods schematic and backup slide on calibration, sampling, filtering, and metric extraction |\\n| \\u201cNo joining\\u201d or \\u201cnear autonomous\\u201d is read as full automation | Medium | Moderate | List manual steps explicitly: slicing, handling, setup, and testing |\\n| Animations distract or fail to communicate without narration | Medium | Moderate | Prefer one short clip plus static annotated frames; follow Doumont\\u2019s signal-to-noise rule |\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey are building a loop that prints PLA\\u2013TPU tensegrity-like absorbers, drop-tests them, and lets Bayesian optimization choose the next print.\\u201d\\n\\nThat retains the basic workflow but loses the intended **dozens rather than hundreds** claim because the deck never demonstrates it. I would also probably describe the work as \\u201clander research,\\u201d which is a distortion caused by the missing proxy-system statement.\\n\\n## (c) My top three Q&A questions\\n\\n1. What is the dated plan to complete the campaign, baseline comparison, and repeat testing before the conference slide freeze?\\n2. Which exact operations are automated today, which remain manual, and what is the measured end-to-end cycle time per adaptive batch?\\n3. What evidence will justify \\u201cdozens rather than hundreds,\\u201d and what will you claim if the budget-matched baseline does not show an advantage?\\n\\n## (d) My most pointed objection\\n\\n**You are presenting a claimed acceleration without showing a completed campaign, a comparator, or even a visible results slide.**\\n\\nUnanswered, this is **critical**. I would not fund a scale-up phase from this deck. I might fund completion of the present campaign if the team showed a disciplined schedule, predefined success criteria, and a credible fallback.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 11.** It must become a real, scheduled results package, preferably three slides: campaign ledger, budget-matched baseline, and feasible Pareto front.\\n- **Hidden-slide decision:** **Unhide slide 11**, but not in its current empty form. The talk cannot make its principal claim without it.\\n\\n### PM slide-level punch list, ordered by schedule criticality\\n\\n**Must be fixed first**\\n\\n1. **Slide 11/results sequence:** assign owners and dates for data freeze, quality control, baseline computation, replicate analysis, and final plots. Define fallback wording now.\\n2. **Slide 2:** add the explicit scope line, \\u201cThis study uses a printed PLA\\u2013TPU T3 prism as a proxy for developing the workflow; it is not flight hardware.\\u201d\\n3. **Slide 7:** replace category counts with the actual optimization problem, test budget, batch size, objectives, constraint, and noise/failure treatment.\\n4. **Slide 10:** remove \\u201csignificantly\\u201d until slide 11 demonstrates a quantitative improvement; show automation boundaries and the loop\\u2019s measured cycle time.\\n5. **Slides 2 and 9:** download and trim media, test on the conference laptop, and put a static fallback image on each slide.\\n\\n**Then fix**\\n\\n6. **Slide 4:** quantify the current cost or candidate count and identify the bottleneck.\\n7. **Slide 8:** label PLA, TPU, interface, and any post-processing; qualify \\u201cno joining.\\u201d\\n8. **Slide 9:** reconcile accelerometers with the instrumented-tup description and show how force, energy absorption, and compaction efficiency are obtained.\\n9. **Slide 12:** convert the application collage into a conditional next-phase roadmap with gates.\\n10. **Slide 13:** end on the measured result and deployment rule, not a blank Q&A screen.\\n\\n**Nice to have**\\n\\n11. Replace decorative or looping media on slide 4 with one static process image unless it communicates a measured delay.\\n12. Add backup slides on Bayesian optimization implementation, finite-element-analysis positioning, repeatability, force-cap rationale, and print failures.\\n\\n---\\n\\n# P1. The skeptical Bayesian-optimization insider\\n\\n## (a) My reaction in the room\\n\\nThe opening is competent but generic for a design-automation conference. Slides 1\\u20134 set up an expensive physical experiment, which is a valid application class for Bayesian optimization (BO). I become interested when the presenter reaches slide 7, because that is where methodological seriousness should appear.\\n\\nInstead, slide 7 says only \\u201c6 variables,\\u201d \\u201c2 types of input data,\\u201d and \\u201c2 objectives.\\u201d I do not learn the variables, their types, how categorical choices are encoded, whether process parameters are optimized, how noisy observations enter the Gaussian processes, how replicates identify noise, how failures are modeled, or how the force cap enters the acquisition. The title says BO handles noisy data, but the visual does not substantiate that message.\\n\\nThere is also an unresolved specification conflict across the supplied materials. The agreed outline says the study jointly optimizes T3 geometry and FDM processing parameters. The abstract\\u2019s design vector lists four continuous geometric variables, one integer, and two categorical variables, apparently **seven variables**, while its fabrication section says print parameters are held fixed within a batch. The slide says **six variables**. A DAC audience will notice if the spoken details do not reconcile these versions.\\n\\nSlides 8\\u201310 explain why physical feedback is useful, but they do not establish why qNEHVI is the right algorithm or whether adaptive selection helped. The visible deck never names qNEHVI, the feasible Pareto front, the peak-force constraint, batch size, initialization, or test budget. Hiding slide 5 is reasonable if it is only a generic \\u201cwhat is BO?\\u201d slide copied from another talk. But slide 7 must then carry the application-specific methodological content.\\n\\nWhen slide 10 says the workflow \\u201csignificantly accelerate[s]\\u201d optimization, I wait for a performance-versus-budget curve. It never arrives. Slide 12 then generalizes to other applications. My default conclusion is exactly the skeptical one posed in the prompt: this appears to be off-the-shelf qNEHVI attached to an interesting physical apparatus, with no demonstrated algorithmic or empirical advantage yet.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cIt was an experimental self-driving-lab-style application of batch multi-objective BO to printed impact absorbers, but the talk did not show whether BO beat space-filling sampling.\\u201d\\n\\nThat differs from the intended message because I would not repeat \\u201cdozens, not hundreds\\u201d without a budget-matched baseline.\\n\\n## (c) My top three Q&A questions\\n\\n1. What are the exact decision variables and encodings, and why do the deck, scope statement, and abstract appear to disagree on whether there are six or seven variables and whether FDM parameters are optimized?\\n2. How is observation noise estimated: fixed noise, learned heteroscedastic noise, technical replicates, or repeated control specimens? What is the replicate allocation?\\n3. Against what budget-matched baseline do you evaluate qNEHVI, and what metric do you report: feasible hypervolume, best feasible objective values, or attainment probability versus physical tests?\\n\\n## (d) My most pointed objection\\n\\n**Nothing visible distinguishes a validated BO contribution from an untested application of standard qNEHVI.**\\n\\nUnanswered, this is **critical for DAC-10 reception**. Application novelty can be enough, but only if the physical campaign is rigorous and the adaptive method is compared fairly under the same test budget.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 7.** Replace the black-box inventory with a visual of posterior mean plus uncertainty \\u2192 constrained batch acquisition \\u2192 next physical tests, accompanied by the exact objectives and force constraint.\\n- **Hidden-slide decision:** **Keep slide 5 hidden** as a standalone generic BO tutorial. Merge one plain-language BO sentence into slide 7 and put qNEHVI details in backup.\\n\\n---\\n\\n# P2. The aerospace practitioner\\n\\n## (a) My reaction in the room\\n\\nThe Super Ball Bot hook works because I know the lineage. Reusable landing structures are interesting. But the title\\u2019s claim that tensegrity provides \\u201crobust, reusable solutions\\u201d is broader than what this deck demonstrates. A lab drop of a polymer proxy is not evidence of robustness in thermal extremes, vacuum, dust, radiation, repeated impacts, or off-axis terrain contact.\\n\\nThe deck never immediately tells me that the actual specimen is **tensegrity-inspired**, lacks ideal compression-only and tension-only membership, and is not intended to fly. That makes slide 2 vulnerable to looking like borrowed aerospace relevance.\\n\\nSlide 4 is plausible but too broad. Tensegrity design can be difficult, but aerospace development is not made slow only by design-space search. Verification, environmental qualification, uncertainty, packaging, guidance, and system integration matter. A faster specimen loop is useful, but it is one subsystem tool.\\n\\nI am not helped much by slide 7. I need BO translated into engineering behavior: it chooses the next small batch based on predicted performance and uncertainty while rejecting designs expected to violate a payload-force limit. The slide should name that limit and show the actual trade-off.\\n\\nSlides 8 and 9 are the first ones that answer \\u201cwhat did you build and how did you hit it?\\u201d A labeled specimen, deformation sequence, drop mass, speed or energy, orientation, and force measurement would hold my attention. Two simultaneous looping videos may not. I also want to know whether the structure rebounds, survives repeated drops, and retains its response, because the opening emphasizes reuse.\\n\\nSlide 10 is understandable. Slide 12 loses me because it jumps from a T3 polymer coupon to lattices and crutch tips without stating transfer conditions or technology gates.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey use an optimizer to choose which 3D-printed soft\\u2013rigid impact absorber to drop-test next, with the eventual motivation of reusable landing structures.\\u201d\\n\\nThat is fairly close, but I would deliberately downgrade \\u201ctensegrity\\u201d to \\u201ctensegrity-inspired absorber\\u201d and treat planetary landing as motivation only.\\n\\n## (c) My top three Q&A questions\\n\\n1. What makes the printed T3 specimen mechanically tensegrity-inspired rather than an ordinary compliant crush structure, and is it actually prestressed?\\n2. What impact energy, mass, orientation, and transmitted-force cap are used, and how do they relate to a payload-protection requirement?\\n3. How does performance change over repeated impacts, and what failure modes or permanent set limit the claimed reusability?\\n\\n## (d) My most pointed objection\\n\\n**The opening borrows flight relevance and reusability, but the visible deck never establishes the proxy boundary or shows repeated-impact evidence.**\\n\\nUnanswered, this is **highly damaging** to credibility with aerospace practitioners, though it does not invalidate the optimization workflow itself.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 2.** Keep the hook, but place a photograph of the actual T3 specimen beside the Super Ball Bot and state \\u201cmotivation, not demonstrated flight readiness.\\u201d\\n- **Hidden-slide decision:** **Keep slide 3 hidden.** A generic elbow-friction GIF would distract and oversimplify. Merge a precise tensegrity-mechanism annotation into slide 8 instead.\\n\\n---\\n\\n# P3. The additive-manufacturing/materials researcher\\n\\n## (a) My reaction in the room\\n\\nSlides 1\\u20134 give me a recognizable motivation for rapid experimental iteration. My interest rises on slide 8 because multi-material fused deposition modeling is where the claimed loop can succeed or fail.\\n\\nThe message \\u201csingle-build co-fabrication, with no joining of parts\\u201d is attractive, but it needs exact boundaries. Is there support removal, trimming, conditioning, manual tensioning, or fixture insertion? Does \\u201csingle build\\u201d mean one tool-change sequence on one machine, or truly co-deposited interfaces throughout the architecture? Are PLA and thermoplastic polyurethane (TPU) mechanically interlocked, chemically bonded, or merely in contact?\\n\\nThe abstract provides more useful information than the slide: a core-wrapping architecture is intended to resist delamination, specimens are weighed and dimensionally inspected, and a control is retested to monitor drift. Those are exactly the details the talk should surface. Slide 8\\u2019s planned printing video will not replace a labeled cross-section or interface image.\\n\\nSlide 9 raises repeatability questions. TPU is rate-, temperature-, moisture-, and history-dependent. \\u201cLimited, noisy data\\u201d on slide 7 acknowledges noise but does not show the control plan. I need batch identity, drying/conditioning, build position, print orientation, machine state, interface failures, and replicates.\\n\\nThe deck also appears inconsistent about process parameters. The agreed scope says FDM processing parameters are optimized with structure, but the abstract says temperature, line width, infill, and retraction are held fixed within a batch. This could mean they vary between batches or are controlled rather than optimized, but the deck must say which.\\n\\nI like the physical loop on slide 10. I do not yet accept that multi-material printing \\u201csupplies data fast\\u201d until cycle time and failure rate are shown.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey co-print PLA and TPU impact specimens and use each mechanical test to guide the next design batch.\\u201d\\n\\nI would probably omit \\u201cno joining\\u201d and \\u201cdozens rather than hundreds\\u201d until I saw interface qualification, failure accounting, and campaign data.\\n\\n## (c) My top three Q&A questions\\n\\n1. How is the PLA\\u2013TPU interface designed and qualified, and what fraction of specimens fail by delamination or print defects?\\n2. Which material, machine, conditioning, and build-position variables are controlled, measured as covariates, or optimized?\\n3. How many true replicates and repeated control specimens are used to separate BO improvement from printer drift and TPU batch variation?\\n\\n## (d) My most pointed objection\\n\\n**The deck treats co-fabrication as an enabling fact without showing interface integrity, repeatability, or the remaining manual processing steps.**\\n\\nUnanswered, this is **highly damaging** because print variation is part of both the physical mechanism and the statistical noise model.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 8.** Replace or supplement the printing clip with an annotated specimen and interface detail: PLA struts, continuous TPU network, deposition/wrapping strategy, prestress status, post-processing, and observed failure modes.\\n- **Hidden-slide decision:** **Keep slide 3 hidden.** Its proposed generic GIFs do not answer the materials questions. Put the relevant deformation mechanism on slide 8.\\n\\n---\\n\\n# P4. The first-year graduate student\\n\\n## (a) My reaction in the room\\n\\nSlide 1 sounds positive, and slide 2 gives me a memorable reason to care. I understand that the lander survives an impact because the structure itself deforms. But I do not yet know what \\u201ctensegrity\\u201d means, and the hidden slide 3 would not necessarily fix that: an elbow GIF and a squishing GIF could leave me with an inaccurate friction-versus-elasticity story.\\n\\nSlide 4 is easy to follow. The three words \\u201cIteration, Building, Testing\\u201d form a simple problem statement, although the images need to make their relationship obvious.\\n\\nI get lost on slide 7. Six variables of what? What are the two input-data types? Are the two objectives \\u201cabsorb energy\\u201d and \\u201creduce force,\\u201d or something else? I do not know what a black box means in this setting. The title says Bayesian optimization works with noisy data, but I have not been shown how it decides anything.\\n\\nSlide 8 helps because I can see printing. Still, I need labels that distinguish the rigid and flexible parts and one sentence explaining why the object is \\u201ctensegrity-inspired.\\u201d Slide 9 is intuitive if the clips are synchronized and annotated. I would understand even better if the slide drew the metric extraction directly on a force\\u2013time or force\\u2013displacement trace.\\n\\nSlide 10 is the clearest slide in the deck. Printing \\u2192 testing \\u2192 optimizer \\u2192 next print is the idea I will remember. But it arrives after the technical terms rather than before them. I would benefit from seeing a version of this loop before slide 7 and then revisiting it after the methods.\\n\\nSlide 12 feels like a new presentation. I have not seen whether the current system worked, so the crutch tip and lattice do not feel earned. Then the talk ends on a blank question slide rather than telling me the answer.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey repeatedly print and drop-test flexible 3D structures, and a machine-learning model decides what to print next.\\u201d\\n\\nMy retelling loses the two objectives, the force constraint, and the \\u201cdozens rather than hundreds\\u201d quantitative point. I might incorrectly call the method generic machine learning rather than Bayesian optimization.\\n\\n## (c) My top three Q&A questions\\n\\n1. What exactly is tensegrity, and which parts of your printed specimen are in tension and compression?\\n2. What are the six things the optimizer can change, and what are the two scores it is trying to improve?\\n3. How does the optimizer know whether to try something uncertain rather than the design it currently thinks is best?\\n\\n## (d) My most pointed objection\\n\\n**The deck names the ingredients before giving me a plain-language map of how they work together.**\\n\\nUnanswered, this is **moderately damaging**. I retain the loop but lose the actual research question and technical contribution.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 7.** It needs one concrete candidate, predicted performance plus uncertainty, and the next selected batch, not abstract counts around a black box.\\n- **Hidden-slide decision:** **Unhide neither slide 3 nor slide 5 as written.** If forced to choose one, **keep slide 5 hidden** and integrate its one-sentence concept into a redesigned slide 7. Generic explanation would add time without restoring the missing optimization details.\\n\\n---\\n\\n# P5. The finite-element-analysis veteran\\n\\n## (a) My reaction in the room\\n\\nThe opening is fine until slide 4 implies that physical iteration is the central route around slow design. I start waiting for the argument about simulation. Because slide 6 is hidden, the deck never states the agreed, defensible position: a sufficiently calibrated model may cost more than it saves for this campaign, particularly when interfaces, defects, rate dependence, contact, and millisecond shock transients dominate.\\n\\nThat omission avoids an overt attack on finite-element analysis (FEA), which is good, but it also creates a logical hole. Why is physical BO the chosen source of objective values? Why not a hybrid strategy, reduced-order model, analytical screening, or multi-fidelity optimization? The deck merely proceeds from \\u201cdesign is slow\\u201d to \\u201cBO makes experimentation affordable.\\u201d\\n\\nI object to any suggestion that experimental data are automatically \\u201creal\\u201d and simulations are not. Measurements also involve sensor bandwidth, filtering, fixture dynamics, uncertainty, and model-based transformations. Slide 9\\u2019s \\u201caccelerometers gather real data\\u201d wording is particularly vulnerable. A force estimate derived from acceleration still rests on calibration and assumptions.\\n\\nSlide 10 is a useful experimental workflow. It should be presented as a scoped engineering choice, not a universal replacement for simulation. Slide 12\\u2019s transfer claim needs the condition from the outline: use this pattern where tests are authoritative, costly, the design space is mixed, and goals conflict.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey bypass a calibrated finite-element model and optimize directly against impact tests because the printed interfaces and transient response are difficult to model economically.\\u201d\\n\\nThat is close to the intended experiment-first rationale, but I had to infer it; the visible slides do not say it clearly.\\n\\n## (c) My top three Q&A questions\\n\\n1. What evidence shows that calibrating a useful simulation would cost more than it saves for this campaign, rather than merely being inconvenient?\\n2. How do you verify the measured peak transmitted force, including sensor bandwidth, fixture dynamics, filtering, and uncertainty?\\n3. Why exclude analytical or low-fidelity screening from a multi-fidelity BO strategy, especially for infeasible geometries and gross structural trends?\\n\\n## (d) My most pointed objection\\n\\n**The deck uses experiment-first optimization without stating a scoped cost-and-validity argument or acknowledging where simulation could still add value.**\\n\\nUnanswered, this is **highly damaging in Q&A**. It makes the methodology look ideologically anti-simulation rather than economically reasoned.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 4.** It should become the two-sided decision: exhaustive physical search is too costly, while campaign-grade calibration for the hardest responses is also costly; therefore choose physical tests adaptively.\\n- **Hidden-slide decision:** **Merge slide 6 into slide 4**, rather than unhide it as written. Keep a fuller evidence-backed simulation/experiment comparison as backup.\\n\\n---\\n\\n# P6. The friendly industry generalist\\n\\n## (a) My reaction in the room\\n\\nThe opening video is memorable, and I like the practical goal of shortening build\\u2013test iteration. Slide 4 is easy to understand. My attention slips on slide 7 because the counts and black-box symbol do not tell a story. I do not need the name qNEHVI, but I need to know that the software balances trying promising designs with learning from uncertain ones and respects a force limit.\\n\\nSlides 8\\u201310 are the strongest run. If the presenter shows one labeled specimen, one clean drop clip, and one loop diagram, I can see how I might use the pattern in another product-development problem. I want a cycle-time number and an indication of what software or human work is required.\\n\\nSlide 12 dilutes the message by offering several destinations before proving the present result. The ending is weak. A blank Q&A slide discards the chance to leave the audience with the workflow, the best specimen, and a measured improvement.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cPrint it, break or drop it, feed the measurements back, and let the optimizer pick the next batch instead of running a huge test matrix.\\u201d\\n\\nThat captures the intended practical pattern, but I would probably overgeneralize it beyond the specific conditions under which it is useful.\\n\\n## (c) My top three Q&A questions\\n\\n1. How many prints and calendar days did it take to reach a useful design compared with your previous workflow?\\n2. What parts of this system could another engineering team reuse without building the same drop tower or writing a custom optimizer?\\n3. What happens when a print fails or a test produces an outlier?\\n\\n## (d) My most pointed objection\\n\\n**I understand the loop, but the deck never gives me the one number or result that proves it was worth building.**\\n\\nUnanswered, this is **moderately to highly damaging**. I would remember the concept but not have a reason to adopt or sponsor it.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 13.** Replace the blank Q&A screen with the best measured result, the closed-loop diagram, and one deployment rule; retain a small \\u201cQuestions?\\u201d label.\\n- **Hidden-slide decision:** **Unhide slide 11** once populated. Results are more valuable than another explanatory slide.\\n\\n---\\n\\n# Synthesis\\n\\n## Cross-persona themes\\n\\n1. **The scope boundary is missing.** The Super Ball Bot is a good hook, but every persona risks interpreting a PLA\\u2013TPU proxy as a claim about flight hardware.\\n2. **Slide 7 is the weakest visible technical slide.** It serves neither experts nor novices. Counts such as \\u201c6 variables\\u201d and \\u201c2 objectives\\u201d are information, not a message.\\n3. **The main claim currently lacks evidence.** \\u201cSignificantly accelerate,\\u201d \\u201csupplies data fast,\\u201d and \\u201cdozens, not hundreds\\u201d require a campaign ledger and a budget-matched comparison.\\n4. **The results omission breaks the story.** The deck follows the outline through the workflow, then skips the outline\\u2019s largest time block and moves directly to future work.\\n5. **The physical system needs more definition.** The audience needs a labeled specimen, a clear explanation of \\u201ctensegrity-inspired,\\u201d interface design, remaining manual steps, and the measurement chain.\\n6. **Noise must be operational rather than rhetorical.** Saying the data are noisy is insufficient. Replicates, periodic controls, drift, failures, and uncertainty should be shown.\\n7. **Experiment-first needs a scoped rationale.** The deck should not dismiss FEA, but it should explain why adaptive physical testing is the economical source of objective values for this campaign.\\n8. **The close is not a conclusion.** Future applications and a blank Q&A slide do not substitute for the measured finding and its conditions of transfer.\\n9. **Media are a communication and schedule risk.** Short clips can work, but multiple looping videos violate the signal-to-noise principle and create failure modes.\\n10. **The project specification needs reconciliation.** The supplied materials differ on six versus seven variables, whether FDM process parameters are optimized, and whether peak force is a constraint or part of the Pareto trade-off.\\n\\n## Five highest-priority slide-level revisions\\n\\n### 1. Slide 11: replace one empty slot with a three-slide quantitative results sequence\\n\\nUse full-sentence titles tied to observed data:\\n\\n- **11A:** \\u201cThe campaign used **N** specimens in **B** adaptive batches, including **R** replicates and **F** failed prints.\\u201d\\n- **11B:** \\u201cAt the same physical-test budget, adaptive selection reached **[measured feasible-performance result]** faster than **[predeclared Sobol/random baseline]**.\\u201d\\n- **11C:** \\u201cThe measured feasible Pareto front trades specific energy absorption against compaction efficiency while respecting a **[value and unit]** peak-force cap.\\u201d\\n\\nShow uncertainty, replicate spread, infeasible points, and representative specimen photographs or traces. If the campaign cannot support the baseline claim, change the talk\\u2019s main message to: **\\u201cWe evaluate whether noise-aware multi-objective Bayesian optimization can reduce the physical-test budget.\\u201d**\\n\\n### 2. Slide 7: state the actual constrained optimization problem\\n\\nReplace the current black-box/count design with:\\n\\n- the named decision variables and their types;\\n- a small batch entering the printer/test loop;\\n- objectives: maximize specific energy absorption and compaction efficiency;\\n- constraint: peak transmitted force below a stated cap;\\n- qNEHVI\\u2019s role in one plain sentence: it chooses a small next batch likely to expand the feasible trade-off frontier while accounting for uncertainty and noisy tests;\\n- the declared test budget, initialization size, and batch size.\\n\\nResolve six versus seven variables and whether processing parameters are decision variables before finalizing this slide.\\n\\n### 3. Slide 2: put the proxy boundary beside the hook\\n\\nKeep the Super Ball Bot clip, but add an image of the actual specimen and the visible sentence:\\n\\n> **\\u201cHere, a printed PLA\\u2013TPU T3 prism is a proxy for developing the workflow, not flight hardware.\\u201d**\\n\\nChange or qualify \\u201crobust, reusable solutions\\u201d unless the present campaign includes repeat-impact evidence.\\n\\n### 4. Slide 4: turn the vague need into the two-sided engineering decision\\n\\nRetitle it:\\n\\n> **\\u201cBecause exhaustive testing and campaign-grade model calibration are both costly, each physical specimen must be chosen for its information value.\\u201d**\\n\\nAdd one quantified physical-search estimate and one scoped calibration challenge. Do not say simulation is untrustworthy. State that FEA and lower-fidelity models remain possible screening or future multi-fidelity tools.\\n\\n### 5. Slides 10 and 13: separate the demonstrated loop from the conclusion\\n\\n- **Slide 10:** retitle to **\\u201cThe closed loop turns each measured test into the next small batch of specimens.\\u201d** Remove \\u201csignificantly accelerate\\u201d until the results demonstrate it. Mark automated and manual stages.\\n- **Slide 13:** retain the closed-loop thumbnail, add the best quantitative result, and state the deployment rule: **use the loop when tests are authoritative, each test is costly, the design space is mixed, and performance goals conflict.** Add \\u201cQuestions?\\u201d without clearing the conclusion from the screen.\\n\\n## Hidden-slide adjudication\\n\\n| Hidden slide | Recommendation | Rationale |\\n|---|---|---|\\n| **3: tensegrity usefulness/mechanism** | **Merge into slide 8** | The generic elbow and squishing GIFs add noise and risk an inaccurate mechanism; an annotated actual specimen and deformation sequence are stronger. |\\n| **5: generic BO explanation** | **Merge into slide 7; keep original as backup or delete** | DAC experts do not need a generic advocacy slide, but novices need one plain-language sentence and a concrete acquisition visual. |\\n| **6: specimen information value/experiment-first** | **Merge into slide 4; keep evidence-rich version as backup** | The argument is structurally necessary, but the current title is vague and the visual does not support the scoped calibration-versus-campaign-cost decision. |\\n| **11: results** | **Unhide and expand to three slides** | Results are the evidentiary center of the talk and were allocated five minutes in the agreed outline. An empty or hidden results slot makes the main claim untenable. |\\n\\n## Visible-slide claims needing evidence, hedging, or backup\\n\\n| Slide | Claim | Required treatment |\\n|---|---|---|\\n| **1** | \\u201cBuild better tensegrity structures faster\\u201d | Define \\u201cbetter\\u201d with the two objectives and force constraint; support \\u201cfaster\\u201d with the results comparison. |\\n| **2** | Tensegrity is robust and reusable for planetary landings | Cite the Super Ball Bot source; distinguish concept evidence from this study; provide repeat-impact evidence or hedge reusability. |\\n| **2** | Parachutes and retrorockets struggle in the stated conditions | Cite an authoritative aerospace source if retained in narration; avoid implying universal inadequacy. |\\n| **4** | Current tensegrity design is slow and resource intensive | Quantify candidate count, print/test time, hand-assembly burden, or calibration effort. |\\n| **7** | BO makes experiments affordable with limited, noisy data | Show campaign budget, noise characterization, replicates, baseline, and performance versus tests. Prepare qNEHVI and Gaussian-process details in backup. |\\n| **7** | Six variables, two input-data types, two objectives | Reconcile with the apparent seven-variable abstract specification and identify every quantity. Clarify \\u201cinput-data types.\\u201d |\\n| **8** | Multi-material AM supplies data fast | Report print-to-result cycle time and failure rate. |\\n| **8** | Single-build co-fabrication with no joining | Show interface and post-processing; use \\u201cno joining of PLA and TPU members\\u201d if other manual operations remain. |\\n| **9** | Accelerometers gather the relevant impact data | Show sensor locations and the derivation of transmitted force, specific energy absorption, and compaction efficiency. Reconcile this wording with the abstract\\u2019s instrumented-tup description. |\\n| **10** | The loop significantly accelerates optimization | Remove \\u201csignificantly\\u201d unless a defined baseline and uncertainty support it. Statistical significance should not be implied casually. |\\n| **12** | Workflow will transfer to lattices and other applications | Present as conditional future work with transfer criteria, not an achieved result. Attribute any external crutch-tip image and claim. |\\n\\n### Prepared backup slides\\n\\n1. Full design-variable table, bounds, encoding, and fixed versus optimized process parameters.\\n2. qNEHVI implementation, Gaussian-process assumptions, constraint treatment, batch size, and acquisition settings.\\n3. Noise plan: replicates, periodic control, heteroscedasticity, drift, outlier policy, and uncertainty propagation.\\n4. Budget-matched baseline definition and evaluation metric.\\n5. Force-cap rationale and full objective definitions.\\n6. Sensor chain, calibration, sample rate, filtering, fixture dynamics, and metric extraction.\\n7. PLA\\u2013TPU interface, conditioning, build layout, dimensional/mass deviations, and print-failure modes.\\n8. Scoped FEA rationale with citations or internal validation; possible analytical or multi-fidelity extensions.\\n9. Automation map and cycle-time breakdown.\\n10. Repeated-impact behavior if reusability remains in the opening.\\n\\n## Merged, ordered presenter TODO list\\n\\n### Must fix before the talk\\n\\n1. **Freeze the scientific specification.** Reconcile the number and type of decision variables, whether FDM parameters are optimized or controlled, the two objectives, the force constraint, treatment of print failures, and the measurement chain. Use one formulation across slides, abstract, paper, and Q&A.\\n2. **Set a results completion plan.** Establish dates and owners for campaign completion, repeat tests, data quality review, baseline analysis, and slide freeze. Predeclare the fallback claim if the baseline does not support \\u201cdozens rather than hundreds.\\u201d\\n3. **Build the three-slide results sequence.** Campaign ledger \\u2192 budget-matched baseline \\u2192 measured feasible Pareto front. Allocate approximately five minutes, as the agreed outline requires.\\n4. **Add the proxy statement to slide 2.** State plainly that the PLA\\u2013TPU T3 specimen is a workflow proxy and not flight hardware.\\n5. **Redesign slide 7.** Show the exact constrained multi-objective problem, test budget, small batches, uncertainty, noisy observations, and the role of qNEHVI.\\n6. **Remove unsupported result language.** Until supported, delete \\u201csignificantly\\u201d from slide 10 and avoid \\u201cdozens rather than hundreds\\u201d in the title, narration, or close.\\n7. **Clarify experiment-first positioning.** Merge the information-value argument into slide 4 and prepare an evidence-backed FEA backup slide.\\n8. **Make the experiment auditable.** On slide 9 or backup, reconcile accelerometers and the instrumented tup; show how all three performance metrics are calculated and how uncertainty is handled.\\n9. **De-risk every video.** Store local trimmed files, disable distracting loops, test them on presentation hardware, and include static fallback frames.\\n10. **Rehearse to 13:30\\u201314:00.** Protect the results and conclusion from being cut. Practice once without slides, consistent with Doumont\\u2019s preparation advice.\\n\\n### Should fix\\n\\n11. **Replace slide 8\\u2019s generic print emphasis with mechanism and materials evidence.** Label PLA, TPU, interface strategy, prestress status, deformation, post-processing, and failure modes.\\n12. **Quantify slide 4.** Give at least one campaign-relevant cost, duration, or candidate-count estimate.\\n13. **Show automation boundaries on slide 10.** Candidate selection and surrogate updating may be automated; slicing, handling, test setup, and testing remain manual according to the abstract.\\n14. **Convert slide 12 to a gated roadmap.** Separate demonstrated T3-proxy results from future lattices, flight materials, and terrestrial applications.\\n15. **Turn slide 13 into the conclusion.** Leave the audience with the best result and transfer rule while taking questions.\\n16. **Prepare technical backup slides.** Prioritize baseline fairness, noise, force-cap rationale, print failures, materials repeatability, and FEA positioning.\\n\\n### Polish\\n\\n17. Replace the slide 1 title with a specific message, such as **\\u201cPhysical Bayesian optimization searches multi-material tensegrity absorbers one small test batch at a time.\\u201d**\\n18. Left-align titles and use intentional line breaks; check every slide at six-per-page scale.\\n19. Use one purposeful visual per slide. Avoid decorative loading or elbow animations.\\n20. Standardize terminology: \\u201cBayesian optimization,\\u201d not \\u201cBays Opt\\u201d; \\u201ctensegrity-inspired\\u201d where appropriate; define specific energy absorption once.\\n21. Add citations directly beside borrowed footage, images, and quantitative claims.\\n\\n## Who the deck serves best and worst\\n\\n- **Best served:** **P6, the friendly industry generalist.** The visible sequence is visually intuitive, practical, and easy to reduce to \\u201cprint \\u2192 test \\u2192 learn \\u2192 repeat.\\u201d\\n- **Worst served:** **P1, the skeptical BO insider.** The current deck omits nearly every item needed to judge a DAC-10 contribution: exact problem formulation, budget, baseline, noise model, constraint handling, and empirical adaptive advantage.\\n\\nThat is **not the right trade-off for DAC-10**. The deck should remain accessible to P4 and P6, but its technical center must satisfy P1 without turning into a BO lecture. One rigorous optimization slide, three quantitative results slides, and targeted backup material would achieve that balance.\\n\\n## Predicted reception\\n\\n- **As is: 4/10.** The physical workflow and application are memorable, but the absent scope statement, empty results section, vague BO slide, and unsupported acceleration claim leave the DAC contribution unproven.\\n- **After the must-fixes: 8/10.** A scoped proxy claim, reconciled methods, fair budget-matched comparison, feasible Pareto front, and measured conclusion would turn the same visual story into a credible design-optimization application talk.\\n\\n## Discretionary analytical decisions\\n\\n- Evaluated only the visible slide order for the simulated in-room reactions, while using hidden slides and presenter notes to judge revision options.\\n- Treated planned placeholder visuals as if implemented according to the presenter\\u2019s notes, but judged the selected visual concept and associated stage risk rather than absent graphic quality.\\n- Prioritized the Draft 3 outline over conflicting Draft 1 wording because the prompt identifies it as the agreed story arc, scope framing, results plan, and timing plan.\\n- Rated objections qualitatively as moderate, high, or critical rather than assigning unsupported numerical probabilities.\\n- Recommended three results slides rather than preserving one reserved slot because the agreed outline specifies three distinct evidentiary products and allocates five minutes to results.\\n- Recommended merging hidden slides 3, 5, and 6 rather than simply unhiding them because their necessary ideas can be conveyed with higher signal-to-noise on existing slides.\\n- Treated the six-versus-seven-variable and optimized-versus-fixed-process-parameter differences as unresolved specification conflicts requiring reconciliation, rather than inferring which document is correct.\\n- Assessed claims against the supplied deck extraction, Draft 3 outline, Doumont notes, and submitted abstract only; no independent validation of cited literature or unseen experimental data was attempted.\"}", + "name": "submit_answer" + } + } + ] + }, + "logprob": null + } + } + }, + "created_at": "2026-07-26T02:31:55.309214Z" + }, + { + "id": "f9b2e23d-1422-48f0-adb4-d5c4743079b1", + "trajectory_id": "afade0b1-f49e-4031-a86d-cb5ec4652536", + "trajectory_timestep": 0, + "step": "after_agent_get_asv", + "state": { + "transition": { + "timestep": 0, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ], + "info": null + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Tensegrity Slides \u2014 Draft 1 (extracted content)\n 2\u2192\n 3\u2192Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4\u2192\n 5\u2192\n 6\u2192## Slide 1 \u2014 layout: Title Only\n 7\u2192\n 8\u2192**On-slide text:**\n 9\u2192- **Title 1**: Let\u2019s build better tensegrity structures faster\n 10\u2192- **TextBox 4**: Marcus Madsen\n 11\u2192Undergraduate researcher under Sterling Baird, Ph.D.\n 12\u2192- **TextBox 6**: IDETC 2026\n 13\u2192\n 14\u2192**Non-text shapes / media / placeholders:**\n 15\u2192- [PICTURE (13)] Picture 3\n 16\u2192- [PICTURE (13)] Picture 8\n 17\u2192\n 18\u2192**Presenter notes:**\n 19\u2192> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20\u2192> \n 21\u2192> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22\u2192\n 23\u2192\n 24\u2192## Slide 2 \u2014 layout: Title Only\n 25\u2192\n 26\u2192**On-slide text:**\n 27\u2192- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings\u2014and to problems closer to home.\n 28\u2192\n 29\u2192**Non-text shapes / media / placeholders:**\n 30\u2192- [MEDIA (16)] Online Media 5\n 31\u2192\n 32\u2192**Presenter notes:**\n 33\u2192> Replace this video with a downloaded and trimmed version of just the fall. \n 34\u2192> \n 35\u2192> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle \u2014 thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander\u00a0is\u00a0the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit\u00a0and get back up\u00a0\u2014 it survives multiple drops and keeps working.\n 36\u2192\n 37\u2192\n 38\u2192## Slide 3 (HIDDEN) \u2014 layout: Title Only\n 39\u2192\n 40\u2192**On-slide text:**\n 41\u2192- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42\u2192- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43\u2192- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44\u2192\n 45\u2192**Presenter notes:**\n 46\u2192> This might be better explained purely vocally while on the previous slide.\n 47\u2192\n 48\u2192\n 49\u2192## Slide 4 \u2014 layout: Title Only\n 50\u2192\n 51\u2192**On-slide text:**\n 52\u2192- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53\u2192- **TextBox 3**: Iteration\n 54\u2192- **TextBox 5**: Building\n 55\u2192- **TextBox 7**: Testing\n 56\u2192\n 57\u2192**Non-text shapes / media / placeholders:**\n 58\u2192- [MEDIA (16)] AdobeStock_207717222\n 59\u2192- [MEDIA (16)] Online Media 2\n 60\u2192- [PICTURE (13)] Picture 14\n 61\u2192- [PICTURE (13)] Picture 16\n 62\u2192- [PICTURE (13)] Picture 18\n 63\u2192\n 64\u2192**Presenter notes:**\n 65\u2192> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn\u2019t become distracting because it\u2019s fun to watch it spin.\n 66\u2192> \n 67\u2192> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68\u2192\n 69\u2192\n 70\u2192## Slide 5 (HIDDEN) \u2014 layout: Title Only\n 71\u2192\n 72\u2192**On-slide text:**\n 73\u2192- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74\u2192- **TextBox 14**: Baird et al. Matter (2022)\n 75\u2192\n 76\u2192**Non-text shapes / media / placeholders:**\n 77\u2192- [GROUP (6)] Group 2\n 78\u2192\n 79\u2192**Presenter notes:**\n 80\u2192> Consult Sterling on this slide. I don\u2019t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it\u2019s well enough known that it\u2019d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81\u2192\n 82\u2192\n 83\u2192## Slide 6 (HIDDEN) \u2014 layout: Title Only\n 84\u2192\n 85\u2192**On-slide text:**\n 86\u2192- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87\u2192- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88\u2192\n 89\u2192**Presenter notes:**\n 90\u2192> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91\u2192\n 92\u2192\n 93\u2192## Slide 7 \u2014 layout: Title Only\n 94\u2192\n 95\u2192**On-slide text:**\n 96\u2192- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97\u2192- **TextBox 5**: 6 variables\n 98\u2192- **TextBox 7**: 2 types of input data\n 99\u2192- **TextBox 9**: 2 objectives\n100\u2192- **TextBox 17**: *image of blackbox*\n101\u2192\n102\u2192**Non-text shapes / media / placeholders:**\n103\u2192- [LINE (9)] Connector: Curved 11\n104\u2192- [LINE (9)] Connector: Curved 13\n105\u2192- [LINE (9)] Connector: Curved 15\n106\u2192\n107\u2192**Presenter notes:**\n108\u2192> Very rough slide as I\u2019m not entirely sure what graphic or text to put on this, partially because I\u2019m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109\u2192\n110\u2192\n111\u2192## Slide 8 \u2014 layout: Title Only\n112\u2192\n113\u2192**On-slide text:**\n114\u2192- **Title 1**: Multi-material additive manufacturing supplies that data fast \u2014 single-build co-fabrication, with no joining of parts.\n115\u2192\n116\u2192**Non-text shapes / media / placeholders:**\n117\u2192- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118\u2192- [PICTURE (13)] Picture 4\n119\u2192\n120\u2192**Presenter notes:**\n121\u2192> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122\u2192\n123\u2192\n124\u2192## Slide 9 \u2014 layout: Title Only\n125\u2192\n126\u2192**On-slide text:**\n127\u2192- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128\u2192- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129\u2192- **TextBox 5**: *placeholder for slo motion of drop test*\n130\u2192\n131\u2192\n132\u2192## Slide 10 \u2014 layout: Title Only\n133\u2192\n134\u2192**On-slide text:**\n135\u2192- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136\u2192- **TextBox 3**: Printing\n137\u2192- **TextBox 5**: Testing\n138\u2192- **TextBox 7**: BO feed\n139\u2192\n140\u2192**Non-text shapes / media / placeholders:**\n141\u2192- [PICTURE (13)] Picture 14\n142\u2192- [PICTURE (13)] Picture 16\n143\u2192- [PICTURE (13)] Picture 18\n144\u2192- [PICTURE (13)] Graphic 8\n145\u2192\n146\u2192**Presenter notes:**\n147\u2192> I think it might be better if we replaced \u201cprinting\u201d with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148\u2192\n149\u2192\n150\u2192## Slide 11 (HIDDEN) \u2014 layout: Title Only\n151\u2192\n152\u2192**On-slide text:**\n153\u2192- **Title 1**: Reserved for results\n154\u2192\n155\u2192**Presenter notes:**\n156\u2192> Would be helpful if we could show that this process works in this case.\n157\u2192\n158\u2192\n159\u2192## Slide 12 \u2014 layout: Title Only\n160\u2192\n161\u2192**On-slide text:**\n162\u2192- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163\u2192- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164\u2192- **TextBox 3**: *image to show lattice structure\n165\u2192\n166\u2192\n167\u2192## Slide 13 \u2014 layout: Title Only\n168\u2192\n169\u2192**Non-text shapes / media / placeholders:**\n170\u2192- [PICTURE (13)] Picture 3\n171\u2192\n172\u2192**Presenter notes:**\n173\u2192> Slide for questions at the end\n174\u2192\n175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "info": { + "start_ts": 8147.330900226, + "end_ts": 8147.337548875 + } + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Presentation \u2014 Doumont Presentation Structure Template\n 2\u2192\n 3\u2192Transcribed from Jean-luc Doumont's presentation-structure template\n 4\u2192([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5\u2192adapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\n 6\u2192All rights reserved. Can be downloaded from .\n 7\u2192\n 8\u2192> To design your presentation, write down your ideas for each component below.\n 9\u2192> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10\u2192\n 11\u2192**Draft 3** \u2014 applies the inline review comments from @sgbaird on PR #84 and the\n 12\u2192priority revisions from the\n 13\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14\u2192Summaries of what changed are at the bottom of this file\n 15\u2192([Draft 2 \u2192 3](#changes-from-draft-2), [Draft 1 \u2192 2](#changes-from-draft-1)).\n 16\u2192\n 17\u2192**Scope note (frame everything below with this):** the study presented is the\n 18\u2192optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19\u2192processing parameters** (e.g., nozzle temperature, print speed). PLA\u2013TPU is a\n 20\u2192**proxy system** \u2014 not flight material \u2014 used to prototype a self-driving-lab\n 21\u2192style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22\u2192The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23\u2192beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24\u2192\n 25\u2192## Opening\n 26\u2192\n 27\u2192### Attention getter\n 28\u2192\n 29\u2192*A way to lead the audience to the need efficiently*\n 30\u2192\n 31\u2192Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32\u2192landings \u2014 and to problems closer to home.\n 33\u2192\n 34\u2192One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35\u2192clip) do the work:\n 36\u2192\n 37\u2192> Landing a payload on another planet means surviving an impact in conditions\n 38\u2192> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n 39\u2192> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40\u2192> structure: the lander *is* the energy absorber. And unlike crushable\n 41\u2192> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42\u2192> hit **and get back up** \u2014 it survives multiple drops and keeps working.\n 43\u2192\n 44\u2192Candidate Super Ball Bot videos to show (verified links):\n 45\u2192\n 46\u2192- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n 47\u2192 (official channel); the canonical overview.\n 48\u2192- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49\u2192 \u2014 Wall Street Journal; prototype drop footage.\n 50\u2192- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51\u2192 \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n 52\u2192\n 53\u2192Immediately after the hook, state the present study scope in one sentence\n 54\u2192(per the scope note above): *we study a printed PLA\u2013TPU T3 prism as a proxy\n 55\u2192system for prototyping an autonomous tensegrity-optimization workflow* \u2014 so\n 56\u2192the planetary framing never outruns the demonstrated scope.\n 57\u2192\n 58\u2192\n 60\u2192\n 61\u2192### Need\n 62\u2192\n 63\u2192*A difference between actual and desired situations*\n 64\u2192\n 65\u2192**Actual situation** (a two-sided pincer \u2014 stated as a scoped engineering\n 66\u2192judgment, not an absolute):\n 67\u2192\n 68\u2192- For these structures, obtaining trustworthy objective values from a\n 69\u2192 validated high-fidelity model would require substantial calibration of\n 70\u2192 interface, rate, contact, and defect behavior \u2014 and even heavy-duty\n 71\u2192 simulation falls short of the experiments we are running: peak transmitted\n 72\u2192 shock is a millisecond-scale transient that is notoriously difficult to\n 73\u2192 resolve numerically. This holds even for idealized cable\u2013strut tensegrities\n 74\u2192 in their actual engineering materials (steel or similar), not just for\n 75\u2192 printed PLA\u2013TPU.\n 76\u2192 \n 79\u2192- Exhaustive physical search fails on cost: a traditional design of\n 80\u2192 experiments over this relatively high-dimensional space (geometry + FDM\n 81\u2192 process parameters) would demand hundreds of specimens, each costing print\n 82\u2192 and test time.\n 83\u2192- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84\u2192 more complex structures \u2014 especially tensegrity *lattices* \u2014 become\n 85\u2192 extremely difficult and ultimately infeasible to hand-build.\n 86\u2192\n 87\u2192**Desired situation:**\n 88\u2192\n 89\u2192- Trustworthy performance data without hand assembly (easy creation of\n 90\u2192 tensegrity structures, scalable beyond a single prism).\n 91\u2192- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n 92\u2192 not the hundreds a traditional design of experiments would require.\n 93\u2192\n 94\u2192### Task\n 95\u2192\n 96\u2192*What I decided/was asked to do to address the need*\n 97\u2192\n 98\u2192Build a closed-loop design\u2013print\u2013test workflow that optimizes a **T3-prism\n 99\u2192tensegrity-inspired energy absorber together with its FDM processing\n100\u2192parameters** (e.g., temperature, print speed) directly from real, measured\n101\u2192impact data \u2014 maximizing energy absorbed per gram (specific energy\n102\u2192absorption) and compaction efficiency, subject to a cap on the peak force\n103\u2192transmitted to the payload. Extensions beyond the T3 prism are future work.\n104\u2192\n105\u2192### Main message\n106\u2192\n107\u2192*The one sentence I want my audience to remember*\n108\u2192\n109\u2192By closing the loop between multi-material 3D printing and Bayesian\n110\u2192optimization, we can optimize tensegrity-inspired energy absorbers directly\n111\u2192from real impact data \u2014 in dozens of prints, not the hundreds a traditional\n112\u2192design of experiments would demand.\n113\u2192\n114\u2192\n118\u2192\n119\u2192### Preview\n120\u2192\n121\u2192*A map of the body (ideally three points, max. five)*\n122\u2192\n123\u21921. When model calibration and exhaustive testing are both costly, each\n124\u2192 physical specimen must be chosen for its information value \u2014 so the\n125\u2192 measured experiment is our source of truth.\n126\u21922. Bayesian optimization makes physical experimentation affordable: it handles\n127\u2192 noisy measurements, small batches, and the constrained multi-objective\n128\u2192 trade-off \u2014 and Honegumi let us scaffold it in minutes.\n129\u21923. Multi-material additive manufacturing closes the loop: single-build PLA\u2013TPU\n130\u2192 co-fabrication turns each proposed design into test data in hours, with no\n131\u2192 joining of parts.\n132\u21924. *(Optional fourth point, if time allows)* The same closed loop points at\n133\u2192 what comes next: tensegrity lattices, flight-relevant materials, and\n134\u2192 safety hardware on Earth.\n135\u2192\n136\u2192## Body\n137\u2192\n138\u2192### Point 1\n139\u2192\n140\u2192When model calibration and exhaustive testing are both costly, each physical\n141\u2192specimen must be chosen for its information value.\n142\u2192\n143\u2192- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144\u2192 these structures, a sufficiently calibrated multiphysics model is expensive\n145\u2192 to build relative to the design campaign itself \u2014 and the hardest quantity,\n146\u2192 peak transmitted shock, is a millisecond-scale transient that even\n147\u2192 heavy-duty simulation struggles to resolve. The gap persists in the actual\n148\u2192 engineering materials a real lander would use (steel struts and cables),\n149\u2192 so this is not merely an artifact of printed PLA\u2013TPU.\n150\u2192- The alternative \u2014 sweeping the design space physically \u2014 fails on cost: a\n151\u2192 traditional design of experiments over geometry plus FDM process parameters\n152\u2192 would take hundreds of specimens, each needing print + test time.\n153\u2192- Therefore: treat the physical experiment as the authoritative source of\n154\u2192 objective values, and be ruthless about *which* experiments to run.\n155\u2192- Anticipate the FEA-veteran pushback (\"inconvenient \u2260 untrustworthy\"): frame\n156\u2192 it as a cost-and-dependence decision, and keep a backup slide with the\n157\u2192 literature (or our own validation) on simulation\u2013experiment discrepancy for\n158\u2192 impact transients.\n159\u2192\n160\u2192### Transition\n161\u2192\n162\u2192If every data point must be a real experiment, we need a method that extracts\n163\u2192the most from every specimen \u2014 we need to make the process radically more\n164\u2192sample-efficient.\n165\u2192\n166\u2192### Point 2\n167\u2192\n168\u2192Bayesian optimization makes physical experimentation affordable, even with\n169\u2192limited, noisy data.\n170\u2192\n171\u2192- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172\u2192 visual in the main talk; implementation detail goes to backup slides.\n173\u2192 Spoken version:\n174\u2192 > \"After each batch, the model estimates both expected performance and\n175\u2192 > uncertainty across the design space. The acquisition rule selects a small\n176\u2192 > next batch that is most likely to expand the feasible trade-off frontier,\n177\u2192 > while accounting for noisy tests and print failures.\"\n178\u2192- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179\u2192 motivate a noise-aware acquisition function (qNEHVI) rather than\n180\u2192 non-noise-aware alternatives. (Backup slide: how observation noise is\n181\u2192 actually modeled \u2014 replicates, noise identification \u2014 and budget-matched\n182\u2192 baselines such as Sobol/random sampling.)\n183\u2192- Small parallel batches: the surrogate proposes the next batch of specimens\n184\u2192 to print, balancing exploration and exploitation.\n185\u2192- Constrained multi-objective by construction: **objectives** = maximize\n186\u2192 specific energy absorption and compaction efficiency; **constraint** = peak\n187\u2192 transmitted force must stay under a cap; the deliverable is the *feasible*\n188\u2192 Pareto front between the two objectives among force-cap-satisfying designs.\n189\u2192 (Keep this formulation consistent everywhere \u2014 do not present \"SEA vs. peak\n190\u2192 force\" as the trade-off.)\n191\u2192- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192\u2192 this advanced task (noisy, batched, constrained, multi-objective) in\n193\u2192 minutes using [Honegumi](https://honegumi.readthedocs.io/) \u2014 the audience\n194\u2192 can do the same for their own problems; show the code-template picker\n195\u2192 briefly so they see how low the barrier is.\n196\u2192\n197\u2192### Transition\n198\u2192\n199\u2192The optimizer is no longer the bottleneck \u2014 the limit is now providing real\n200\u2192data quickly enough.\n201\u2192\n202\u2192### Point 3\n203\u2192\n204\u2192Multi-material additive manufacturing supplies that data fast \u2014 single-build\n205\u2192co-fabrication, with no joining of parts.\n206\u2192\n207\u2192- Single-build PLA\u2013TPU co-fabrication: rigid struts and flexible tension\n208\u2192 network in one print \u2014 no joining or hand assembly of members, which closes\n209\u2192 the \"easy creation\" gap from the Need and is what makes future lattices\n210\u2192 feasible at all. (Be precise: if support removal, trimming, or conditioning\n211\u2192 remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212\u2192 assembly.\")\n213\u2192- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214\u2192 as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215\u2192 pre-tension status) plus a short deformation sequence \u2014 so the EDL engineer\n216\u2192 doesn't file it as a \"crush structure\" and the grad student can tell it from\n217\u2192 an ordinary lattice.\n218\u2192- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219\u2192 tested specimen (quasi-static compression + instrumented drop-weight impact)\n220\u2192 in hours. (Backup slide: representative print-to-result time breakdown and\n221\u2192 which steps remain manual \u2014 candidate selection is automated; slicing,\n222\u2192 handling, and testing are not yet.)\n223\u2192- Now we have the data we need to complete the loop.\n224\u2192\n225\u2192### Point 4 (optional \u2014 future applications)\n226\u2192\n227\u2192*Include if timing allows; otherwise fold into the Conclusion.*\n228\u2192\n229\u2192The same closed loop transfers wherever tests are authoritative, each test is\n230\u2192costly, the design space is mixed, and several performance goals conflict:\n231\u2192\n232\u2192- Tensegrity *lattices* and more complex architectures \u2014 exactly where hand\n233\u2192 assembly becomes infeasible and single-build co-fabrication pays off.\n234\u2192- Migration from the PLA\u2013TPU proxy toward flight-relevant materials and\n235\u2192 impact regimes.\n236\u2192- Safety hardware on Earth (e.g., the tensegrity crutch tip \u2014 future work).\n237\u2192- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238\u2192 handling, testing) in the loop.\n239\u2192\n240\u2192### Evidence / results\n241\u2192\n242\u2192*What we found \u2014 ground the conclusion in shown data*\n243\u2192\n244\u2192Build the talk around a quantitative three-slide sequence (per the\n245\u2192mock-audience report; insert observed values once the campaign completes):\n246\u2192\n247\u21921. **Campaign ledger** \u2014 \"The campaign used N specimens across B adaptive\n248\u2192 batches under a fixed physical-test budget\": initialization vs. adaptive\n249\u2192 batches, replicates, print failures, cycle time, and the numerical value\n250\u2192 (and rationale) of the force cap.\n251\u21922. **Budget-matched baseline** \u2014 \"Adaptive selection improved the feasible\n252\u2192 design set faster than [predeclared baseline]\": performance vs. number of\n253\u2192 physical tests against Sobol/random sampling at the same budget, with\n254\u2192 uncertainty where available.\n255\u21923. **Measured feasible Pareto front** \u2014 specific energy absorption vs.\n256\u2192 compaction efficiency among designs satisfying the force cap, with\n257\u2192 replicate spread, marked infeasible designs, and specimen photos or\n258\u2192 force\u2013time traces for representative Pareto designs.\n259\u2192\n260\u2192Supporting raw-data candidates: drop-test force\u2013time traces, Sobol /\n261\u2192first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262\u2192\n263\u2192### Suggested 15-minute allocation\n264\u2192\n265\u2192*(From the mock-audience report \u2014 results get the largest block.)*\n266\u2192\n267\u2192| Time | Content |\n268\u2192|---|---|\n269\u2192| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270\u2192| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271\u2192| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272\u2192| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273\u2192| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274\u2192| 1 min | Limitations and transfer conditions |\n275\u2192| 1 min | Measured conclusion + bookend |\n276\u2192\n277\u2192Move kernel choices, encodings, and other implementation detail to backup\n278\u2192slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279\u2192[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280\u2192\n281\u2192## Closing\n282\u2192\n283\u2192### Review\n284\u2192\n285\u2192*A recap of the body, leading into the conclusion*\n286\u2192\n287\u2192Using real, measured impact data from additively manufactured\n288\u2192tensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\n289\u2192optimization routine \u2014 we rapidly iterate toward better energy absorbers.\n290\u2192This feedback loop makes tensegrity optimization dramatically faster: dozens\n291\u2192of prints instead of the hundreds a traditional design of experiments over\n292\u2192this design space would require.\n293\u2192\n294\u2192### Conclusion\n295\u2192\n296\u2192*What the above means to the audience in the end*\n297\u2192\n298\u2192Because the loop runs on physical measurements, it is useful precisely where\n299\u2192model calibration is not worth its cost \u2014 and the same closed-loop pattern\n300\u2192should transfer (a conditional claim, pending evidence) to other additively\n301\u2192manufactured architectures whose performance is dominated by\n302\u2192hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303\u2192*use this loop when tests are authoritative, each test is costly, the design\n304\u2192space is mixed, and several performance goals conflict.* For tensegrity\n305\u2192specifically, it opens a path from this T3 proxy study toward lattices,\n306\u2192flight-relevant materials, payload protection on other planets, and safety\n307\u2192hardware here on Earth (e.g., the crutch tip, as future work).\n308\u2192\n309\u2192### Close\n310\u2192\n311\u2192*A way to end the presentation clearly and elegantly*\n312\u2192\n313\u2192Return to the opening image: the next planetary lander's energy absorber \u2014 a\n314\u2192structure that has to survive not one impact but many \u2014 doesn't have to take\n315\u2192years of hand-built prototypes; with this loop, candidate designs can be\n316\u2192designed, printed, and tested against real impacts in weeks instead of months\n317\u2192or longer. End on the measured engineering result and its transfer condition,\n318\u2192then the bookend: every method that compresses design time moves our ventures\n319\u2192\u2014 on this planet and beyond \u2014 forward.\n320\u2192\n321\u2192---\n322\u2192\n323\u2192## Changes from Draft 2\n324\u2192\n325\u2192Applied from @sgbaird's inline review comments on PR #84 and the\n326\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327\u2192\n328\u2192**From the inline review comments:**\n329\u2192\n330\u21921. **Reusability in the hook** (line 22): the tensegrity lander survives\n331\u2192 multiple drops, unlike crushable/sacrificial alternatives.\n332\u21922. **Proxy-system framing** (lines 49, 87): PLA\u2013TPU would never fly; it is a\n333\u2192 proxy for prototyping a self-driving-lab-style workflow \u2014 stated in a\n334\u2192 scope note up front and re-stated right after the hook.\n335\u21923. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336\u2192 heavy-duty simulation falls short of the experiments (millisecond-scale\n337\u2192 peak shock), not that it merely costs too much.\n338\u21924. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339\u2192 complex structures and especially lattices are not.\n340\u21925. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341\u2192 (temperature, print speed, \u2026) only; the rest is explicitly future work.\n342\u21926. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343\u2192 literature search (or validation of experiments against simulation), plus\n344\u2192 the millisecond-resolution peak-shock argument.\n345\u21927. **Optional Point 4 added** (line 97): future applications, with a note it\n346\u2192 can fold into the Conclusion if time is short.\n347\u21928. **Steel-materials point** (line 104): simulations fall short for the\n348\u2192 actual engineering materials (steel struts/cables), not just printed\n349\u2192 PLA\u2013TPU.\n350\u21929. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351\u2192 in minutes, pitched so the audience sees they can use it too.\n352\u219210. **\"Thousands\" \u2192 \"hundreds\"** (line 173): baseline is now a traditional\n353\u2192 design of experiments over a high-dimensional space (hundreds), in the\n354\u2192 main message, Need, Point 1, and Review.\n355\u219211. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356\u2192 or longer\" for candidate designs.\n357\u2192\n358\u2192**From the mock-audience report's priority revisions:**\n359\u2192\n360\u219212. **Quantitative three-slide results sequence** (Priority 1): campaign\n361\u2192 ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front, with a\n362\u2192 fallback main-message wording if the campaign can't yet support the\n363\u2192 efficiency claim.\n364\u219213. **Anti-simulation premise replaced with a scoped cost decision**\n365\u2192 (Priority 2): Point 1 retitled to the information-value message;\n366\u2192 \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367\u2192 anticipated with a backup slide.\n368\u219214. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369\u2192 implementation detail moved to backup slides; Q&A prep pointed at the\n370\u2192 report's claims-vs-evidence table.\n371\u219215. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372\u2192 the main talk, machinery in backup slides; the misleading\n373\u2192 \"qNEHVI over standard expected improvement\" comparison replaced with\n374\u2192 \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375\u219216. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376\u2192 sequence in Point 3, so the structure isn't remembered as a generic crush\n377\u2192 structure or lattice.\n378\u219217. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379\u2192 co-fabrication / no joining of parts,\" with a note to list remaining\n380\u2192 manual steps and an automation-status caveat.\n381\u219218. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382\u2192 compaction efficiency, constraint = force cap, deliverable = *feasible*\n383\u2192 Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384\u219219. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385\u2192 transfer claim marked conditional, and the close ends on the measured\n386\u2192 result before the bookend.\n387\u2192\n388\u2192## Changes from Draft 1\n389\u2192\n390\u2192Feedback from the PR #84 review, applied in Draft 2:\n391\u2192\n392\u21921. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n393\u2192 dozens-not-thousands claim); body and message now carry the same story.\n394\u21922. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n395\u2192 manuscript's positioning (#75/#76) is that the physical experiment is the\n396\u2192 source of truth, explicitly avoiding calibrated FE simulation.\n397\u21923. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n398\u2192 parametric design space and avoid an over-claim to a design-automation\n399\u2192 audience.\n400\u21924. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401\u2192 energy absorption and compaction efficiency, cap peak transmitted force.\n402\u21925. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403\u2192 *and* the space is too big to brute-force \u2014 which makes BO + AM the\n404\u2192 inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405\u2192 above.)*\n406\u21926. **Point 1 given its own content** (the *why* behind the inefficiency)\n407\u2192 instead of restating the Need.\n408\u21927. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409\u2192 the specifics (noise-aware acquisition, small batches, multi-objective).\n410\u21928. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411\u2192 (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412\u2192 above.)*\n413\u21929. **Evidence/results slot added** before the Closing, so the Conclusion is\n414\u2192 grounded in shown data rather than three hedged \"may\"s.\n415\u219210. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n416\u2192 Ball Bot visual; verified video links included.\n417\u219211. **Close bookends the opening** (the next lander's absorber, designed in\n418\u2192 weeks) before the broader humankind line.\n419\u219212. **Spelling**: Doumont (was \"Duomont\").", + "name": "read", + "tool_call_id": "call_93haurOMDGiH2mQFFbaMaYKn", + "info": { + "start_ts": 8147.331065886, + "end_ts": 8147.336467845 + } + }, + { + "role": "tool", + "content": " 1\u2192# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n 2\u2192\n 3\u2192Video: (assigned in #83)\n 4\u2192\n 5\u2192Transcribed from @me-madsen's handwritten notes\n 6\u2192([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7\u2192dated 2026-07-15).\n 8\u2192\n 9\u2192## Overview\n10\u2192\n11\u2192A presentation is all about the right content, displayed the right way, at the\n12\u2192right time.\n13\u2192\n14\u2192- Verbal communication is sequential.\n15\u2192- Message is *interpretation* of the information.\n16\u2192 - Ensure the audience can get the most out of what you're doing.\n17\u2192 - Don't just give them information \u2014 help them understand it, interpret it,\n18\u2192 and internalize it.\n19\u2192\n20\u2192## Doumont's suggested rules for a presentation\n21\u2192\n22\u21920. Ensure you have **messages** for your audience, not just words.\n23\u21921. **Adapt to your audience.**\n24\u2192 - Think like the audience, not the speaker.\n25\u21922. **Maximize signal-to-noise ratio.**\n26\u2192 - Reduce noise (animations, laser pointers, anything that pulls attention\n27\u2192 away from your message).\n28\u21923. **Use effective redundancy.**\n29\u2192 - Tell your message in word *and* on slide: someone deaf should understand\n30\u2192 your messages just as well as someone blind.\n31\u2192 - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32\u2192 date).\n33\u2192\n34\u2192## When creating slides\n35\u2192\n36\u2192- If the audience doesn't need it, why put it on the slides?\n37\u2192- People shouldn't have to count the items on a slide to know how many are\n38\u2192 there.\n39\u2192- Don't use your slides as your \"presentation notes.\"\n40\u2192 - Don't show your notes to the audience.\n41\u2192 - **A bad slide is worse than no slide at all.**\n42\u2192- **One message per slide** is a good rule of thumb.\n43\u2192 - Prioritize the *so what* (or *therefore what*) of your message.\n44\u2192- Make it easy to interpret the information on the slide:\n45\u2192 - 3\u20135 items at most.\n46\u2192 - Align the title left.\n47\u2192 - Do not clutter slides with repetitive or unnecessary information: logos,\n48\u2192 affiliation, date, slide numbers.\n49\u2192- Doumont's suggested workflow:\n50\u2192 1. **Start with the title.**\n51\u2192 - Use a full sentence in the title.\n52\u2192 - The title is the main message of the slide.\n53\u2192 - Make it easy to read: left-aligned, with intentional line breaks.\n54\u2192 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55\u2192- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56\u2192\n57\u2192## General preparation\n58\u2192\n59\u2192- Planning for audience, content, and structure matters more than materials\n60\u2192 (i.e., slides).\n61\u2192- Try using one sheet of paper for presentation preparation.\n62\u2192- It's most important that your presentation is:\n63\u2192 - well planned, structured, and delivered;\n64\u2192 - **if you don't have a lot of time to prepare, don't focus on making\n65\u2192 slides.**\n66\u2192- Practice the presentation a few times. Practice at least once *without* your\n67\u2192 slideshow!\n68\u2192- If doing a handout, keep it separate from the slides you're preparing.\n69\u2192- Impress the audience with your body.\n70\u2192 - Use gestures, not laser pointers.\n71\u2192 - \"Control\" the audience with your body \u2014 keep their attention on your\n72\u2192 presentation.\n73\u2192\n74\u2192## Impressions / what to apply to the IDETC presentation\n75\u2192\n76\u2192\n77\u2192\n78\u2192## Questions / things to discuss\n79\u2192\n80\u2192", + "name": "read", + "tool_call_id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "info": { + "start_ts": 8147.331133416, + "end_ts": 8147.338304015 + } + }, + { + "role": "tool", + "content": " 1\u2192\\documentclass[10pt]{article}\n 2\u2192\n 3\u2192% --- Packages ---\n 4\u2192\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5\u2192\\usepackage[hidelinks]{hyperref}\n 6\u2192\\usepackage{parskip}\n 7\u2192\\usepackage{graphicx}\n 8\u2192\\usepackage{caption}\n 9\u2192\\usepackage{titlesec}\n 10\u2192\\usepackage{wrapfig}\n 11\u2192\\usepackage[numbers,sort&compress]{natbib}\n 12\u2192\n 13\u2192% --- Formatting ---\n 14\u2192\\setlength{\\parskip}{0.2em}\n 15\u2192\\setlength{\\parindent}{0em}\n 16\u2192\\titleformat*{\\section}{\\large\\bfseries}\n 17\u2192\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18\u2192\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19\u2192\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20\u2192\\setlength{\\emergencystretch}{2em}\n 21\u2192\\setlength{\\intextsep}{0.2em}\n 22\u2192\\bibliographystyle{abbrvnat}\n 23\u2192\n 24\u2192\\begin{document}\n 25\u2192\n 26\u2192\\begin{center}\n 27\u2192{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28\u21923D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29\u2192\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30\u2192\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31\u2192\\end{center}\n 32\u2192\n 33\u2192\\vspace{0.4em}\n 34\u2192\n 35\u2192\\section*{Abstract}\n 36\u2192Tensegrity-inspired architectures---rigid struts suspended within a\n 37\u2192continuous flexible network---can exhibit tunable nonlinear\n 38\u2192force--displacement responses and favorable energy absorption per unit\n 39\u2192mass, motivating their use in lightweight energy-absorbing and protective\n 40\u2192structures\n 41\u2192\\citep{skelton2009tensegrity,pajunen2019design}.\n 42\u2192Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43\u2192struts and flexible (TPU) elements in a single build\n 44\u2192\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45\u2192space---strut geometry, tension-element cross-section, connectivity\n 46\u2192topology, and unit-cell tiling---is too large to explore by trial and\n 47\u2192error. We present a closed-loop experimental campaign that uses\n 48\u2192Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49\u2192to drive a design--print--test workflow operating directly on physical\n 50\u2192measurements, without relying on calibrated finite-element simulation\n 51\u2192for objective evaluation.\n 52\u2192\n 53\u2192\\section{Introduction and Motivation}\n 54\u2192\n 55\u2192\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56\u2192\\centering\n 57\u2192\\vspace{-0.4em}\n 58\u2192\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59\u2192\\captionsetup{font=small}\n 60\u2192\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61\u2192Candidate tensegrity-inspired unit cells are instantiated from a\n 62\u2192parameter vector, fabricated by multi-material FDM, and tested under\n 63\u2192quasi-static compression and drop-weight impact. Measured peak force,\n 64\u2192specific energy absorption (SEA), and compaction efficiency update a\n 65\u2192Gaussian-process surrogate that proposes the next batch of designs.}\n 66\u2192\\label{fig:overview}\n 67\u2192\\vspace{-0.4em}\n 68\u2192\\end{wrapfigure}\n 69\u2192\n 70\u2192Tensegrity structures combine compression-only members with a\n 71\u2192pre-tensioned tension network to achieve stiffness without rigid\n 72\u2192joinery~\\citep{skelton2009tensegrity}. Their\n 73\u2192nonlinear, often load-limiting force--displacement response makes them\n 74\u2192attractive for impact mitigation, packaging, and wearable protective\n 75\u2192gear~\\citep{pajunen2019design}. Idealized\n 76\u2192cable--strut tensegrities are difficult to manufacture at scale, but\n 77\u2192\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78\u21923D printing reproduce key tensegrity behaviors---including\n 79\u2192post-buckling stability and load-limiting force--displacement\n 80\u2192response~\\citep{pajunen2019design}---motivating our extension to\n 81\u2192multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82\u2192elements. PLA provides rigid struts; TPU provides flexible,\n 83\u2192rate-dependent tension elements whose viscoelasticity is expected to\n 84\u2192contribute hysteretic energy dissipation under impact loading,\n 85\u2192consistent with the energy-absorbing behavior reported for rigid--TPU\n 86\u2192multi-material prints~\\citep{khatri2024energy}.\n 87\u2192\n 88\u2192The challenge is that small geometric and topological changes can\n 89\u2192produce large changes in the measured response, and high-fidelity\n 90\u2192simulation of FDM specimens is computationally expensive and often\n 91\u2192struggles to capture physical defects such as interfacial slip and\n 92\u2192TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93\u2192trial-and-error sweeps over the joint design space are also infeasible:\n 94\u2192even modest parameter resolutions yield thousands of candidates, each\n 95\u2192requiring print and test time. We instead treat the physical\n 96\u2192experiment as the source of truth and use BO to select which specimens\n 97\u2192are most informative to fabricate next---an approach used in related\n 98\u2192closed-loop materials and structures\n 99\u2192campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100\u2192Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101\u2192set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102\u2192designs using parallel, noisy multi-objective BO driven by measured\n103\u2192$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104\u2192\n105\u2192\\section{Approach}\n106\u2192\\subsection{Closed-Loop Workflow}\n107\u2192Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108\u2192proceeds in four steps: (i)~a parameterized unit-cell design is\n109\u2192instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110\u2192is sliced and printed on a multi-material FDM system; (iii)~it is\n111\u2192tested under quasi-static compression and instrumented drop-weight\n112\u2192impact; and (iv)~the resulting performance metrics update a\n113\u2192Gaussian-process (GP) surrogate, which a batch acquisition function\n114\u2192queries to recommend the next set of candidates. Surrogate updating\n115\u2192and experiment selection are automated; specimen handling, slicing,\n116\u2192and test setup remain manual.\n117\u2192\n118\u2192\\subsection{Design Space}\n119\u2192We parameterize a family of unit cells using a core-wrapping\n120\u2192architecture in which rigid PLA struts are enclosed by continuous TPU\n121\u2192skins; the wrapping rationale follows multi-material FDM strategies in\n122\u2192which rigid panels are wrapped by stretchable soft material to prevent\n123\u2192delamination and improve interfacial bonding at the rigid--flexible\n124\u2192interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125\u2192comprises continuous geometric variables (strut diameter and length,\n126\u2192tension-element width and thickness), an integer strut count per unit\n127\u2192cell, and two categorical variables (connectivity topology, unit-cell\n128\u2192tiling pattern). Categorical choices are encoded explicitly rather\n129\u2192than embedded in a continuous metric, and bounds on the continuous\n130\u2192variables are chosen to remain within the printer's resolution and\n131\u2192overhang limits and to keep specimens in a common form factor for the\n132\u2192test fixtures.\n133\u2192\n134\u2192\\subsection{Surrogate and Acquisition}\n135\u2192The GP surrogate is trained on three measured performance metrics:\n136\u2192peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137\u2192(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138\u2192objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139\u2192likelihood with priors to avoid pathological length scales at small $n$.\n140\u2192Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141\u2192interfacial defects motivates our use of q-noisy expected hypervolume\n142\u2192improvement (qNEHVI), which explicitly accounts for observation noise\n143\u2192in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144\u2192expected-improvement variants. Print failures are treated as a\n145\u2192probabilistic feasibility constraint: the acquisition score for each\n146\u2192candidate is its qNEHVI improvement weighted by the GP-estimated\n147\u2192probability of successful fabrication, following recent constrained\n148\u2192multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149\u2192Where applicable, we use numerically stable log-space variants of\n150\u2192improvement-based acquisitions to mitigate vanishing-gradient\n151\u2192pathologies as data and constraints\n152\u2192accumulate~\\citep{ament2023logei}.\n153\u2192\n154\u2192\\section{Methods}\n155\u2192\\subsection{Fabrication}\n156\u2192Specimens are fabricated on a multi-material FDM system capable of\n157\u2192co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158\u2192Print parameters (temperature, line width, infill, retraction) are\n159\u2192held fixed within a batch to isolate the effect of the design\n160\u2192variables. Each specimen is weighed and dimensionally inspected prior\n161\u2192to testing so that mass and geometric deviations are available as\n162\u2192covariates.\n163\u2192\n164\u2192\\subsection{Mechanical Testing}\n165\u2192Quasi-static compression is performed on a screw-driven load frame at\n166\u2192a fixed nominal strain rate, with force--displacement recorded to\n167\u2192densification. Drop-weight impact testing uses an instrumented tup at\n168\u2192a fixed impact energy; transmitted force is captured at high sample\n169\u2192rate, and specimens are imaged before and after impact to document\n170\u2192failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171\u2192unit mass, and compaction efficiency $\\eta$, defined consistently\n172\u2192across the campaign. A control specimen from an earlier batch is\n173\u2192retested periodically to monitor TPU batch-to-batch and printer drift.\n174\u2192\n175\u2192\\section{Expected Outcomes and Discussion}\n176\u2192We will report how BO-selected designs evolve across iterations, how\n177\u2192GP surrogate predictions and uncertainties change as data accumulate,\n178\u2192and which Pareto-efficient trade-offs emerge between SEA and peak\n179\u2192transmitted force within the explored design space. We will also\n180\u2192discuss practical lessons for operating the closed-loop workflow,\n181\u2192including handling of print failures, batch-to-batch variability in\n182\u2192TPU response, the influence of the rigid--flexible interface on\n183\u2192energy absorption, and the exploration--exploitation balance of the\n184\u2192acquisition function under realistic experimental noise. We expect\n185\u2192the campaign to clarify where physical experimentation is genuinely\n186\u2192required versus where simpler analytical or multi-fidelity surrogate\n187\u2192shortcuts~\\citep{mo2023accelerated} could be\n188\u2192substituted in future work.\n189\u2192\n190\u2192\\section{Conclusion}\n191\u2192This work demonstrates a partially automated, experiment-driven\n192\u2192closed-loop pipeline for designing multi-material 3D-printed\n193\u2192tensegrity-inspired energy absorbers, with surrogate updating and\n194\u2192candidate selection automated and fabrication and testing kept manual.\n195\u2192By updating a GP surrogate directly from physical measurements, the\n196\u2192workflow avoids direct dependence on calibrated finite-element\n197\u2192simulation for objective evaluation while still providing principled,\n198\u2192multi-objective design recommendations. The same closed-loop pattern is\n199\u2192expected to transfer to other multi-material additively manufactured\n200\u2192architectures whose performance is dominated by hard-to-simulate\n201\u2192effects.\n202\u2192\n203\u2192{\\tiny\n204\u2192\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205\u2192\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206\u2192% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207\u2192% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208\u2192\\providecommand{\\doi}[1]{}\n209\u2192\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210\u2192\\bibliography{references}\n211\u2192}\n212\u2192\n213\u2192\\end{document}", + "name": "read", + "tool_call_id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "info": { + "start_ts": 8147.332633466, + "end_ts": 8147.339043095 + } + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\", \"offset\": 174, \"limit\": 100}", + "name": "read" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ], + "info": null + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Tensegrity Slides \u2014 Draft 1 (extracted content)\n 2\u2192\n 3\u2192Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4\u2192\n 5\u2192\n 6\u2192## Slide 1 \u2014 layout: Title Only\n 7\u2192\n 8\u2192**On-slide text:**\n 9\u2192- **Title 1**: Let\u2019s build better tensegrity structures faster\n 10\u2192- **TextBox 4**: Marcus Madsen\n 11\u2192Undergraduate researcher under Sterling Baird, Ph.D.\n 12\u2192- **TextBox 6**: IDETC 2026\n 13\u2192\n 14\u2192**Non-text shapes / media / placeholders:**\n 15\u2192- [PICTURE (13)] Picture 3\n 16\u2192- [PICTURE (13)] Picture 8\n 17\u2192\n 18\u2192**Presenter notes:**\n 19\u2192> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20\u2192> \n 21\u2192> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22\u2192\n 23\u2192\n 24\u2192## Slide 2 \u2014 layout: Title Only\n 25\u2192\n 26\u2192**On-slide text:**\n 27\u2192- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings\u2014and to problems closer to home.\n 28\u2192\n 29\u2192**Non-text shapes / media / placeholders:**\n 30\u2192- [MEDIA (16)] Online Media 5\n 31\u2192\n 32\u2192**Presenter notes:**\n 33\u2192> Replace this video with a downloaded and trimmed version of just the fall. \n 34\u2192> \n 35\u2192> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle \u2014 thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander\u00a0is\u00a0the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit\u00a0and get back up\u00a0\u2014 it survives multiple drops and keeps working.\n 36\u2192\n 37\u2192\n 38\u2192## Slide 3 (HIDDEN) \u2014 layout: Title Only\n 39\u2192\n 40\u2192**On-slide text:**\n 41\u2192- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42\u2192- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43\u2192- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44\u2192\n 45\u2192**Presenter notes:**\n 46\u2192> This might be better explained purely vocally while on the previous slide.\n 47\u2192\n 48\u2192\n 49\u2192## Slide 4 \u2014 layout: Title Only\n 50\u2192\n 51\u2192**On-slide text:**\n 52\u2192- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53\u2192- **TextBox 3**: Iteration\n 54\u2192- **TextBox 5**: Building\n 55\u2192- **TextBox 7**: Testing\n 56\u2192\n 57\u2192**Non-text shapes / media / placeholders:**\n 58\u2192- [MEDIA (16)] AdobeStock_207717222\n 59\u2192- [MEDIA (16)] Online Media 2\n 60\u2192- [PICTURE (13)] Picture 14\n 61\u2192- [PICTURE (13)] Picture 16\n 62\u2192- [PICTURE (13)] Picture 18\n 63\u2192\n 64\u2192**Presenter notes:**\n 65\u2192> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn\u2019t become distracting because it\u2019s fun to watch it spin.\n 66\u2192> \n 67\u2192> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68\u2192\n 69\u2192\n 70\u2192## Slide 5 (HIDDEN) \u2014 layout: Title Only\n 71\u2192\n 72\u2192**On-slide text:**\n 73\u2192- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74\u2192- **TextBox 14**: Baird et al. Matter (2022)\n 75\u2192\n 76\u2192**Non-text shapes / media / placeholders:**\n 77\u2192- [GROUP (6)] Group 2\n 78\u2192\n 79\u2192**Presenter notes:**\n 80\u2192> Consult Sterling on this slide. I don\u2019t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it\u2019s well enough known that it\u2019d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81\u2192\n 82\u2192\n 83\u2192## Slide 6 (HIDDEN) \u2014 layout: Title Only\n 84\u2192\n 85\u2192**On-slide text:**\n 86\u2192- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87\u2192- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88\u2192\n 89\u2192**Presenter notes:**\n 90\u2192> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91\u2192\n 92\u2192\n 93\u2192## Slide 7 \u2014 layout: Title Only\n 94\u2192\n 95\u2192**On-slide text:**\n 96\u2192- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97\u2192- **TextBox 5**: 6 variables\n 98\u2192- **TextBox 7**: 2 types of input data\n 99\u2192- **TextBox 9**: 2 objectives\n100\u2192- **TextBox 17**: *image of blackbox*\n101\u2192\n102\u2192**Non-text shapes / media / placeholders:**\n103\u2192- [LINE (9)] Connector: Curved 11\n104\u2192- [LINE (9)] Connector: Curved 13\n105\u2192- [LINE (9)] Connector: Curved 15\n106\u2192\n107\u2192**Presenter notes:**\n108\u2192> Very rough slide as I\u2019m not entirely sure what graphic or text to put on this, partially because I\u2019m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109\u2192\n110\u2192\n111\u2192## Slide 8 \u2014 layout: Title Only\n112\u2192\n113\u2192**On-slide text:**\n114\u2192- **Title 1**: Multi-material additive manufacturing supplies that data fast \u2014 single-build co-fabrication, with no joining of parts.\n115\u2192\n116\u2192**Non-text shapes / media / placeholders:**\n117\u2192- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118\u2192- [PICTURE (13)] Picture 4\n119\u2192\n120\u2192**Presenter notes:**\n121\u2192> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122\u2192\n123\u2192\n124\u2192## Slide 9 \u2014 layout: Title Only\n125\u2192\n126\u2192**On-slide text:**\n127\u2192- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128\u2192- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129\u2192- **TextBox 5**: *placeholder for slo motion of drop test*\n130\u2192\n131\u2192\n132\u2192## Slide 10 \u2014 layout: Title Only\n133\u2192\n134\u2192**On-slide text:**\n135\u2192- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136\u2192- **TextBox 3**: Printing\n137\u2192- **TextBox 5**: Testing\n138\u2192- **TextBox 7**: BO feed\n139\u2192\n140\u2192**Non-text shapes / media / placeholders:**\n141\u2192- [PICTURE (13)] Picture 14\n142\u2192- [PICTURE (13)] Picture 16\n143\u2192- [PICTURE (13)] Picture 18\n144\u2192- [PICTURE (13)] Graphic 8\n145\u2192\n146\u2192**Presenter notes:**\n147\u2192> I think it might be better if we replaced \u201cprinting\u201d with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148\u2192\n149\u2192\n150\u2192## Slide 11 (HIDDEN) \u2014 layout: Title Only\n151\u2192\n152\u2192**On-slide text:**\n153\u2192- **Title 1**: Reserved for results\n154\u2192\n155\u2192**Presenter notes:**\n156\u2192> Would be helpful if we could show that this process works in this case.\n157\u2192\n158\u2192\n159\u2192## Slide 12 \u2014 layout: Title Only\n160\u2192\n161\u2192**On-slide text:**\n162\u2192- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163\u2192- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164\u2192- **TextBox 3**: *image to show lattice structure\n165\u2192\n166\u2192\n167\u2192## Slide 13 \u2014 layout: Title Only\n168\u2192\n169\u2192**Non-text shapes / media / placeholders:**\n170\u2192- [PICTURE (13)] Picture 3\n171\u2192\n172\u2192**Presenter notes:**\n173\u2192> Slide for questions at the end\n174\u2192\n175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "info": { + "start_ts": 8147.330900226, + "end_ts": 8147.337548875 + } + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Presentation \u2014 Doumont Presentation Structure Template\n 2\u2192\n 3\u2192Transcribed from Jean-luc Doumont's presentation-structure template\n 4\u2192([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5\u2192adapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\n 6\u2192All rights reserved. Can be downloaded from .\n 7\u2192\n 8\u2192> To design your presentation, write down your ideas for each component below.\n 9\u2192> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10\u2192\n 11\u2192**Draft 3** \u2014 applies the inline review comments from @sgbaird on PR #84 and the\n 12\u2192priority revisions from the\n 13\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14\u2192Summaries of what changed are at the bottom of this file\n 15\u2192([Draft 2 \u2192 3](#changes-from-draft-2), [Draft 1 \u2192 2](#changes-from-draft-1)).\n 16\u2192\n 17\u2192**Scope note (frame everything below with this):** the study presented is the\n 18\u2192optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19\u2192processing parameters** (e.g., nozzle temperature, print speed). PLA\u2013TPU is a\n 20\u2192**proxy system** \u2014 not flight material \u2014 used to prototype a self-driving-lab\n 21\u2192style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22\u2192The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23\u2192beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24\u2192\n 25\u2192## Opening\n 26\u2192\n 27\u2192### Attention getter\n 28\u2192\n 29\u2192*A way to lead the audience to the need efficiently*\n 30\u2192\n 31\u2192Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32\u2192landings \u2014 and to problems closer to home.\n 33\u2192\n 34\u2192One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35\u2192clip) do the work:\n 36\u2192\n 37\u2192> Landing a payload on another planet means surviving an impact in conditions\n 38\u2192> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n 39\u2192> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40\u2192> structure: the lander *is* the energy absorber. And unlike crushable\n 41\u2192> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42\u2192> hit **and get back up** \u2014 it survives multiple drops and keeps working.\n 43\u2192\n 44\u2192Candidate Super Ball Bot videos to show (verified links):\n 45\u2192\n 46\u2192- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n 47\u2192 (official channel); the canonical overview.\n 48\u2192- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49\u2192 \u2014 Wall Street Journal; prototype drop footage.\n 50\u2192- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51\u2192 \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n 52\u2192\n 53\u2192Immediately after the hook, state the present study scope in one sentence\n 54\u2192(per the scope note above): *we study a printed PLA\u2013TPU T3 prism as a proxy\n 55\u2192system for prototyping an autonomous tensegrity-optimization workflow* \u2014 so\n 56\u2192the planetary framing never outruns the demonstrated scope.\n 57\u2192\n 58\u2192\n 60\u2192\n 61\u2192### Need\n 62\u2192\n 63\u2192*A difference between actual and desired situations*\n 64\u2192\n 65\u2192**Actual situation** (a two-sided pincer \u2014 stated as a scoped engineering\n 66\u2192judgment, not an absolute):\n 67\u2192\n 68\u2192- For these structures, obtaining trustworthy objective values from a\n 69\u2192 validated high-fidelity model would require substantial calibration of\n 70\u2192 interface, rate, contact, and defect behavior \u2014 and even heavy-duty\n 71\u2192 simulation falls short of the experiments we are running: peak transmitted\n 72\u2192 shock is a millisecond-scale transient that is notoriously difficult to\n 73\u2192 resolve numerically. This holds even for idealized cable\u2013strut tensegrities\n 74\u2192 in their actual engineering materials (steel or similar), not just for\n 75\u2192 printed PLA\u2013TPU.\n 76\u2192 \n 79\u2192- Exhaustive physical search fails on cost: a traditional design of\n 80\u2192 experiments over this relatively high-dimensional space (geometry + FDM\n 81\u2192 process parameters) would demand hundreds of specimens, each costing print\n 82\u2192 and test time.\n 83\u2192- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84\u2192 more complex structures \u2014 especially tensegrity *lattices* \u2014 become\n 85\u2192 extremely difficult and ultimately infeasible to hand-build.\n 86\u2192\n 87\u2192**Desired situation:**\n 88\u2192\n 89\u2192- Trustworthy performance data without hand assembly (easy creation of\n 90\u2192 tensegrity structures, scalable beyond a single prism).\n 91\u2192- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n 92\u2192 not the hundreds a traditional design of experiments would require.\n 93\u2192\n 94\u2192### Task\n 95\u2192\n 96\u2192*What I decided/was asked to do to address the need*\n 97\u2192\n 98\u2192Build a closed-loop design\u2013print\u2013test workflow that optimizes a **T3-prism\n 99\u2192tensegrity-inspired energy absorber together with its FDM processing\n100\u2192parameters** (e.g., temperature, print speed) directly from real, measured\n101\u2192impact data \u2014 maximizing energy absorbed per gram (specific energy\n102\u2192absorption) and compaction efficiency, subject to a cap on the peak force\n103\u2192transmitted to the payload. Extensions beyond the T3 prism are future work.\n104\u2192\n105\u2192### Main message\n106\u2192\n107\u2192*The one sentence I want my audience to remember*\n108\u2192\n109\u2192By closing the loop between multi-material 3D printing and Bayesian\n110\u2192optimization, we can optimize tensegrity-inspired energy absorbers directly\n111\u2192from real impact data \u2014 in dozens of prints, not the hundreds a traditional\n112\u2192design of experiments would demand.\n113\u2192\n114\u2192\n118\u2192\n119\u2192### Preview\n120\u2192\n121\u2192*A map of the body (ideally three points, max. five)*\n122\u2192\n123\u21921. When model calibration and exhaustive testing are both costly, each\n124\u2192 physical specimen must be chosen for its information value \u2014 so the\n125\u2192 measured experiment is our source of truth.\n126\u21922. Bayesian optimization makes physical experimentation affordable: it handles\n127\u2192 noisy measurements, small batches, and the constrained multi-objective\n128\u2192 trade-off \u2014 and Honegumi let us scaffold it in minutes.\n129\u21923. Multi-material additive manufacturing closes the loop: single-build PLA\u2013TPU\n130\u2192 co-fabrication turns each proposed design into test data in hours, with no\n131\u2192 joining of parts.\n132\u21924. *(Optional fourth point, if time allows)* The same closed loop points at\n133\u2192 what comes next: tensegrity lattices, flight-relevant materials, and\n134\u2192 safety hardware on Earth.\n135\u2192\n136\u2192## Body\n137\u2192\n138\u2192### Point 1\n139\u2192\n140\u2192When model calibration and exhaustive testing are both costly, each physical\n141\u2192specimen must be chosen for its information value.\n142\u2192\n143\u2192- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144\u2192 these structures, a sufficiently calibrated multiphysics model is expensive\n145\u2192 to build relative to the design campaign itself \u2014 and the hardest quantity,\n146\u2192 peak transmitted shock, is a millisecond-scale transient that even\n147\u2192 heavy-duty simulation struggles to resolve. The gap persists in the actual\n148\u2192 engineering materials a real lander would use (steel struts and cables),\n149\u2192 so this is not merely an artifact of printed PLA\u2013TPU.\n150\u2192- The alternative \u2014 sweeping the design space physically \u2014 fails on cost: a\n151\u2192 traditional design of experiments over geometry plus FDM process parameters\n152\u2192 would take hundreds of specimens, each needing print + test time.\n153\u2192- Therefore: treat the physical experiment as the authoritative source of\n154\u2192 objective values, and be ruthless about *which* experiments to run.\n155\u2192- Anticipate the FEA-veteran pushback (\"inconvenient \u2260 untrustworthy\"): frame\n156\u2192 it as a cost-and-dependence decision, and keep a backup slide with the\n157\u2192 literature (or our own validation) on simulation\u2013experiment discrepancy for\n158\u2192 impact transients.\n159\u2192\n160\u2192### Transition\n161\u2192\n162\u2192If every data point must be a real experiment, we need a method that extracts\n163\u2192the most from every specimen \u2014 we need to make the process radically more\n164\u2192sample-efficient.\n165\u2192\n166\u2192### Point 2\n167\u2192\n168\u2192Bayesian optimization makes physical experimentation affordable, even with\n169\u2192limited, noisy data.\n170\u2192\n171\u2192- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172\u2192 visual in the main talk; implementation detail goes to backup slides.\n173\u2192 Spoken version:\n174\u2192 > \"After each batch, the model estimates both expected performance and\n175\u2192 > uncertainty across the design space. The acquisition rule selects a small\n176\u2192 > next batch that is most likely to expand the feasible trade-off frontier,\n177\u2192 > while accounting for noisy tests and print failures.\"\n178\u2192- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179\u2192 motivate a noise-aware acquisition function (qNEHVI) rather than\n180\u2192 non-noise-aware alternatives. (Backup slide: how observation noise is\n181\u2192 actually modeled \u2014 replicates, noise identification \u2014 and budget-matched\n182\u2192 baselines such as Sobol/random sampling.)\n183\u2192- Small parallel batches: the surrogate proposes the next batch of specimens\n184\u2192 to print, balancing exploration and exploitation.\n185\u2192- Constrained multi-objective by construction: **objectives** = maximize\n186\u2192 specific energy absorption and compaction efficiency; **constraint** = peak\n187\u2192 transmitted force must stay under a cap; the deliverable is the *feasible*\n188\u2192 Pareto front between the two objectives among force-cap-satisfying designs.\n189\u2192 (Keep this formulation consistent everywhere \u2014 do not present \"SEA vs. peak\n190\u2192 force\" as the trade-off.)\n191\u2192- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192\u2192 this advanced task (noisy, batched, constrained, multi-objective) in\n193\u2192 minutes using [Honegumi](https://honegumi.readthedocs.io/) \u2014 the audience\n194\u2192 can do the same for their own problems; show the code-template picker\n195\u2192 briefly so they see how low the barrier is.\n196\u2192\n197\u2192### Transition\n198\u2192\n199\u2192The optimizer is no longer the bottleneck \u2014 the limit is now providing real\n200\u2192data quickly enough.\n201\u2192\n202\u2192### Point 3\n203\u2192\n204\u2192Multi-material additive manufacturing supplies that data fast \u2014 single-build\n205\u2192co-fabrication, with no joining of parts.\n206\u2192\n207\u2192- Single-build PLA\u2013TPU co-fabrication: rigid struts and flexible tension\n208\u2192 network in one print \u2014 no joining or hand assembly of members, which closes\n209\u2192 the \"easy creation\" gap from the Need and is what makes future lattices\n210\u2192 feasible at all. (Be precise: if support removal, trimming, or conditioning\n211\u2192 remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212\u2192 assembly.\")\n213\u2192- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214\u2192 as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215\u2192 pre-tension status) plus a short deformation sequence \u2014 so the EDL engineer\n216\u2192 doesn't file it as a \"crush structure\" and the grad student can tell it from\n217\u2192 an ordinary lattice.\n218\u2192- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219\u2192 tested specimen (quasi-static compression + instrumented drop-weight impact)\n220\u2192 in hours. (Backup slide: representative print-to-result time breakdown and\n221\u2192 which steps remain manual \u2014 candidate selection is automated; slicing,\n222\u2192 handling, and testing are not yet.)\n223\u2192- Now we have the data we need to complete the loop.\n224\u2192\n225\u2192### Point 4 (optional \u2014 future applications)\n226\u2192\n227\u2192*Include if timing allows; otherwise fold into the Conclusion.*\n228\u2192\n229\u2192The same closed loop transfers wherever tests are authoritative, each test is\n230\u2192costly, the design space is mixed, and several performance goals conflict:\n231\u2192\n232\u2192- Tensegrity *lattices* and more complex architectures \u2014 exactly where hand\n233\u2192 assembly becomes infeasible and single-build co-fabrication pays off.\n234\u2192- Migration from the PLA\u2013TPU proxy toward flight-relevant materials and\n235\u2192 impact regimes.\n236\u2192- Safety hardware on Earth (e.g., the tensegrity crutch tip \u2014 future work).\n237\u2192- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238\u2192 handling, testing) in the loop.\n239\u2192\n240\u2192### Evidence / results\n241\u2192\n242\u2192*What we found \u2014 ground the conclusion in shown data*\n243\u2192\n244\u2192Build the talk around a quantitative three-slide sequence (per the\n245\u2192mock-audience report; insert observed values once the campaign completes):\n246\u2192\n247\u21921. **Campaign ledger** \u2014 \"The campaign used N specimens across B adaptive\n248\u2192 batches under a fixed physical-test budget\": initialization vs. adaptive\n249\u2192 batches, replicates, print failures, cycle time, and the numerical value\n250\u2192 (and rationale) of the force cap.\n251\u21922. **Budget-matched baseline** \u2014 \"Adaptive selection improved the feasible\n252\u2192 design set faster than [predeclared baseline]\": performance vs. number of\n253\u2192 physical tests against Sobol/random sampling at the same budget, with\n254\u2192 uncertainty where available.\n255\u21923. **Measured feasible Pareto front** \u2014 specific energy absorption vs.\n256\u2192 compaction efficiency among designs satisfying the force cap, with\n257\u2192 replicate spread, marked infeasible designs, and specimen photos or\n258\u2192 force\u2013time traces for representative Pareto designs.\n259\u2192\n260\u2192Supporting raw-data candidates: drop-test force\u2013time traces, Sobol /\n261\u2192first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262\u2192\n263\u2192### Suggested 15-minute allocation\n264\u2192\n265\u2192*(From the mock-audience report \u2014 results get the largest block.)*\n266\u2192\n267\u2192| Time | Content |\n268\u2192|---|---|\n269\u2192| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270\u2192| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271\u2192| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272\u2192| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273\u2192| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274\u2192| 1 min | Limitations and transfer conditions |\n275\u2192| 1 min | Measured conclusion + bookend |\n276\u2192\n277\u2192Move kernel choices, encodings, and other implementation detail to backup\n278\u2192slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279\u2192[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280\u2192\n281\u2192## Closing\n282\u2192\n283\u2192### Review\n284\u2192\n285\u2192*A recap of the body, leading into the conclusion*\n286\u2192\n287\u2192Using real, measured impact data from additively manufactured\n288\u2192tensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\n289\u2192optimization routine \u2014 we rapidly iterate toward better energy absorbers.\n290\u2192This feedback loop makes tensegrity optimization dramatically faster: dozens\n291\u2192of prints instead of the hundreds a traditional design of experiments over\n292\u2192this design space would require.\n293\u2192\n294\u2192### Conclusion\n295\u2192\n296\u2192*What the above means to the audience in the end*\n297\u2192\n298\u2192Because the loop runs on physical measurements, it is useful precisely where\n299\u2192model calibration is not worth its cost \u2014 and the same closed-loop pattern\n300\u2192should transfer (a conditional claim, pending evidence) to other additively\n301\u2192manufactured architectures whose performance is dominated by\n302\u2192hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303\u2192*use this loop when tests are authoritative, each test is costly, the design\n304\u2192space is mixed, and several performance goals conflict.* For tensegrity\n305\u2192specifically, it opens a path from this T3 proxy study toward lattices,\n306\u2192flight-relevant materials, payload protection on other planets, and safety\n307\u2192hardware here on Earth (e.g., the crutch tip, as future work).\n308\u2192\n309\u2192### Close\n310\u2192\n311\u2192*A way to end the presentation clearly and elegantly*\n312\u2192\n313\u2192Return to the opening image: the next planetary lander's energy absorber \u2014 a\n314\u2192structure that has to survive not one impact but many \u2014 doesn't have to take\n315\u2192years of hand-built prototypes; with this loop, candidate designs can be\n316\u2192designed, printed, and tested against real impacts in weeks instead of months\n317\u2192or longer. End on the measured engineering result and its transfer condition,\n318\u2192then the bookend: every method that compresses design time moves our ventures\n319\u2192\u2014 on this planet and beyond \u2014 forward.\n320\u2192\n321\u2192---\n322\u2192\n323\u2192## Changes from Draft 2\n324\u2192\n325\u2192Applied from @sgbaird's inline review comments on PR #84 and the\n326\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327\u2192\n328\u2192**From the inline review comments:**\n329\u2192\n330\u21921. **Reusability in the hook** (line 22): the tensegrity lander survives\n331\u2192 multiple drops, unlike crushable/sacrificial alternatives.\n332\u21922. **Proxy-system framing** (lines 49, 87): PLA\u2013TPU would never fly; it is a\n333\u2192 proxy for prototyping a self-driving-lab-style workflow \u2014 stated in a\n334\u2192 scope note up front and re-stated right after the hook.\n335\u21923. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336\u2192 heavy-duty simulation falls short of the experiments (millisecond-scale\n337\u2192 peak shock), not that it merely costs too much.\n338\u21924. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339\u2192 complex structures and especially lattices are not.\n340\u21925. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341\u2192 (temperature, print speed, \u2026) only; the rest is explicitly future work.\n342\u21926. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343\u2192 literature search (or validation of experiments against simulation), plus\n344\u2192 the millisecond-resolution peak-shock argument.\n345\u21927. **Optional Point 4 added** (line 97): future applications, with a note it\n346\u2192 can fold into the Conclusion if time is short.\n347\u21928. **Steel-materials point** (line 104): simulations fall short for the\n348\u2192 actual engineering materials (steel struts/cables), not just printed\n349\u2192 PLA\u2013TPU.\n350\u21929. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351\u2192 in minutes, pitched so the audience sees they can use it too.\n352\u219210. **\"Thousands\" \u2192 \"hundreds\"** (line 173): baseline is now a traditional\n353\u2192 design of experiments over a high-dimensional space (hundreds), in the\n354\u2192 main message, Need, Point 1, and Review.\n355\u219211. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356\u2192 or longer\" for candidate designs.\n357\u2192\n358\u2192**From the mock-audience report's priority revisions:**\n359\u2192\n360\u219212. **Quantitative three-slide results sequence** (Priority 1): campaign\n361\u2192 ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front, with a\n362\u2192 fallback main-message wording if the campaign can't yet support the\n363\u2192 efficiency claim.\n364\u219213. **Anti-simulation premise replaced with a scoped cost decision**\n365\u2192 (Priority 2): Point 1 retitled to the information-value message;\n366\u2192 \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367\u2192 anticipated with a backup slide.\n368\u219214. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369\u2192 implementation detail moved to backup slides; Q&A prep pointed at the\n370\u2192 report's claims-vs-evidence table.\n371\u219215. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372\u2192 the main talk, machinery in backup slides; the misleading\n373\u2192 \"qNEHVI over standard expected improvement\" comparison replaced with\n374\u2192 \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375\u219216. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376\u2192 sequence in Point 3, so the structure isn't remembered as a generic crush\n377\u2192 structure or lattice.\n378\u219217. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379\u2192 co-fabrication / no joining of parts,\" with a note to list remaining\n380\u2192 manual steps and an automation-status caveat.\n381\u219218. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382\u2192 compaction efficiency, constraint = force cap, deliverable = *feasible*\n383\u2192 Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384\u219219. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385\u2192 transfer claim marked conditional, and the close ends on the measured\n386\u2192 result before the bookend.\n387\u2192\n388\u2192## Changes from Draft 1\n389\u2192\n390\u2192Feedback from the PR #84 review, applied in Draft 2:\n391\u2192\n392\u21921. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n393\u2192 dozens-not-thousands claim); body and message now carry the same story.\n394\u21922. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n395\u2192 manuscript's positioning (#75/#76) is that the physical experiment is the\n396\u2192 source of truth, explicitly avoiding calibrated FE simulation.\n397\u21923. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n398\u2192 parametric design space and avoid an over-claim to a design-automation\n399\u2192 audience.\n400\u21924. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401\u2192 energy absorption and compaction efficiency, cap peak transmitted force.\n402\u21925. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403\u2192 *and* the space is too big to brute-force \u2014 which makes BO + AM the\n404\u2192 inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405\u2192 above.)*\n406\u21926. **Point 1 given its own content** (the *why* behind the inefficiency)\n407\u2192 instead of restating the Need.\n408\u21927. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409\u2192 the specifics (noise-aware acquisition, small batches, multi-objective).\n410\u21928. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411\u2192 (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412\u2192 above.)*\n413\u21929. **Evidence/results slot added** before the Closing, so the Conclusion is\n414\u2192 grounded in shown data rather than three hedged \"may\"s.\n415\u219210. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n416\u2192 Ball Bot visual; verified video links included.\n417\u219211. **Close bookends the opening** (the next lander's absorber, designed in\n418\u2192 weeks) before the broader humankind line.\n419\u219212. **Spelling**: Doumont (was \"Duomont\").", + "name": "read", + "tool_call_id": "call_93haurOMDGiH2mQFFbaMaYKn", + "info": { + "start_ts": 8147.331065886, + "end_ts": 8147.336467845 + } + }, + { + "role": "tool", + "content": " 1\u2192# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n 2\u2192\n 3\u2192Video: (assigned in #83)\n 4\u2192\n 5\u2192Transcribed from @me-madsen's handwritten notes\n 6\u2192([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7\u2192dated 2026-07-15).\n 8\u2192\n 9\u2192## Overview\n10\u2192\n11\u2192A presentation is all about the right content, displayed the right way, at the\n12\u2192right time.\n13\u2192\n14\u2192- Verbal communication is sequential.\n15\u2192- Message is *interpretation* of the information.\n16\u2192 - Ensure the audience can get the most out of what you're doing.\n17\u2192 - Don't just give them information \u2014 help them understand it, interpret it,\n18\u2192 and internalize it.\n19\u2192\n20\u2192## Doumont's suggested rules for a presentation\n21\u2192\n22\u21920. Ensure you have **messages** for your audience, not just words.\n23\u21921. **Adapt to your audience.**\n24\u2192 - Think like the audience, not the speaker.\n25\u21922. **Maximize signal-to-noise ratio.**\n26\u2192 - Reduce noise (animations, laser pointers, anything that pulls attention\n27\u2192 away from your message).\n28\u21923. **Use effective redundancy.**\n29\u2192 - Tell your message in word *and* on slide: someone deaf should understand\n30\u2192 your messages just as well as someone blind.\n31\u2192 - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32\u2192 date).\n33\u2192\n34\u2192## When creating slides\n35\u2192\n36\u2192- If the audience doesn't need it, why put it on the slides?\n37\u2192- People shouldn't have to count the items on a slide to know how many are\n38\u2192 there.\n39\u2192- Don't use your slides as your \"presentation notes.\"\n40\u2192 - Don't show your notes to the audience.\n41\u2192 - **A bad slide is worse than no slide at all.**\n42\u2192- **One message per slide** is a good rule of thumb.\n43\u2192 - Prioritize the *so what* (or *therefore what*) of your message.\n44\u2192- Make it easy to interpret the information on the slide:\n45\u2192 - 3\u20135 items at most.\n46\u2192 - Align the title left.\n47\u2192 - Do not clutter slides with repetitive or unnecessary information: logos,\n48\u2192 affiliation, date, slide numbers.\n49\u2192- Doumont's suggested workflow:\n50\u2192 1. **Start with the title.**\n51\u2192 - Use a full sentence in the title.\n52\u2192 - The title is the main message of the slide.\n53\u2192 - Make it easy to read: left-aligned, with intentional line breaks.\n54\u2192 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55\u2192- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56\u2192\n57\u2192## General preparation\n58\u2192\n59\u2192- Planning for audience, content, and structure matters more than materials\n60\u2192 (i.e., slides).\n61\u2192- Try using one sheet of paper for presentation preparation.\n62\u2192- It's most important that your presentation is:\n63\u2192 - well planned, structured, and delivered;\n64\u2192 - **if you don't have a lot of time to prepare, don't focus on making\n65\u2192 slides.**\n66\u2192- Practice the presentation a few times. Practice at least once *without* your\n67\u2192 slideshow!\n68\u2192- If doing a handout, keep it separate from the slides you're preparing.\n69\u2192- Impress the audience with your body.\n70\u2192 - Use gestures, not laser pointers.\n71\u2192 - \"Control\" the audience with your body \u2014 keep their attention on your\n72\u2192 presentation.\n73\u2192\n74\u2192## Impressions / what to apply to the IDETC presentation\n75\u2192\n76\u2192\n77\u2192\n78\u2192## Questions / things to discuss\n79\u2192\n80\u2192", + "name": "read", + "tool_call_id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "info": { + "start_ts": 8147.331133416, + "end_ts": 8147.338304015 + } + }, + { + "role": "tool", + "content": " 1\u2192\\documentclass[10pt]{article}\n 2\u2192\n 3\u2192% --- Packages ---\n 4\u2192\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5\u2192\\usepackage[hidelinks]{hyperref}\n 6\u2192\\usepackage{parskip}\n 7\u2192\\usepackage{graphicx}\n 8\u2192\\usepackage{caption}\n 9\u2192\\usepackage{titlesec}\n 10\u2192\\usepackage{wrapfig}\n 11\u2192\\usepackage[numbers,sort&compress]{natbib}\n 12\u2192\n 13\u2192% --- Formatting ---\n 14\u2192\\setlength{\\parskip}{0.2em}\n 15\u2192\\setlength{\\parindent}{0em}\n 16\u2192\\titleformat*{\\section}{\\large\\bfseries}\n 17\u2192\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18\u2192\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19\u2192\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20\u2192\\setlength{\\emergencystretch}{2em}\n 21\u2192\\setlength{\\intextsep}{0.2em}\n 22\u2192\\bibliographystyle{abbrvnat}\n 23\u2192\n 24\u2192\\begin{document}\n 25\u2192\n 26\u2192\\begin{center}\n 27\u2192{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28\u21923D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29\u2192\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30\u2192\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31\u2192\\end{center}\n 32\u2192\n 33\u2192\\vspace{0.4em}\n 34\u2192\n 35\u2192\\section*{Abstract}\n 36\u2192Tensegrity-inspired architectures---rigid struts suspended within a\n 37\u2192continuous flexible network---can exhibit tunable nonlinear\n 38\u2192force--displacement responses and favorable energy absorption per unit\n 39\u2192mass, motivating their use in lightweight energy-absorbing and protective\n 40\u2192structures\n 41\u2192\\citep{skelton2009tensegrity,pajunen2019design}.\n 42\u2192Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43\u2192struts and flexible (TPU) elements in a single build\n 44\u2192\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45\u2192space---strut geometry, tension-element cross-section, connectivity\n 46\u2192topology, and unit-cell tiling---is too large to explore by trial and\n 47\u2192error. We present a closed-loop experimental campaign that uses\n 48\u2192Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49\u2192to drive a design--print--test workflow operating directly on physical\n 50\u2192measurements, without relying on calibrated finite-element simulation\n 51\u2192for objective evaluation.\n 52\u2192\n 53\u2192\\section{Introduction and Motivation}\n 54\u2192\n 55\u2192\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56\u2192\\centering\n 57\u2192\\vspace{-0.4em}\n 58\u2192\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59\u2192\\captionsetup{font=small}\n 60\u2192\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61\u2192Candidate tensegrity-inspired unit cells are instantiated from a\n 62\u2192parameter vector, fabricated by multi-material FDM, and tested under\n 63\u2192quasi-static compression and drop-weight impact. Measured peak force,\n 64\u2192specific energy absorption (SEA), and compaction efficiency update a\n 65\u2192Gaussian-process surrogate that proposes the next batch of designs.}\n 66\u2192\\label{fig:overview}\n 67\u2192\\vspace{-0.4em}\n 68\u2192\\end{wrapfigure}\n 69\u2192\n 70\u2192Tensegrity structures combine compression-only members with a\n 71\u2192pre-tensioned tension network to achieve stiffness without rigid\n 72\u2192joinery~\\citep{skelton2009tensegrity}. Their\n 73\u2192nonlinear, often load-limiting force--displacement response makes them\n 74\u2192attractive for impact mitigation, packaging, and wearable protective\n 75\u2192gear~\\citep{pajunen2019design}. Idealized\n 76\u2192cable--strut tensegrities are difficult to manufacture at scale, but\n 77\u2192\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78\u21923D printing reproduce key tensegrity behaviors---including\n 79\u2192post-buckling stability and load-limiting force--displacement\n 80\u2192response~\\citep{pajunen2019design}---motivating our extension to\n 81\u2192multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82\u2192elements. PLA provides rigid struts; TPU provides flexible,\n 83\u2192rate-dependent tension elements whose viscoelasticity is expected to\n 84\u2192contribute hysteretic energy dissipation under impact loading,\n 85\u2192consistent with the energy-absorbing behavior reported for rigid--TPU\n 86\u2192multi-material prints~\\citep{khatri2024energy}.\n 87\u2192\n 88\u2192The challenge is that small geometric and topological changes can\n 89\u2192produce large changes in the measured response, and high-fidelity\n 90\u2192simulation of FDM specimens is computationally expensive and often\n 91\u2192struggles to capture physical defects such as interfacial slip and\n 92\u2192TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93\u2192trial-and-error sweeps over the joint design space are also infeasible:\n 94\u2192even modest parameter resolutions yield thousands of candidates, each\n 95\u2192requiring print and test time. We instead treat the physical\n 96\u2192experiment as the source of truth and use BO to select which specimens\n 97\u2192are most informative to fabricate next---an approach used in related\n 98\u2192closed-loop materials and structures\n 99\u2192campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100\u2192Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101\u2192set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102\u2192designs using parallel, noisy multi-objective BO driven by measured\n103\u2192$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104\u2192\n105\u2192\\section{Approach}\n106\u2192\\subsection{Closed-Loop Workflow}\n107\u2192Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108\u2192proceeds in four steps: (i)~a parameterized unit-cell design is\n109\u2192instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110\u2192is sliced and printed on a multi-material FDM system; (iii)~it is\n111\u2192tested under quasi-static compression and instrumented drop-weight\n112\u2192impact; and (iv)~the resulting performance metrics update a\n113\u2192Gaussian-process (GP) surrogate, which a batch acquisition function\n114\u2192queries to recommend the next set of candidates. Surrogate updating\n115\u2192and experiment selection are automated; specimen handling, slicing,\n116\u2192and test setup remain manual.\n117\u2192\n118\u2192\\subsection{Design Space}\n119\u2192We parameterize a family of unit cells using a core-wrapping\n120\u2192architecture in which rigid PLA struts are enclosed by continuous TPU\n121\u2192skins; the wrapping rationale follows multi-material FDM strategies in\n122\u2192which rigid panels are wrapped by stretchable soft material to prevent\n123\u2192delamination and improve interfacial bonding at the rigid--flexible\n124\u2192interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125\u2192comprises continuous geometric variables (strut diameter and length,\n126\u2192tension-element width and thickness), an integer strut count per unit\n127\u2192cell, and two categorical variables (connectivity topology, unit-cell\n128\u2192tiling pattern). Categorical choices are encoded explicitly rather\n129\u2192than embedded in a continuous metric, and bounds on the continuous\n130\u2192variables are chosen to remain within the printer's resolution and\n131\u2192overhang limits and to keep specimens in a common form factor for the\n132\u2192test fixtures.\n133\u2192\n134\u2192\\subsection{Surrogate and Acquisition}\n135\u2192The GP surrogate is trained on three measured performance metrics:\n136\u2192peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137\u2192(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138\u2192objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139\u2192likelihood with priors to avoid pathological length scales at small $n$.\n140\u2192Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141\u2192interfacial defects motivates our use of q-noisy expected hypervolume\n142\u2192improvement (qNEHVI), which explicitly accounts for observation noise\n143\u2192in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144\u2192expected-improvement variants. Print failures are treated as a\n145\u2192probabilistic feasibility constraint: the acquisition score for each\n146\u2192candidate is its qNEHVI improvement weighted by the GP-estimated\n147\u2192probability of successful fabrication, following recent constrained\n148\u2192multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149\u2192Where applicable, we use numerically stable log-space variants of\n150\u2192improvement-based acquisitions to mitigate vanishing-gradient\n151\u2192pathologies as data and constraints\n152\u2192accumulate~\\citep{ament2023logei}.\n153\u2192\n154\u2192\\section{Methods}\n155\u2192\\subsection{Fabrication}\n156\u2192Specimens are fabricated on a multi-material FDM system capable of\n157\u2192co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158\u2192Print parameters (temperature, line width, infill, retraction) are\n159\u2192held fixed within a batch to isolate the effect of the design\n160\u2192variables. Each specimen is weighed and dimensionally inspected prior\n161\u2192to testing so that mass and geometric deviations are available as\n162\u2192covariates.\n163\u2192\n164\u2192\\subsection{Mechanical Testing}\n165\u2192Quasi-static compression is performed on a screw-driven load frame at\n166\u2192a fixed nominal strain rate, with force--displacement recorded to\n167\u2192densification. Drop-weight impact testing uses an instrumented tup at\n168\u2192a fixed impact energy; transmitted force is captured at high sample\n169\u2192rate, and specimens are imaged before and after impact to document\n170\u2192failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171\u2192unit mass, and compaction efficiency $\\eta$, defined consistently\n172\u2192across the campaign. A control specimen from an earlier batch is\n173\u2192retested periodically to monitor TPU batch-to-batch and printer drift.\n174\u2192\n175\u2192\\section{Expected Outcomes and Discussion}\n176\u2192We will report how BO-selected designs evolve across iterations, how\n177\u2192GP surrogate predictions and uncertainties change as data accumulate,\n178\u2192and which Pareto-efficient trade-offs emerge between SEA and peak\n179\u2192transmitted force within the explored design space. We will also\n180\u2192discuss practical lessons for operating the closed-loop workflow,\n181\u2192including handling of print failures, batch-to-batch variability in\n182\u2192TPU response, the influence of the rigid--flexible interface on\n183\u2192energy absorption, and the exploration--exploitation balance of the\n184\u2192acquisition function under realistic experimental noise. We expect\n185\u2192the campaign to clarify where physical experimentation is genuinely\n186\u2192required versus where simpler analytical or multi-fidelity surrogate\n187\u2192shortcuts~\\citep{mo2023accelerated} could be\n188\u2192substituted in future work.\n189\u2192\n190\u2192\\section{Conclusion}\n191\u2192This work demonstrates a partially automated, experiment-driven\n192\u2192closed-loop pipeline for designing multi-material 3D-printed\n193\u2192tensegrity-inspired energy absorbers, with surrogate updating and\n194\u2192candidate selection automated and fabrication and testing kept manual.\n195\u2192By updating a GP surrogate directly from physical measurements, the\n196\u2192workflow avoids direct dependence on calibrated finite-element\n197\u2192simulation for objective evaluation while still providing principled,\n198\u2192multi-objective design recommendations. The same closed-loop pattern is\n199\u2192expected to transfer to other multi-material additively manufactured\n200\u2192architectures whose performance is dominated by hard-to-simulate\n201\u2192effects.\n202\u2192\n203\u2192{\\tiny\n204\u2192\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205\u2192\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206\u2192% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207\u2192% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208\u2192\\providecommand{\\doi}[1]{}\n209\u2192\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210\u2192\\bibliography{references}\n211\u2192}\n212\u2192\n213\u2192\\end{document}", + "name": "read", + "tool_call_id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "info": { + "start_ts": 8147.332633466, + "end_ts": 8147.339043095 + } + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\", \"offset\": 174, \"limit\": 100}", + "name": "read" + } + } + ], + "info": null + }, + { + "role": "tool", + "content": "175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "info": { + "start_ts": 8153.825818486, + "end_ts": 8153.828437556 + } + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8In1tJq32AD2tTyHX3tmIlLf", + "type": "function", + "function": { + "arguments": "{\"answer\": \"# Mock audience report: IDETC-CIE 2026 slide deck, Draft 1\\n\\n## Executive read\\n\\nThe visible deck has a clear visual spine: **motivation \\u2192 slow iteration \\u2192 Bayesian optimization \\u2192 multi-material printing \\u2192 impact testing \\u2192 closed loop \\u2192 future applications**. The strongest section is slides 8\\u201310, where fabrication, testing, and adaptive selection finally appear as one workflow.\\n\\nBut Draft 1 does not yet deliver the technical case promised by the agreed outline. The visible deck omits four things a DAC-10 audience will need:\\n\\n1. **Honest scope:** no visible slide says that the PLA\\u2013TPU T3 prism is a proxy system rather than flight hardware.\\n2. **A precise optimization problem:** the six variables, two objectives, force constraint, noise treatment, and fabrication failures are not intelligible on slide 7.\\n3. **Quantitative evidence:** the sole results slot is hidden and empty, yet slides 10 and 12 already claim acceleration and transfer.\\n4. **A measured conclusion:** the talk jumps from the workflow to speculative applications without showing a campaign ledger, comparison against a budget-matched baseline, or feasible Pareto front.\\n\\nThe slide titles generally follow Doumont\\u2019s full-sentence-message rule, but several messages outrun the evidence. The deck also departs substantially from the Draft 3 timing plan, which reserves **5 of 15 minutes for results**. As built, results receive zero visible slides.\\n\\n---\\n\\n# P0. The program manager in the audience\\n\\n## (a) My reaction in the room\\n\\nSlide 1 tells me the team wants to work faster, but it does not tell me what is being optimized or what has actually been built. The title sounds aspirational rather than traceable to a project deliverable. I immediately want the formal title from the abstract or a shorter version of it.\\n\\nThe Super Ball Bot footage on slide 2 is an effective hook. I understand why reusable impact attenuation matters. But the slide creates a scope risk: I am looking at planetary landing hardware while the actual work is a printed PLA\\u2013TPU T3 proxy. Because that distinction is not stated immediately, I may spend the rest of the talk evaluating the work against flight-readiness criteria the project never intended to meet.\\n\\nSlide 4 gives me the business problem, but not a baseline. \\u201cSlow and resource intensive\\u201d needs a number: hours per print-test cycle, number of factors, or the candidate count under a declared design of experiments. Without that, I cannot assess whether the proposed loop attacks the dominant schedule driver.\\n\\nSlide 7 is where I expect the project definition. Instead, \\u201c6 variables,\\u201d \\u201c2 types of input data,\\u201d and \\u201c2 objectives\\u201d read like inventory labels. I cannot reconstruct the statement of work from them. I need to see what is automated, what remains manual, what the force cap is, and what constitutes a successful campaign.\\n\\nSlides 8 and 9 make the project tangible. A labeled specimen and an actual drop test would build confidence. Yet \\u201csingle-build\\u201d and \\u201cno joining\\u201d need a process qualification caveat, and I need the measurement chain to be explicit. The abstract refers to an instrumented tup and force\\u2013time data; slide 9 says accelerometers. Those may be compatible, but the deck presently leaves the relationship unclear.\\n\\nSlide 10 is the right management view of the system, but \\u201csignificantly accelerate\\u201d is a result claim placed on a workflow diagram. At this point I ask, \\u201cCompared with what, by how much, and over how many specimens?\\u201d Then the deck skips the hidden results slide and goes directly to future applications. That is the point where my confidence drops sharply. The project is asking me to extrapolate before closing the evidence loop.\\n\\nSlide 12 is too early and too broad. A crutch tip and lattice can be legitimate next phases, but only after the team shows what the present campaign established and what technical risks remain. The blank questions slide gives me no durable take-home message.\\n\\n### PM stage-risk register\\n\\n| Risk | Likelihood | Consequence | Mitigation |\\n|---|---:|---:|---|\\n| Results are incomplete by conference lock | Medium to high | Critical: central claim becomes unsupported | Set a dated campaign freeze; predeclare a fallback message and minimum publishable result package |\\n| Embedded Super Ball Bot or drop-test video fails | Medium | Moderate: weak opening or broken pacing | Use local, trimmed files; retain a still-frame fallback on every video slide |\\n| Talk overruns because several videos and explanations are improvised | High | High: results or conclusion gets cut | Script videos to 10\\u201315 seconds total each; rehearse to 13:30; assign 5 minutes to results |\\n| Scope is mistaken for flight-hardware validation | High | High: credibility damage in Q&A | Put \\u201cPLA\\u2013TPU T3 proxy; not flight hardware\\u201d on slide 2 or the next slide |\\n| Acceleration claim lacks a budget-matched comparator | High | Critical for DAC review audience | Show Sobol/random or another predeclared baseline at the same physical-test budget |\\n| Measurement chain is challenged | Medium | High | Add a methods schematic and backup slide on calibration, sampling, filtering, and metric extraction |\\n| \\u201cNo joining\\u201d or \\u201cnear autonomous\\u201d is read as full automation | Medium | Moderate | List manual steps explicitly: slicing, handling, setup, and testing |\\n| Animations distract or fail to communicate without narration | Medium | Moderate | Prefer one short clip plus static annotated frames; follow Doumont\\u2019s signal-to-noise rule |\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey are building a loop that prints PLA\\u2013TPU tensegrity-like absorbers, drop-tests them, and lets Bayesian optimization choose the next print.\\u201d\\n\\nThat retains the basic workflow but loses the intended **dozens rather than hundreds** claim because the deck never demonstrates it. I would also probably describe the work as \\u201clander research,\\u201d which is a distortion caused by the missing proxy-system statement.\\n\\n## (c) My top three Q&A questions\\n\\n1. What is the dated plan to complete the campaign, baseline comparison, and repeat testing before the conference slide freeze?\\n2. Which exact operations are automated today, which remain manual, and what is the measured end-to-end cycle time per adaptive batch?\\n3. What evidence will justify \\u201cdozens rather than hundreds,\\u201d and what will you claim if the budget-matched baseline does not show an advantage?\\n\\n## (d) My most pointed objection\\n\\n**You are presenting a claimed acceleration without showing a completed campaign, a comparator, or even a visible results slide.**\\n\\nUnanswered, this is **critical**. I would not fund a scale-up phase from this deck. I might fund completion of the present campaign if the team showed a disciplined schedule, predefined success criteria, and a credible fallback.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 11.** It must become a real, scheduled results package, preferably three slides: campaign ledger, budget-matched baseline, and feasible Pareto front.\\n- **Hidden-slide decision:** **Unhide slide 11**, but not in its current empty form. The talk cannot make its principal claim without it.\\n\\n### PM slide-level punch list, ordered by schedule criticality\\n\\n**Must be fixed first**\\n\\n1. **Slide 11/results sequence:** assign owners and dates for data freeze, quality control, baseline computation, replicate analysis, and final plots. Define fallback wording now.\\n2. **Slide 2:** add the explicit scope line, \\u201cThis study uses a printed PLA\\u2013TPU T3 prism as a proxy for developing the workflow; it is not flight hardware.\\u201d\\n3. **Slide 7:** replace category counts with the actual optimization problem, test budget, batch size, objectives, constraint, and noise/failure treatment.\\n4. **Slide 10:** remove \\u201csignificantly\\u201d until slide 11 demonstrates a quantitative improvement; show automation boundaries and the loop\\u2019s measured cycle time.\\n5. **Slides 2 and 9:** download and trim media, test on the conference laptop, and put a static fallback image on each slide.\\n\\n**Then fix**\\n\\n6. **Slide 4:** quantify the current cost or candidate count and identify the bottleneck.\\n7. **Slide 8:** label PLA, TPU, interface, and any post-processing; qualify \\u201cno joining.\\u201d\\n8. **Slide 9:** reconcile accelerometers with the instrumented-tup description and show how force, energy absorption, and compaction efficiency are obtained.\\n9. **Slide 12:** convert the application collage into a conditional next-phase roadmap with gates.\\n10. **Slide 13:** end on the measured result and deployment rule, not a blank Q&A screen.\\n\\n**Nice to have**\\n\\n11. Replace decorative or looping media on slide 4 with one static process image unless it communicates a measured delay.\\n12. Add backup slides on Bayesian optimization implementation, finite-element-analysis positioning, repeatability, force-cap rationale, and print failures.\\n\\n---\\n\\n# P1. The skeptical Bayesian-optimization insider\\n\\n## (a) My reaction in the room\\n\\nThe opening is competent but generic for a design-automation conference. Slides 1\\u20134 set up an expensive physical experiment, which is a valid application class for Bayesian optimization (BO). I become interested when the presenter reaches slide 7, because that is where methodological seriousness should appear.\\n\\nInstead, slide 7 says only \\u201c6 variables,\\u201d \\u201c2 types of input data,\\u201d and \\u201c2 objectives.\\u201d I do not learn the variables, their types, how categorical choices are encoded, whether process parameters are optimized, how noisy observations enter the Gaussian processes, how replicates identify noise, how failures are modeled, or how the force cap enters the acquisition. The title says BO handles noisy data, but the visual does not substantiate that message.\\n\\nThere is also an unresolved specification conflict across the supplied materials. The agreed outline says the study jointly optimizes T3 geometry and FDM processing parameters. The abstract\\u2019s design vector lists four continuous geometric variables, one integer, and two categorical variables, apparently **seven variables**, while its fabrication section says print parameters are held fixed within a batch. The slide says **six variables**. A DAC audience will notice if the spoken details do not reconcile these versions.\\n\\nSlides 8\\u201310 explain why physical feedback is useful, but they do not establish why qNEHVI is the right algorithm or whether adaptive selection helped. The visible deck never names qNEHVI, the feasible Pareto front, the peak-force constraint, batch size, initialization, or test budget. Hiding slide 5 is reasonable if it is only a generic \\u201cwhat is BO?\\u201d slide copied from another talk. But slide 7 must then carry the application-specific methodological content.\\n\\nWhen slide 10 says the workflow \\u201csignificantly accelerate[s]\\u201d optimization, I wait for a performance-versus-budget curve. It never arrives. Slide 12 then generalizes to other applications. My default conclusion is exactly the skeptical one posed in the prompt: this appears to be off-the-shelf qNEHVI attached to an interesting physical apparatus, with no demonstrated algorithmic or empirical advantage yet.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cIt was an experimental self-driving-lab-style application of batch multi-objective BO to printed impact absorbers, but the talk did not show whether BO beat space-filling sampling.\\u201d\\n\\nThat differs from the intended message because I would not repeat \\u201cdozens, not hundreds\\u201d without a budget-matched baseline.\\n\\n## (c) My top three Q&A questions\\n\\n1. What are the exact decision variables and encodings, and why do the deck, scope statement, and abstract appear to disagree on whether there are six or seven variables and whether FDM parameters are optimized?\\n2. How is observation noise estimated: fixed noise, learned heteroscedastic noise, technical replicates, or repeated control specimens? What is the replicate allocation?\\n3. Against what budget-matched baseline do you evaluate qNEHVI, and what metric do you report: feasible hypervolume, best feasible objective values, or attainment probability versus physical tests?\\n\\n## (d) My most pointed objection\\n\\n**Nothing visible distinguishes a validated BO contribution from an untested application of standard qNEHVI.**\\n\\nUnanswered, this is **critical for DAC-10 reception**. Application novelty can be enough, but only if the physical campaign is rigorous and the adaptive method is compared fairly under the same test budget.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 7.** Replace the black-box inventory with a visual of posterior mean plus uncertainty \\u2192 constrained batch acquisition \\u2192 next physical tests, accompanied by the exact objectives and force constraint.\\n- **Hidden-slide decision:** **Keep slide 5 hidden** as a standalone generic BO tutorial. Merge one plain-language BO sentence into slide 7 and put qNEHVI details in backup.\\n\\n---\\n\\n# P2. The aerospace practitioner\\n\\n## (a) My reaction in the room\\n\\nThe Super Ball Bot hook works because I know the lineage. Reusable landing structures are interesting. But the title\\u2019s claim that tensegrity provides \\u201crobust, reusable solutions\\u201d is broader than what this deck demonstrates. A lab drop of a polymer proxy is not evidence of robustness in thermal extremes, vacuum, dust, radiation, repeated impacts, or off-axis terrain contact.\\n\\nThe deck never immediately tells me that the actual specimen is **tensegrity-inspired**, lacks ideal compression-only and tension-only membership, and is not intended to fly. That makes slide 2 vulnerable to looking like borrowed aerospace relevance.\\n\\nSlide 4 is plausible but too broad. Tensegrity design can be difficult, but aerospace development is not made slow only by design-space search. Verification, environmental qualification, uncertainty, packaging, guidance, and system integration matter. A faster specimen loop is useful, but it is one subsystem tool.\\n\\nI am not helped much by slide 7. I need BO translated into engineering behavior: it chooses the next small batch based on predicted performance and uncertainty while rejecting designs expected to violate a payload-force limit. The slide should name that limit and show the actual trade-off.\\n\\nSlides 8 and 9 are the first ones that answer \\u201cwhat did you build and how did you hit it?\\u201d A labeled specimen, deformation sequence, drop mass, speed or energy, orientation, and force measurement would hold my attention. Two simultaneous looping videos may not. I also want to know whether the structure rebounds, survives repeated drops, and retains its response, because the opening emphasizes reuse.\\n\\nSlide 10 is understandable. Slide 12 loses me because it jumps from a T3 polymer coupon to lattices and crutch tips without stating transfer conditions or technology gates.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey use an optimizer to choose which 3D-printed soft\\u2013rigid impact absorber to drop-test next, with the eventual motivation of reusable landing structures.\\u201d\\n\\nThat is fairly close, but I would deliberately downgrade \\u201ctensegrity\\u201d to \\u201ctensegrity-inspired absorber\\u201d and treat planetary landing as motivation only.\\n\\n## (c) My top three Q&A questions\\n\\n1. What makes the printed T3 specimen mechanically tensegrity-inspired rather than an ordinary compliant crush structure, and is it actually prestressed?\\n2. What impact energy, mass, orientation, and transmitted-force cap are used, and how do they relate to a payload-protection requirement?\\n3. How does performance change over repeated impacts, and what failure modes or permanent set limit the claimed reusability?\\n\\n## (d) My most pointed objection\\n\\n**The opening borrows flight relevance and reusability, but the visible deck never establishes the proxy boundary or shows repeated-impact evidence.**\\n\\nUnanswered, this is **highly damaging** to credibility with aerospace practitioners, though it does not invalidate the optimization workflow itself.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 2.** Keep the hook, but place a photograph of the actual T3 specimen beside the Super Ball Bot and state \\u201cmotivation, not demonstrated flight readiness.\\u201d\\n- **Hidden-slide decision:** **Keep slide 3 hidden.** A generic elbow-friction GIF would distract and oversimplify. Merge a precise tensegrity-mechanism annotation into slide 8 instead.\\n\\n---\\n\\n# P3. The additive-manufacturing/materials researcher\\n\\n## (a) My reaction in the room\\n\\nSlides 1\\u20134 give me a recognizable motivation for rapid experimental iteration. My interest rises on slide 8 because multi-material fused deposition modeling is where the claimed loop can succeed or fail.\\n\\nThe message \\u201csingle-build co-fabrication, with no joining of parts\\u201d is attractive, but it needs exact boundaries. Is there support removal, trimming, conditioning, manual tensioning, or fixture insertion? Does \\u201csingle build\\u201d mean one tool-change sequence on one machine, or truly co-deposited interfaces throughout the architecture? Are PLA and thermoplastic polyurethane (TPU) mechanically interlocked, chemically bonded, or merely in contact?\\n\\nThe abstract provides more useful information than the slide: a core-wrapping architecture is intended to resist delamination, specimens are weighed and dimensionally inspected, and a control is retested to monitor drift. Those are exactly the details the talk should surface. Slide 8\\u2019s planned printing video will not replace a labeled cross-section or interface image.\\n\\nSlide 9 raises repeatability questions. TPU is rate-, temperature-, moisture-, and history-dependent. \\u201cLimited, noisy data\\u201d on slide 7 acknowledges noise but does not show the control plan. I need batch identity, drying/conditioning, build position, print orientation, machine state, interface failures, and replicates.\\n\\nThe deck also appears inconsistent about process parameters. The agreed scope says FDM processing parameters are optimized with structure, but the abstract says temperature, line width, infill, and retraction are held fixed within a batch. This could mean they vary between batches or are controlled rather than optimized, but the deck must say which.\\n\\nI like the physical loop on slide 10. I do not yet accept that multi-material printing \\u201csupplies data fast\\u201d until cycle time and failure rate are shown.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey co-print PLA and TPU impact specimens and use each mechanical test to guide the next design batch.\\u201d\\n\\nI would probably omit \\u201cno joining\\u201d and \\u201cdozens rather than hundreds\\u201d until I saw interface qualification, failure accounting, and campaign data.\\n\\n## (c) My top three Q&A questions\\n\\n1. How is the PLA\\u2013TPU interface designed and qualified, and what fraction of specimens fail by delamination or print defects?\\n2. Which material, machine, conditioning, and build-position variables are controlled, measured as covariates, or optimized?\\n3. How many true replicates and repeated control specimens are used to separate BO improvement from printer drift and TPU batch variation?\\n\\n## (d) My most pointed objection\\n\\n**The deck treats co-fabrication as an enabling fact without showing interface integrity, repeatability, or the remaining manual processing steps.**\\n\\nUnanswered, this is **highly damaging** because print variation is part of both the physical mechanism and the statistical noise model.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 8.** Replace or supplement the printing clip with an annotated specimen and interface detail: PLA struts, continuous TPU network, deposition/wrapping strategy, prestress status, post-processing, and observed failure modes.\\n- **Hidden-slide decision:** **Keep slide 3 hidden.** Its proposed generic GIFs do not answer the materials questions. Put the relevant deformation mechanism on slide 8.\\n\\n---\\n\\n# P4. The first-year graduate student\\n\\n## (a) My reaction in the room\\n\\nSlide 1 sounds positive, and slide 2 gives me a memorable reason to care. I understand that the lander survives an impact because the structure itself deforms. But I do not yet know what \\u201ctensegrity\\u201d means, and the hidden slide 3 would not necessarily fix that: an elbow GIF and a squishing GIF could leave me with an inaccurate friction-versus-elasticity story.\\n\\nSlide 4 is easy to follow. The three words \\u201cIteration, Building, Testing\\u201d form a simple problem statement, although the images need to make their relationship obvious.\\n\\nI get lost on slide 7. Six variables of what? What are the two input-data types? Are the two objectives \\u201cabsorb energy\\u201d and \\u201creduce force,\\u201d or something else? I do not know what a black box means in this setting. The title says Bayesian optimization works with noisy data, but I have not been shown how it decides anything.\\n\\nSlide 8 helps because I can see printing. Still, I need labels that distinguish the rigid and flexible parts and one sentence explaining why the object is \\u201ctensegrity-inspired.\\u201d Slide 9 is intuitive if the clips are synchronized and annotated. I would understand even better if the slide drew the metric extraction directly on a force\\u2013time or force\\u2013displacement trace.\\n\\nSlide 10 is the clearest slide in the deck. Printing \\u2192 testing \\u2192 optimizer \\u2192 next print is the idea I will remember. But it arrives after the technical terms rather than before them. I would benefit from seeing a version of this loop before slide 7 and then revisiting it after the methods.\\n\\nSlide 12 feels like a new presentation. I have not seen whether the current system worked, so the crutch tip and lattice do not feel earned. Then the talk ends on a blank question slide rather than telling me the answer.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey repeatedly print and drop-test flexible 3D structures, and a machine-learning model decides what to print next.\\u201d\\n\\nMy retelling loses the two objectives, the force constraint, and the \\u201cdozens rather than hundreds\\u201d quantitative point. I might incorrectly call the method generic machine learning rather than Bayesian optimization.\\n\\n## (c) My top three Q&A questions\\n\\n1. What exactly is tensegrity, and which parts of your printed specimen are in tension and compression?\\n2. What are the six things the optimizer can change, and what are the two scores it is trying to improve?\\n3. How does the optimizer know whether to try something uncertain rather than the design it currently thinks is best?\\n\\n## (d) My most pointed objection\\n\\n**The deck names the ingredients before giving me a plain-language map of how they work together.**\\n\\nUnanswered, this is **moderately damaging**. I retain the loop but lose the actual research question and technical contribution.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 7.** It needs one concrete candidate, predicted performance plus uncertainty, and the next selected batch, not abstract counts around a black box.\\n- **Hidden-slide decision:** **Unhide neither slide 3 nor slide 5 as written.** If forced to choose one, **keep slide 5 hidden** and integrate its one-sentence concept into a redesigned slide 7. Generic explanation would add time without restoring the missing optimization details.\\n\\n---\\n\\n# P5. The finite-element-analysis veteran\\n\\n## (a) My reaction in the room\\n\\nThe opening is fine until slide 4 implies that physical iteration is the central route around slow design. I start waiting for the argument about simulation. Because slide 6 is hidden, the deck never states the agreed, defensible position: a sufficiently calibrated model may cost more than it saves for this campaign, particularly when interfaces, defects, rate dependence, contact, and millisecond shock transients dominate.\\n\\nThat omission avoids an overt attack on finite-element analysis (FEA), which is good, but it also creates a logical hole. Why is physical BO the chosen source of objective values? Why not a hybrid strategy, reduced-order model, analytical screening, or multi-fidelity optimization? The deck merely proceeds from \\u201cdesign is slow\\u201d to \\u201cBO makes experimentation affordable.\\u201d\\n\\nI object to any suggestion that experimental data are automatically \\u201creal\\u201d and simulations are not. Measurements also involve sensor bandwidth, filtering, fixture dynamics, uncertainty, and model-based transformations. Slide 9\\u2019s \\u201caccelerometers gather real data\\u201d wording is particularly vulnerable. A force estimate derived from acceleration still rests on calibration and assumptions.\\n\\nSlide 10 is a useful experimental workflow. It should be presented as a scoped engineering choice, not a universal replacement for simulation. Slide 12\\u2019s transfer claim needs the condition from the outline: use this pattern where tests are authoritative, costly, the design space is mixed, and goals conflict.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey bypass a calibrated finite-element model and optimize directly against impact tests because the printed interfaces and transient response are difficult to model economically.\\u201d\\n\\nThat is close to the intended experiment-first rationale, but I had to infer it; the visible slides do not say it clearly.\\n\\n## (c) My top three Q&A questions\\n\\n1. What evidence shows that calibrating a useful simulation would cost more than it saves for this campaign, rather than merely being inconvenient?\\n2. How do you verify the measured peak transmitted force, including sensor bandwidth, fixture dynamics, filtering, and uncertainty?\\n3. Why exclude analytical or low-fidelity screening from a multi-fidelity BO strategy, especially for infeasible geometries and gross structural trends?\\n\\n## (d) My most pointed objection\\n\\n**The deck uses experiment-first optimization without stating a scoped cost-and-validity argument or acknowledging where simulation could still add value.**\\n\\nUnanswered, this is **highly damaging in Q&A**. It makes the methodology look ideologically anti-simulation rather than economically reasoned.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 4.** It should become the two-sided decision: exhaustive physical search is too costly, while campaign-grade calibration for the hardest responses is also costly; therefore choose physical tests adaptively.\\n- **Hidden-slide decision:** **Merge slide 6 into slide 4**, rather than unhide it as written. Keep a fuller evidence-backed simulation/experiment comparison as backup.\\n\\n---\\n\\n# P6. The friendly industry generalist\\n\\n## (a) My reaction in the room\\n\\nThe opening video is memorable, and I like the practical goal of shortening build\\u2013test iteration. Slide 4 is easy to understand. My attention slips on slide 7 because the counts and black-box symbol do not tell a story. I do not need the name qNEHVI, but I need to know that the software balances trying promising designs with learning from uncertain ones and respects a force limit.\\n\\nSlides 8\\u201310 are the strongest run. If the presenter shows one labeled specimen, one clean drop clip, and one loop diagram, I can see how I might use the pattern in another product-development problem. I want a cycle-time number and an indication of what software or human work is required.\\n\\nSlide 12 dilutes the message by offering several destinations before proving the present result. The ending is weak. A blank Q&A slide discards the chance to leave the audience with the workflow, the best specimen, and a measured improvement.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cPrint it, break or drop it, feed the measurements back, and let the optimizer pick the next batch instead of running a huge test matrix.\\u201d\\n\\nThat captures the intended practical pattern, but I would probably overgeneralize it beyond the specific conditions under which it is useful.\\n\\n## (c) My top three Q&A questions\\n\\n1. How many prints and calendar days did it take to reach a useful design compared with your previous workflow?\\n2. What parts of this system could another engineering team reuse without building the same drop tower or writing a custom optimizer?\\n3. What happens when a print fails or a test produces an outlier?\\n\\n## (d) My most pointed objection\\n\\n**I understand the loop, but the deck never gives me the one number or result that proves it was worth building.**\\n\\nUnanswered, this is **moderately to highly damaging**. I would remember the concept but not have a reason to adopt or sponsor it.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 13.** Replace the blank Q&A screen with the best measured result, the closed-loop diagram, and one deployment rule; retain a small \\u201cQuestions?\\u201d label.\\n- **Hidden-slide decision:** **Unhide slide 11** once populated. Results are more valuable than another explanatory slide.\\n\\n---\\n\\n# Synthesis\\n\\n## Cross-persona themes\\n\\n1. **The scope boundary is missing.** The Super Ball Bot is a good hook, but every persona risks interpreting a PLA\\u2013TPU proxy as a claim about flight hardware.\\n2. **Slide 7 is the weakest visible technical slide.** It serves neither experts nor novices. Counts such as \\u201c6 variables\\u201d and \\u201c2 objectives\\u201d are information, not a message.\\n3. **The main claim currently lacks evidence.** \\u201cSignificantly accelerate,\\u201d \\u201csupplies data fast,\\u201d and \\u201cdozens, not hundreds\\u201d require a campaign ledger and a budget-matched comparison.\\n4. **The results omission breaks the story.** The deck follows the outline through the workflow, then skips the outline\\u2019s largest time block and moves directly to future work.\\n5. **The physical system needs more definition.** The audience needs a labeled specimen, a clear explanation of \\u201ctensegrity-inspired,\\u201d interface design, remaining manual steps, and the measurement chain.\\n6. **Noise must be operational rather than rhetorical.** Saying the data are noisy is insufficient. Replicates, periodic controls, drift, failures, and uncertainty should be shown.\\n7. **Experiment-first needs a scoped rationale.** The deck should not dismiss FEA, but it should explain why adaptive physical testing is the economical source of objective values for this campaign.\\n8. **The close is not a conclusion.** Future applications and a blank Q&A slide do not substitute for the measured finding and its conditions of transfer.\\n9. **Media are a communication and schedule risk.** Short clips can work, but multiple looping videos violate the signal-to-noise principle and create failure modes.\\n10. **The project specification needs reconciliation.** The supplied materials differ on six versus seven variables, whether FDM process parameters are optimized, and whether peak force is a constraint or part of the Pareto trade-off.\\n\\n## Five highest-priority slide-level revisions\\n\\n### 1. Slide 11: replace one empty slot with a three-slide quantitative results sequence\\n\\nUse full-sentence titles tied to observed data:\\n\\n- **11A:** \\u201cThe campaign used **N** specimens in **B** adaptive batches, including **R** replicates and **F** failed prints.\\u201d\\n- **11B:** \\u201cAt the same physical-test budget, adaptive selection reached **[measured feasible-performance result]** faster than **[predeclared Sobol/random baseline]**.\\u201d\\n- **11C:** \\u201cThe measured feasible Pareto front trades specific energy absorption against compaction efficiency while respecting a **[value and unit]** peak-force cap.\\u201d\\n\\nShow uncertainty, replicate spread, infeasible points, and representative specimen photographs or traces. If the campaign cannot support the baseline claim, change the talk\\u2019s main message to: **\\u201cWe evaluate whether noise-aware multi-objective Bayesian optimization can reduce the physical-test budget.\\u201d**\\n\\n### 2. Slide 7: state the actual constrained optimization problem\\n\\nReplace the current black-box/count design with:\\n\\n- the named decision variables and their types;\\n- a small batch entering the printer/test loop;\\n- objectives: maximize specific energy absorption and compaction efficiency;\\n- constraint: peak transmitted force below a stated cap;\\n- qNEHVI\\u2019s role in one plain sentence: it chooses a small next batch likely to expand the feasible trade-off frontier while accounting for uncertainty and noisy tests;\\n- the declared test budget, initialization size, and batch size.\\n\\nResolve six versus seven variables and whether processing parameters are decision variables before finalizing this slide.\\n\\n### 3. Slide 2: put the proxy boundary beside the hook\\n\\nKeep the Super Ball Bot clip, but add an image of the actual specimen and the visible sentence:\\n\\n> **\\u201cHere, a printed PLA\\u2013TPU T3 prism is a proxy for developing the workflow, not flight hardware.\\u201d**\\n\\nChange or qualify \\u201crobust, reusable solutions\\u201d unless the present campaign includes repeat-impact evidence.\\n\\n### 4. Slide 4: turn the vague need into the two-sided engineering decision\\n\\nRetitle it:\\n\\n> **\\u201cBecause exhaustive testing and campaign-grade model calibration are both costly, each physical specimen must be chosen for its information value.\\u201d**\\n\\nAdd one quantified physical-search estimate and one scoped calibration challenge. Do not say simulation is untrustworthy. State that FEA and lower-fidelity models remain possible screening or future multi-fidelity tools.\\n\\n### 5. Slides 10 and 13: separate the demonstrated loop from the conclusion\\n\\n- **Slide 10:** retitle to **\\u201cThe closed loop turns each measured test into the next small batch of specimens.\\u201d** Remove \\u201csignificantly accelerate\\u201d until the results demonstrate it. Mark automated and manual stages.\\n- **Slide 13:** retain the closed-loop thumbnail, add the best quantitative result, and state the deployment rule: **use the loop when tests are authoritative, each test is costly, the design space is mixed, and performance goals conflict.** Add \\u201cQuestions?\\u201d without clearing the conclusion from the screen.\\n\\n## Hidden-slide adjudication\\n\\n| Hidden slide | Recommendation | Rationale |\\n|---|---|---|\\n| **3: tensegrity usefulness/mechanism** | **Merge into slide 8** | The generic elbow and squishing GIFs add noise and risk an inaccurate mechanism; an annotated actual specimen and deformation sequence are stronger. |\\n| **5: generic BO explanation** | **Merge into slide 7; keep original as backup or delete** | DAC experts do not need a generic advocacy slide, but novices need one plain-language sentence and a concrete acquisition visual. |\\n| **6: specimen information value/experiment-first** | **Merge into slide 4; keep evidence-rich version as backup** | The argument is structurally necessary, but the current title is vague and the visual does not support the scoped calibration-versus-campaign-cost decision. |\\n| **11: results** | **Unhide and expand to three slides** | Results are the evidentiary center of the talk and were allocated five minutes in the agreed outline. An empty or hidden results slot makes the main claim untenable. |\\n\\n## Visible-slide claims needing evidence, hedging, or backup\\n\\n| Slide | Claim | Required treatment |\\n|---|---|---|\\n| **1** | \\u201cBuild better tensegrity structures faster\\u201d | Define \\u201cbetter\\u201d with the two objectives and force constraint; support \\u201cfaster\\u201d with the results comparison. |\\n| **2** | Tensegrity is robust and reusable for planetary landings | Cite the Super Ball Bot source; distinguish concept evidence from this study; provide repeat-impact evidence or hedge reusability. |\\n| **2** | Parachutes and retrorockets struggle in the stated conditions | Cite an authoritative aerospace source if retained in narration; avoid implying universal inadequacy. |\\n| **4** | Current tensegrity design is slow and resource intensive | Quantify candidate count, print/test time, hand-assembly burden, or calibration effort. |\\n| **7** | BO makes experiments affordable with limited, noisy data | Show campaign budget, noise characterization, replicates, baseline, and performance versus tests. Prepare qNEHVI and Gaussian-process details in backup. |\\n| **7** | Six variables, two input-data types, two objectives | Reconcile with the apparent seven-variable abstract specification and identify every quantity. Clarify \\u201cinput-data types.\\u201d |\\n| **8** | Multi-material AM supplies data fast | Report print-to-result cycle time and failure rate. |\\n| **8** | Single-build co-fabrication with no joining | Show interface and post-processing; use \\u201cno joining of PLA and TPU members\\u201d if other manual operations remain. |\\n| **9** | Accelerometers gather the relevant impact data | Show sensor locations and the derivation of transmitted force, specific energy absorption, and compaction efficiency. Reconcile this wording with the abstract\\u2019s instrumented-tup description. |\\n| **10** | The loop significantly accelerates optimization | Remove \\u201csignificantly\\u201d unless a defined baseline and uncertainty support it. Statistical significance should not be implied casually. |\\n| **12** | Workflow will transfer to lattices and other applications | Present as conditional future work with transfer criteria, not an achieved result. Attribute any external crutch-tip image and claim. |\\n\\n### Prepared backup slides\\n\\n1. Full design-variable table, bounds, encoding, and fixed versus optimized process parameters.\\n2. qNEHVI implementation, Gaussian-process assumptions, constraint treatment, batch size, and acquisition settings.\\n3. Noise plan: replicates, periodic control, heteroscedasticity, drift, outlier policy, and uncertainty propagation.\\n4. Budget-matched baseline definition and evaluation metric.\\n5. Force-cap rationale and full objective definitions.\\n6. Sensor chain, calibration, sample rate, filtering, fixture dynamics, and metric extraction.\\n7. PLA\\u2013TPU interface, conditioning, build layout, dimensional/mass deviations, and print-failure modes.\\n8. Scoped FEA rationale with citations or internal validation; possible analytical or multi-fidelity extensions.\\n9. Automation map and cycle-time breakdown.\\n10. Repeated-impact behavior if reusability remains in the opening.\\n\\n## Merged, ordered presenter TODO list\\n\\n### Must fix before the talk\\n\\n1. **Freeze the scientific specification.** Reconcile the number and type of decision variables, whether FDM parameters are optimized or controlled, the two objectives, the force constraint, treatment of print failures, and the measurement chain. Use one formulation across slides, abstract, paper, and Q&A.\\n2. **Set a results completion plan.** Establish dates and owners for campaign completion, repeat tests, data quality review, baseline analysis, and slide freeze. Predeclare the fallback claim if the baseline does not support \\u201cdozens rather than hundreds.\\u201d\\n3. **Build the three-slide results sequence.** Campaign ledger \\u2192 budget-matched baseline \\u2192 measured feasible Pareto front. Allocate approximately five minutes, as the agreed outline requires.\\n4. **Add the proxy statement to slide 2.** State plainly that the PLA\\u2013TPU T3 specimen is a workflow proxy and not flight hardware.\\n5. **Redesign slide 7.** Show the exact constrained multi-objective problem, test budget, small batches, uncertainty, noisy observations, and the role of qNEHVI.\\n6. **Remove unsupported result language.** Until supported, delete \\u201csignificantly\\u201d from slide 10 and avoid \\u201cdozens rather than hundreds\\u201d in the title, narration, or close.\\n7. **Clarify experiment-first positioning.** Merge the information-value argument into slide 4 and prepare an evidence-backed FEA backup slide.\\n8. **Make the experiment auditable.** On slide 9 or backup, reconcile accelerometers and the instrumented tup; show how all three performance metrics are calculated and how uncertainty is handled.\\n9. **De-risk every video.** Store local trimmed files, disable distracting loops, test them on presentation hardware, and include static fallback frames.\\n10. **Rehearse to 13:30\\u201314:00.** Protect the results and conclusion from being cut. Practice once without slides, consistent with Doumont\\u2019s preparation advice.\\n\\n### Should fix\\n\\n11. **Replace slide 8\\u2019s generic print emphasis with mechanism and materials evidence.** Label PLA, TPU, interface strategy, prestress status, deformation, post-processing, and failure modes.\\n12. **Quantify slide 4.** Give at least one campaign-relevant cost, duration, or candidate-count estimate.\\n13. **Show automation boundaries on slide 10.** Candidate selection and surrogate updating may be automated; slicing, handling, test setup, and testing remain manual according to the abstract.\\n14. **Convert slide 12 to a gated roadmap.** Separate demonstrated T3-proxy results from future lattices, flight materials, and terrestrial applications.\\n15. **Turn slide 13 into the conclusion.** Leave the audience with the best result and transfer rule while taking questions.\\n16. **Prepare technical backup slides.** Prioritize baseline fairness, noise, force-cap rationale, print failures, materials repeatability, and FEA positioning.\\n\\n### Polish\\n\\n17. Replace the slide 1 title with a specific message, such as **\\u201cPhysical Bayesian optimization searches multi-material tensegrity absorbers one small test batch at a time.\\u201d**\\n18. Left-align titles and use intentional line breaks; check every slide at six-per-page scale.\\n19. Use one purposeful visual per slide. Avoid decorative loading or elbow animations.\\n20. Standardize terminology: \\u201cBayesian optimization,\\u201d not \\u201cBays Opt\\u201d; \\u201ctensegrity-inspired\\u201d where appropriate; define specific energy absorption once.\\n21. Add citations directly beside borrowed footage, images, and quantitative claims.\\n\\n## Who the deck serves best and worst\\n\\n- **Best served:** **P6, the friendly industry generalist.** The visible sequence is visually intuitive, practical, and easy to reduce to \\u201cprint \\u2192 test \\u2192 learn \\u2192 repeat.\\u201d\\n- **Worst served:** **P1, the skeptical BO insider.** The current deck omits nearly every item needed to judge a DAC-10 contribution: exact problem formulation, budget, baseline, noise model, constraint handling, and empirical adaptive advantage.\\n\\nThat is **not the right trade-off for DAC-10**. The deck should remain accessible to P4 and P6, but its technical center must satisfy P1 without turning into a BO lecture. One rigorous optimization slide, three quantitative results slides, and targeted backup material would achieve that balance.\\n\\n## Predicted reception\\n\\n- **As is: 4/10.** The physical workflow and application are memorable, but the absent scope statement, empty results section, vague BO slide, and unsupported acceleration claim leave the DAC contribution unproven.\\n- **After the must-fixes: 8/10.** A scoped proxy claim, reconciled methods, fair budget-matched comparison, feasible Pareto front, and measured conclusion would turn the same visual story into a credible design-optimization application talk.\\n\\n## Discretionary analytical decisions\\n\\n- Evaluated only the visible slide order for the simulated in-room reactions, while using hidden slides and presenter notes to judge revision options.\\n- Treated planned placeholder visuals as if implemented according to the presenter\\u2019s notes, but judged the selected visual concept and associated stage risk rather than absent graphic quality.\\n- Prioritized the Draft 3 outline over conflicting Draft 1 wording because the prompt identifies it as the agreed story arc, scope framing, results plan, and timing plan.\\n- Rated objections qualitatively as moderate, high, or critical rather than assigning unsupported numerical probabilities.\\n- Recommended three results slides rather than preserving one reserved slot because the agreed outline specifies three distinct evidentiary products and allocates five minutes to results.\\n- Recommended merging hidden slides 3, 5, and 6 rather than simply unhiding them because their necessary ideas can be conveyed with higher signal-to-noise on existing slides.\\n- Treated the six-versus-seven-variable and optimized-versus-fixed-process-parameter differences as unresolved specification conflicts requiring reconciliation, rather than inferring which document is correct.\\n- Assessed claims against the supplied deck extraction, Draft 3 outline, Doumont notes, and submitted abstract only; no independent validation of cited literature or unseen experimental data was attempted.\"}", + "name": "submit_answer" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "tool", + "content": "175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "info": { + "start_ts": 8153.825818486, + "end_ts": 8153.828437556 + } + } + ], + "next_observation": [ + { + "role": "tool", + "content": "175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "info": { + "start_ts": 8153.825818486, + "end_ts": 8153.828437556 + } + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "callback_type": "after_agent_get_asv" + }, + "action": { + "call_id": { + "run_id": "4566fd2d-f49d-4655-ac55-692f306b1db0", + "fwd_id": "f6b2ecbb-a1f2-438d-9b88-1a216eb20c49" + }, + "op_name": "IdentityOp-3b7eacd87007", + "op_class_name": "ldp.graph.common_ops.IdentityOp", + "value": { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8In1tJq32AD2tTyHX3tmIlLf", + "type": "function", + "function": { + "arguments": "{\"answer\": \"# Mock audience report: IDETC-CIE 2026 slide deck, Draft 1\\n\\n## Executive read\\n\\nThe visible deck has a clear visual spine: **motivation \\u2192 slow iteration \\u2192 Bayesian optimization \\u2192 multi-material printing \\u2192 impact testing \\u2192 closed loop \\u2192 future applications**. The strongest section is slides 8\\u201310, where fabrication, testing, and adaptive selection finally appear as one workflow.\\n\\nBut Draft 1 does not yet deliver the technical case promised by the agreed outline. The visible deck omits four things a DAC-10 audience will need:\\n\\n1. **Honest scope:** no visible slide says that the PLA\\u2013TPU T3 prism is a proxy system rather than flight hardware.\\n2. **A precise optimization problem:** the six variables, two objectives, force constraint, noise treatment, and fabrication failures are not intelligible on slide 7.\\n3. **Quantitative evidence:** the sole results slot is hidden and empty, yet slides 10 and 12 already claim acceleration and transfer.\\n4. **A measured conclusion:** the talk jumps from the workflow to speculative applications without showing a campaign ledger, comparison against a budget-matched baseline, or feasible Pareto front.\\n\\nThe slide titles generally follow Doumont\\u2019s full-sentence-message rule, but several messages outrun the evidence. The deck also departs substantially from the Draft 3 timing plan, which reserves **5 of 15 minutes for results**. As built, results receive zero visible slides.\\n\\n---\\n\\n# P0. The program manager in the audience\\n\\n## (a) My reaction in the room\\n\\nSlide 1 tells me the team wants to work faster, but it does not tell me what is being optimized or what has actually been built. The title sounds aspirational rather than traceable to a project deliverable. I immediately want the formal title from the abstract or a shorter version of it.\\n\\nThe Super Ball Bot footage on slide 2 is an effective hook. I understand why reusable impact attenuation matters. But the slide creates a scope risk: I am looking at planetary landing hardware while the actual work is a printed PLA\\u2013TPU T3 proxy. Because that distinction is not stated immediately, I may spend the rest of the talk evaluating the work against flight-readiness criteria the project never intended to meet.\\n\\nSlide 4 gives me the business problem, but not a baseline. \\u201cSlow and resource intensive\\u201d needs a number: hours per print-test cycle, number of factors, or the candidate count under a declared design of experiments. Without that, I cannot assess whether the proposed loop attacks the dominant schedule driver.\\n\\nSlide 7 is where I expect the project definition. Instead, \\u201c6 variables,\\u201d \\u201c2 types of input data,\\u201d and \\u201c2 objectives\\u201d read like inventory labels. I cannot reconstruct the statement of work from them. I need to see what is automated, what remains manual, what the force cap is, and what constitutes a successful campaign.\\n\\nSlides 8 and 9 make the project tangible. A labeled specimen and an actual drop test would build confidence. Yet \\u201csingle-build\\u201d and \\u201cno joining\\u201d need a process qualification caveat, and I need the measurement chain to be explicit. The abstract refers to an instrumented tup and force\\u2013time data; slide 9 says accelerometers. Those may be compatible, but the deck presently leaves the relationship unclear.\\n\\nSlide 10 is the right management view of the system, but \\u201csignificantly accelerate\\u201d is a result claim placed on a workflow diagram. At this point I ask, \\u201cCompared with what, by how much, and over how many specimens?\\u201d Then the deck skips the hidden results slide and goes directly to future applications. That is the point where my confidence drops sharply. The project is asking me to extrapolate before closing the evidence loop.\\n\\nSlide 12 is too early and too broad. A crutch tip and lattice can be legitimate next phases, but only after the team shows what the present campaign established and what technical risks remain. The blank questions slide gives me no durable take-home message.\\n\\n### PM stage-risk register\\n\\n| Risk | Likelihood | Consequence | Mitigation |\\n|---|---:|---:|---|\\n| Results are incomplete by conference lock | Medium to high | Critical: central claim becomes unsupported | Set a dated campaign freeze; predeclare a fallback message and minimum publishable result package |\\n| Embedded Super Ball Bot or drop-test video fails | Medium | Moderate: weak opening or broken pacing | Use local, trimmed files; retain a still-frame fallback on every video slide |\\n| Talk overruns because several videos and explanations are improvised | High | High: results or conclusion gets cut | Script videos to 10\\u201315 seconds total each; rehearse to 13:30; assign 5 minutes to results |\\n| Scope is mistaken for flight-hardware validation | High | High: credibility damage in Q&A | Put \\u201cPLA\\u2013TPU T3 proxy; not flight hardware\\u201d on slide 2 or the next slide |\\n| Acceleration claim lacks a budget-matched comparator | High | Critical for DAC review audience | Show Sobol/random or another predeclared baseline at the same physical-test budget |\\n| Measurement chain is challenged | Medium | High | Add a methods schematic and backup slide on calibration, sampling, filtering, and metric extraction |\\n| \\u201cNo joining\\u201d or \\u201cnear autonomous\\u201d is read as full automation | Medium | Moderate | List manual steps explicitly: slicing, handling, setup, and testing |\\n| Animations distract or fail to communicate without narration | Medium | Moderate | Prefer one short clip plus static annotated frames; follow Doumont\\u2019s signal-to-noise rule |\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey are building a loop that prints PLA\\u2013TPU tensegrity-like absorbers, drop-tests them, and lets Bayesian optimization choose the next print.\\u201d\\n\\nThat retains the basic workflow but loses the intended **dozens rather than hundreds** claim because the deck never demonstrates it. I would also probably describe the work as \\u201clander research,\\u201d which is a distortion caused by the missing proxy-system statement.\\n\\n## (c) My top three Q&A questions\\n\\n1. What is the dated plan to complete the campaign, baseline comparison, and repeat testing before the conference slide freeze?\\n2. Which exact operations are automated today, which remain manual, and what is the measured end-to-end cycle time per adaptive batch?\\n3. What evidence will justify \\u201cdozens rather than hundreds,\\u201d and what will you claim if the budget-matched baseline does not show an advantage?\\n\\n## (d) My most pointed objection\\n\\n**You are presenting a claimed acceleration without showing a completed campaign, a comparator, or even a visible results slide.**\\n\\nUnanswered, this is **critical**. I would not fund a scale-up phase from this deck. I might fund completion of the present campaign if the team showed a disciplined schedule, predefined success criteria, and a credible fallback.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 11.** It must become a real, scheduled results package, preferably three slides: campaign ledger, budget-matched baseline, and feasible Pareto front.\\n- **Hidden-slide decision:** **Unhide slide 11**, but not in its current empty form. The talk cannot make its principal claim without it.\\n\\n### PM slide-level punch list, ordered by schedule criticality\\n\\n**Must be fixed first**\\n\\n1. **Slide 11/results sequence:** assign owners and dates for data freeze, quality control, baseline computation, replicate analysis, and final plots. Define fallback wording now.\\n2. **Slide 2:** add the explicit scope line, \\u201cThis study uses a printed PLA\\u2013TPU T3 prism as a proxy for developing the workflow; it is not flight hardware.\\u201d\\n3. **Slide 7:** replace category counts with the actual optimization problem, test budget, batch size, objectives, constraint, and noise/failure treatment.\\n4. **Slide 10:** remove \\u201csignificantly\\u201d until slide 11 demonstrates a quantitative improvement; show automation boundaries and the loop\\u2019s measured cycle time.\\n5. **Slides 2 and 9:** download and trim media, test on the conference laptop, and put a static fallback image on each slide.\\n\\n**Then fix**\\n\\n6. **Slide 4:** quantify the current cost or candidate count and identify the bottleneck.\\n7. **Slide 8:** label PLA, TPU, interface, and any post-processing; qualify \\u201cno joining.\\u201d\\n8. **Slide 9:** reconcile accelerometers with the instrumented-tup description and show how force, energy absorption, and compaction efficiency are obtained.\\n9. **Slide 12:** convert the application collage into a conditional next-phase roadmap with gates.\\n10. **Slide 13:** end on the measured result and deployment rule, not a blank Q&A screen.\\n\\n**Nice to have**\\n\\n11. Replace decorative or looping media on slide 4 with one static process image unless it communicates a measured delay.\\n12. Add backup slides on Bayesian optimization implementation, finite-element-analysis positioning, repeatability, force-cap rationale, and print failures.\\n\\n---\\n\\n# P1. The skeptical Bayesian-optimization insider\\n\\n## (a) My reaction in the room\\n\\nThe opening is competent but generic for a design-automation conference. Slides 1\\u20134 set up an expensive physical experiment, which is a valid application class for Bayesian optimization (BO). I become interested when the presenter reaches slide 7, because that is where methodological seriousness should appear.\\n\\nInstead, slide 7 says only \\u201c6 variables,\\u201d \\u201c2 types of input data,\\u201d and \\u201c2 objectives.\\u201d I do not learn the variables, their types, how categorical choices are encoded, whether process parameters are optimized, how noisy observations enter the Gaussian processes, how replicates identify noise, how failures are modeled, or how the force cap enters the acquisition. The title says BO handles noisy data, but the visual does not substantiate that message.\\n\\nThere is also an unresolved specification conflict across the supplied materials. The agreed outline says the study jointly optimizes T3 geometry and FDM processing parameters. The abstract\\u2019s design vector lists four continuous geometric variables, one integer, and two categorical variables, apparently **seven variables**, while its fabrication section says print parameters are held fixed within a batch. The slide says **six variables**. A DAC audience will notice if the spoken details do not reconcile these versions.\\n\\nSlides 8\\u201310 explain why physical feedback is useful, but they do not establish why qNEHVI is the right algorithm or whether adaptive selection helped. The visible deck never names qNEHVI, the feasible Pareto front, the peak-force constraint, batch size, initialization, or test budget. Hiding slide 5 is reasonable if it is only a generic \\u201cwhat is BO?\\u201d slide copied from another talk. But slide 7 must then carry the application-specific methodological content.\\n\\nWhen slide 10 says the workflow \\u201csignificantly accelerate[s]\\u201d optimization, I wait for a performance-versus-budget curve. It never arrives. Slide 12 then generalizes to other applications. My default conclusion is exactly the skeptical one posed in the prompt: this appears to be off-the-shelf qNEHVI attached to an interesting physical apparatus, with no demonstrated algorithmic or empirical advantage yet.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cIt was an experimental self-driving-lab-style application of batch multi-objective BO to printed impact absorbers, but the talk did not show whether BO beat space-filling sampling.\\u201d\\n\\nThat differs from the intended message because I would not repeat \\u201cdozens, not hundreds\\u201d without a budget-matched baseline.\\n\\n## (c) My top three Q&A questions\\n\\n1. What are the exact decision variables and encodings, and why do the deck, scope statement, and abstract appear to disagree on whether there are six or seven variables and whether FDM parameters are optimized?\\n2. How is observation noise estimated: fixed noise, learned heteroscedastic noise, technical replicates, or repeated control specimens? What is the replicate allocation?\\n3. Against what budget-matched baseline do you evaluate qNEHVI, and what metric do you report: feasible hypervolume, best feasible objective values, or attainment probability versus physical tests?\\n\\n## (d) My most pointed objection\\n\\n**Nothing visible distinguishes a validated BO contribution from an untested application of standard qNEHVI.**\\n\\nUnanswered, this is **critical for DAC-10 reception**. Application novelty can be enough, but only if the physical campaign is rigorous and the adaptive method is compared fairly under the same test budget.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 7.** Replace the black-box inventory with a visual of posterior mean plus uncertainty \\u2192 constrained batch acquisition \\u2192 next physical tests, accompanied by the exact objectives and force constraint.\\n- **Hidden-slide decision:** **Keep slide 5 hidden** as a standalone generic BO tutorial. Merge one plain-language BO sentence into slide 7 and put qNEHVI details in backup.\\n\\n---\\n\\n# P2. The aerospace practitioner\\n\\n## (a) My reaction in the room\\n\\nThe Super Ball Bot hook works because I know the lineage. Reusable landing structures are interesting. But the title\\u2019s claim that tensegrity provides \\u201crobust, reusable solutions\\u201d is broader than what this deck demonstrates. A lab drop of a polymer proxy is not evidence of robustness in thermal extremes, vacuum, dust, radiation, repeated impacts, or off-axis terrain contact.\\n\\nThe deck never immediately tells me that the actual specimen is **tensegrity-inspired**, lacks ideal compression-only and tension-only membership, and is not intended to fly. That makes slide 2 vulnerable to looking like borrowed aerospace relevance.\\n\\nSlide 4 is plausible but too broad. Tensegrity design can be difficult, but aerospace development is not made slow only by design-space search. Verification, environmental qualification, uncertainty, packaging, guidance, and system integration matter. A faster specimen loop is useful, but it is one subsystem tool.\\n\\nI am not helped much by slide 7. I need BO translated into engineering behavior: it chooses the next small batch based on predicted performance and uncertainty while rejecting designs expected to violate a payload-force limit. The slide should name that limit and show the actual trade-off.\\n\\nSlides 8 and 9 are the first ones that answer \\u201cwhat did you build and how did you hit it?\\u201d A labeled specimen, deformation sequence, drop mass, speed or energy, orientation, and force measurement would hold my attention. Two simultaneous looping videos may not. I also want to know whether the structure rebounds, survives repeated drops, and retains its response, because the opening emphasizes reuse.\\n\\nSlide 10 is understandable. Slide 12 loses me because it jumps from a T3 polymer coupon to lattices and crutch tips without stating transfer conditions or technology gates.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey use an optimizer to choose which 3D-printed soft\\u2013rigid impact absorber to drop-test next, with the eventual motivation of reusable landing structures.\\u201d\\n\\nThat is fairly close, but I would deliberately downgrade \\u201ctensegrity\\u201d to \\u201ctensegrity-inspired absorber\\u201d and treat planetary landing as motivation only.\\n\\n## (c) My top three Q&A questions\\n\\n1. What makes the printed T3 specimen mechanically tensegrity-inspired rather than an ordinary compliant crush structure, and is it actually prestressed?\\n2. What impact energy, mass, orientation, and transmitted-force cap are used, and how do they relate to a payload-protection requirement?\\n3. How does performance change over repeated impacts, and what failure modes or permanent set limit the claimed reusability?\\n\\n## (d) My most pointed objection\\n\\n**The opening borrows flight relevance and reusability, but the visible deck never establishes the proxy boundary or shows repeated-impact evidence.**\\n\\nUnanswered, this is **highly damaging** to credibility with aerospace practitioners, though it does not invalidate the optimization workflow itself.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 2.** Keep the hook, but place a photograph of the actual T3 specimen beside the Super Ball Bot and state \\u201cmotivation, not demonstrated flight readiness.\\u201d\\n- **Hidden-slide decision:** **Keep slide 3 hidden.** A generic elbow-friction GIF would distract and oversimplify. Merge a precise tensegrity-mechanism annotation into slide 8 instead.\\n\\n---\\n\\n# P3. The additive-manufacturing/materials researcher\\n\\n## (a) My reaction in the room\\n\\nSlides 1\\u20134 give me a recognizable motivation for rapid experimental iteration. My interest rises on slide 8 because multi-material fused deposition modeling is where the claimed loop can succeed or fail.\\n\\nThe message \\u201csingle-build co-fabrication, with no joining of parts\\u201d is attractive, but it needs exact boundaries. Is there support removal, trimming, conditioning, manual tensioning, or fixture insertion? Does \\u201csingle build\\u201d mean one tool-change sequence on one machine, or truly co-deposited interfaces throughout the architecture? Are PLA and thermoplastic polyurethane (TPU) mechanically interlocked, chemically bonded, or merely in contact?\\n\\nThe abstract provides more useful information than the slide: a core-wrapping architecture is intended to resist delamination, specimens are weighed and dimensionally inspected, and a control is retested to monitor drift. Those are exactly the details the talk should surface. Slide 8\\u2019s planned printing video will not replace a labeled cross-section or interface image.\\n\\nSlide 9 raises repeatability questions. TPU is rate-, temperature-, moisture-, and history-dependent. \\u201cLimited, noisy data\\u201d on slide 7 acknowledges noise but does not show the control plan. I need batch identity, drying/conditioning, build position, print orientation, machine state, interface failures, and replicates.\\n\\nThe deck also appears inconsistent about process parameters. The agreed scope says FDM processing parameters are optimized with structure, but the abstract says temperature, line width, infill, and retraction are held fixed within a batch. This could mean they vary between batches or are controlled rather than optimized, but the deck must say which.\\n\\nI like the physical loop on slide 10. I do not yet accept that multi-material printing \\u201csupplies data fast\\u201d until cycle time and failure rate are shown.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey co-print PLA and TPU impact specimens and use each mechanical test to guide the next design batch.\\u201d\\n\\nI would probably omit \\u201cno joining\\u201d and \\u201cdozens rather than hundreds\\u201d until I saw interface qualification, failure accounting, and campaign data.\\n\\n## (c) My top three Q&A questions\\n\\n1. How is the PLA\\u2013TPU interface designed and qualified, and what fraction of specimens fail by delamination or print defects?\\n2. Which material, machine, conditioning, and build-position variables are controlled, measured as covariates, or optimized?\\n3. How many true replicates and repeated control specimens are used to separate BO improvement from printer drift and TPU batch variation?\\n\\n## (d) My most pointed objection\\n\\n**The deck treats co-fabrication as an enabling fact without showing interface integrity, repeatability, or the remaining manual processing steps.**\\n\\nUnanswered, this is **highly damaging** because print variation is part of both the physical mechanism and the statistical noise model.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 8.** Replace or supplement the printing clip with an annotated specimen and interface detail: PLA struts, continuous TPU network, deposition/wrapping strategy, prestress status, post-processing, and observed failure modes.\\n- **Hidden-slide decision:** **Keep slide 3 hidden.** Its proposed generic GIFs do not answer the materials questions. Put the relevant deformation mechanism on slide 8.\\n\\n---\\n\\n# P4. The first-year graduate student\\n\\n## (a) My reaction in the room\\n\\nSlide 1 sounds positive, and slide 2 gives me a memorable reason to care. I understand that the lander survives an impact because the structure itself deforms. But I do not yet know what \\u201ctensegrity\\u201d means, and the hidden slide 3 would not necessarily fix that: an elbow GIF and a squishing GIF could leave me with an inaccurate friction-versus-elasticity story.\\n\\nSlide 4 is easy to follow. The three words \\u201cIteration, Building, Testing\\u201d form a simple problem statement, although the images need to make their relationship obvious.\\n\\nI get lost on slide 7. Six variables of what? What are the two input-data types? Are the two objectives \\u201cabsorb energy\\u201d and \\u201creduce force,\\u201d or something else? I do not know what a black box means in this setting. The title says Bayesian optimization works with noisy data, but I have not been shown how it decides anything.\\n\\nSlide 8 helps because I can see printing. Still, I need labels that distinguish the rigid and flexible parts and one sentence explaining why the object is \\u201ctensegrity-inspired.\\u201d Slide 9 is intuitive if the clips are synchronized and annotated. I would understand even better if the slide drew the metric extraction directly on a force\\u2013time or force\\u2013displacement trace.\\n\\nSlide 10 is the clearest slide in the deck. Printing \\u2192 testing \\u2192 optimizer \\u2192 next print is the idea I will remember. But it arrives after the technical terms rather than before them. I would benefit from seeing a version of this loop before slide 7 and then revisiting it after the methods.\\n\\nSlide 12 feels like a new presentation. I have not seen whether the current system worked, so the crutch tip and lattice do not feel earned. Then the talk ends on a blank question slide rather than telling me the answer.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey repeatedly print and drop-test flexible 3D structures, and a machine-learning model decides what to print next.\\u201d\\n\\nMy retelling loses the two objectives, the force constraint, and the \\u201cdozens rather than hundreds\\u201d quantitative point. I might incorrectly call the method generic machine learning rather than Bayesian optimization.\\n\\n## (c) My top three Q&A questions\\n\\n1. What exactly is tensegrity, and which parts of your printed specimen are in tension and compression?\\n2. What are the six things the optimizer can change, and what are the two scores it is trying to improve?\\n3. How does the optimizer know whether to try something uncertain rather than the design it currently thinks is best?\\n\\n## (d) My most pointed objection\\n\\n**The deck names the ingredients before giving me a plain-language map of how they work together.**\\n\\nUnanswered, this is **moderately damaging**. I retain the loop but lose the actual research question and technical contribution.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 7.** It needs one concrete candidate, predicted performance plus uncertainty, and the next selected batch, not abstract counts around a black box.\\n- **Hidden-slide decision:** **Unhide neither slide 3 nor slide 5 as written.** If forced to choose one, **keep slide 5 hidden** and integrate its one-sentence concept into a redesigned slide 7. Generic explanation would add time without restoring the missing optimization details.\\n\\n---\\n\\n# P5. The finite-element-analysis veteran\\n\\n## (a) My reaction in the room\\n\\nThe opening is fine until slide 4 implies that physical iteration is the central route around slow design. I start waiting for the argument about simulation. Because slide 6 is hidden, the deck never states the agreed, defensible position: a sufficiently calibrated model may cost more than it saves for this campaign, particularly when interfaces, defects, rate dependence, contact, and millisecond shock transients dominate.\\n\\nThat omission avoids an overt attack on finite-element analysis (FEA), which is good, but it also creates a logical hole. Why is physical BO the chosen source of objective values? Why not a hybrid strategy, reduced-order model, analytical screening, or multi-fidelity optimization? The deck merely proceeds from \\u201cdesign is slow\\u201d to \\u201cBO makes experimentation affordable.\\u201d\\n\\nI object to any suggestion that experimental data are automatically \\u201creal\\u201d and simulations are not. Measurements also involve sensor bandwidth, filtering, fixture dynamics, uncertainty, and model-based transformations. Slide 9\\u2019s \\u201caccelerometers gather real data\\u201d wording is particularly vulnerable. A force estimate derived from acceleration still rests on calibration and assumptions.\\n\\nSlide 10 is a useful experimental workflow. It should be presented as a scoped engineering choice, not a universal replacement for simulation. Slide 12\\u2019s transfer claim needs the condition from the outline: use this pattern where tests are authoritative, costly, the design space is mixed, and goals conflict.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cThey bypass a calibrated finite-element model and optimize directly against impact tests because the printed interfaces and transient response are difficult to model economically.\\u201d\\n\\nThat is close to the intended experiment-first rationale, but I had to infer it; the visible slides do not say it clearly.\\n\\n## (c) My top three Q&A questions\\n\\n1. What evidence shows that calibrating a useful simulation would cost more than it saves for this campaign, rather than merely being inconvenient?\\n2. How do you verify the measured peak transmitted force, including sensor bandwidth, fixture dynamics, filtering, and uncertainty?\\n3. Why exclude analytical or low-fidelity screening from a multi-fidelity BO strategy, especially for infeasible geometries and gross structural trends?\\n\\n## (d) My most pointed objection\\n\\n**The deck uses experiment-first optimization without stating a scoped cost-and-validity argument or acknowledging where simulation could still add value.**\\n\\nUnanswered, this is **highly damaging in Q&A**. It makes the methodology look ideologically anti-simulation rather than economically reasoned.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 4.** It should become the two-sided decision: exhaustive physical search is too costly, while campaign-grade calibration for the hardest responses is also costly; therefore choose physical tests adaptively.\\n- **Hidden-slide decision:** **Merge slide 6 into slide 4**, rather than unhide it as written. Keep a fuller evidence-backed simulation/experiment comparison as backup.\\n\\n---\\n\\n# P6. The friendly industry generalist\\n\\n## (a) My reaction in the room\\n\\nThe opening video is memorable, and I like the practical goal of shortening build\\u2013test iteration. Slide 4 is easy to understand. My attention slips on slide 7 because the counts and black-box symbol do not tell a story. I do not need the name qNEHVI, but I need to know that the software balances trying promising designs with learning from uncertain ones and respects a force limit.\\n\\nSlides 8\\u201310 are the strongest run. If the presenter shows one labeled specimen, one clean drop clip, and one loop diagram, I can see how I might use the pattern in another product-development problem. I want a cycle-time number and an indication of what software or human work is required.\\n\\nSlide 12 dilutes the message by offering several destinations before proving the present result. The ending is weak. A blank Q&A slide discards the chance to leave the audience with the workflow, the best specimen, and a measured improvement.\\n\\n## (b) What I would tell a colleague the next day\\n\\n> \\u201cPrint it, break or drop it, feed the measurements back, and let the optimizer pick the next batch instead of running a huge test matrix.\\u201d\\n\\nThat captures the intended practical pattern, but I would probably overgeneralize it beyond the specific conditions under which it is useful.\\n\\n## (c) My top three Q&A questions\\n\\n1. How many prints and calendar days did it take to reach a useful design compared with your previous workflow?\\n2. What parts of this system could another engineering team reuse without building the same drop tower or writing a custom optimizer?\\n3. What happens when a print fails or a test produces an outlier?\\n\\n## (d) My most pointed objection\\n\\n**I understand the loop, but the deck never gives me the one number or result that proves it was worth building.**\\n\\nUnanswered, this is **moderately to highly damaging**. I would remember the concept but not have a reason to adopt or sponsor it.\\n\\n## (e) Slide-deck verdict\\n\\n- **Slide most needing work:** **Slide 13.** Replace the blank Q&A screen with the best measured result, the closed-loop diagram, and one deployment rule; retain a small \\u201cQuestions?\\u201d label.\\n- **Hidden-slide decision:** **Unhide slide 11** once populated. Results are more valuable than another explanatory slide.\\n\\n---\\n\\n# Synthesis\\n\\n## Cross-persona themes\\n\\n1. **The scope boundary is missing.** The Super Ball Bot is a good hook, but every persona risks interpreting a PLA\\u2013TPU proxy as a claim about flight hardware.\\n2. **Slide 7 is the weakest visible technical slide.** It serves neither experts nor novices. Counts such as \\u201c6 variables\\u201d and \\u201c2 objectives\\u201d are information, not a message.\\n3. **The main claim currently lacks evidence.** \\u201cSignificantly accelerate,\\u201d \\u201csupplies data fast,\\u201d and \\u201cdozens, not hundreds\\u201d require a campaign ledger and a budget-matched comparison.\\n4. **The results omission breaks the story.** The deck follows the outline through the workflow, then skips the outline\\u2019s largest time block and moves directly to future work.\\n5. **The physical system needs more definition.** The audience needs a labeled specimen, a clear explanation of \\u201ctensegrity-inspired,\\u201d interface design, remaining manual steps, and the measurement chain.\\n6. **Noise must be operational rather than rhetorical.** Saying the data are noisy is insufficient. Replicates, periodic controls, drift, failures, and uncertainty should be shown.\\n7. **Experiment-first needs a scoped rationale.** The deck should not dismiss FEA, but it should explain why adaptive physical testing is the economical source of objective values for this campaign.\\n8. **The close is not a conclusion.** Future applications and a blank Q&A slide do not substitute for the measured finding and its conditions of transfer.\\n9. **Media are a communication and schedule risk.** Short clips can work, but multiple looping videos violate the signal-to-noise principle and create failure modes.\\n10. **The project specification needs reconciliation.** The supplied materials differ on six versus seven variables, whether FDM process parameters are optimized, and whether peak force is a constraint or part of the Pareto trade-off.\\n\\n## Five highest-priority slide-level revisions\\n\\n### 1. Slide 11: replace one empty slot with a three-slide quantitative results sequence\\n\\nUse full-sentence titles tied to observed data:\\n\\n- **11A:** \\u201cThe campaign used **N** specimens in **B** adaptive batches, including **R** replicates and **F** failed prints.\\u201d\\n- **11B:** \\u201cAt the same physical-test budget, adaptive selection reached **[measured feasible-performance result]** faster than **[predeclared Sobol/random baseline]**.\\u201d\\n- **11C:** \\u201cThe measured feasible Pareto front trades specific energy absorption against compaction efficiency while respecting a **[value and unit]** peak-force cap.\\u201d\\n\\nShow uncertainty, replicate spread, infeasible points, and representative specimen photographs or traces. If the campaign cannot support the baseline claim, change the talk\\u2019s main message to: **\\u201cWe evaluate whether noise-aware multi-objective Bayesian optimization can reduce the physical-test budget.\\u201d**\\n\\n### 2. Slide 7: state the actual constrained optimization problem\\n\\nReplace the current black-box/count design with:\\n\\n- the named decision variables and their types;\\n- a small batch entering the printer/test loop;\\n- objectives: maximize specific energy absorption and compaction efficiency;\\n- constraint: peak transmitted force below a stated cap;\\n- qNEHVI\\u2019s role in one plain sentence: it chooses a small next batch likely to expand the feasible trade-off frontier while accounting for uncertainty and noisy tests;\\n- the declared test budget, initialization size, and batch size.\\n\\nResolve six versus seven variables and whether processing parameters are decision variables before finalizing this slide.\\n\\n### 3. Slide 2: put the proxy boundary beside the hook\\n\\nKeep the Super Ball Bot clip, but add an image of the actual specimen and the visible sentence:\\n\\n> **\\u201cHere, a printed PLA\\u2013TPU T3 prism is a proxy for developing the workflow, not flight hardware.\\u201d**\\n\\nChange or qualify \\u201crobust, reusable solutions\\u201d unless the present campaign includes repeat-impact evidence.\\n\\n### 4. Slide 4: turn the vague need into the two-sided engineering decision\\n\\nRetitle it:\\n\\n> **\\u201cBecause exhaustive testing and campaign-grade model calibration are both costly, each physical specimen must be chosen for its information value.\\u201d**\\n\\nAdd one quantified physical-search estimate and one scoped calibration challenge. Do not say simulation is untrustworthy. State that FEA and lower-fidelity models remain possible screening or future multi-fidelity tools.\\n\\n### 5. Slides 10 and 13: separate the demonstrated loop from the conclusion\\n\\n- **Slide 10:** retitle to **\\u201cThe closed loop turns each measured test into the next small batch of specimens.\\u201d** Remove \\u201csignificantly accelerate\\u201d until the results demonstrate it. Mark automated and manual stages.\\n- **Slide 13:** retain the closed-loop thumbnail, add the best quantitative result, and state the deployment rule: **use the loop when tests are authoritative, each test is costly, the design space is mixed, and performance goals conflict.** Add \\u201cQuestions?\\u201d without clearing the conclusion from the screen.\\n\\n## Hidden-slide adjudication\\n\\n| Hidden slide | Recommendation | Rationale |\\n|---|---|---|\\n| **3: tensegrity usefulness/mechanism** | **Merge into slide 8** | The generic elbow and squishing GIFs add noise and risk an inaccurate mechanism; an annotated actual specimen and deformation sequence are stronger. |\\n| **5: generic BO explanation** | **Merge into slide 7; keep original as backup or delete** | DAC experts do not need a generic advocacy slide, but novices need one plain-language sentence and a concrete acquisition visual. |\\n| **6: specimen information value/experiment-first** | **Merge into slide 4; keep evidence-rich version as backup** | The argument is structurally necessary, but the current title is vague and the visual does not support the scoped calibration-versus-campaign-cost decision. |\\n| **11: results** | **Unhide and expand to three slides** | Results are the evidentiary center of the talk and were allocated five minutes in the agreed outline. An empty or hidden results slot makes the main claim untenable. |\\n\\n## Visible-slide claims needing evidence, hedging, or backup\\n\\n| Slide | Claim | Required treatment |\\n|---|---|---|\\n| **1** | \\u201cBuild better tensegrity structures faster\\u201d | Define \\u201cbetter\\u201d with the two objectives and force constraint; support \\u201cfaster\\u201d with the results comparison. |\\n| **2** | Tensegrity is robust and reusable for planetary landings | Cite the Super Ball Bot source; distinguish concept evidence from this study; provide repeat-impact evidence or hedge reusability. |\\n| **2** | Parachutes and retrorockets struggle in the stated conditions | Cite an authoritative aerospace source if retained in narration; avoid implying universal inadequacy. |\\n| **4** | Current tensegrity design is slow and resource intensive | Quantify candidate count, print/test time, hand-assembly burden, or calibration effort. |\\n| **7** | BO makes experiments affordable with limited, noisy data | Show campaign budget, noise characterization, replicates, baseline, and performance versus tests. Prepare qNEHVI and Gaussian-process details in backup. |\\n| **7** | Six variables, two input-data types, two objectives | Reconcile with the apparent seven-variable abstract specification and identify every quantity. Clarify \\u201cinput-data types.\\u201d |\\n| **8** | Multi-material AM supplies data fast | Report print-to-result cycle time and failure rate. |\\n| **8** | Single-build co-fabrication with no joining | Show interface and post-processing; use \\u201cno joining of PLA and TPU members\\u201d if other manual operations remain. |\\n| **9** | Accelerometers gather the relevant impact data | Show sensor locations and the derivation of transmitted force, specific energy absorption, and compaction efficiency. Reconcile this wording with the abstract\\u2019s instrumented-tup description. |\\n| **10** | The loop significantly accelerates optimization | Remove \\u201csignificantly\\u201d unless a defined baseline and uncertainty support it. Statistical significance should not be implied casually. |\\n| **12** | Workflow will transfer to lattices and other applications | Present as conditional future work with transfer criteria, not an achieved result. Attribute any external crutch-tip image and claim. |\\n\\n### Prepared backup slides\\n\\n1. Full design-variable table, bounds, encoding, and fixed versus optimized process parameters.\\n2. qNEHVI implementation, Gaussian-process assumptions, constraint treatment, batch size, and acquisition settings.\\n3. Noise plan: replicates, periodic control, heteroscedasticity, drift, outlier policy, and uncertainty propagation.\\n4. Budget-matched baseline definition and evaluation metric.\\n5. Force-cap rationale and full objective definitions.\\n6. Sensor chain, calibration, sample rate, filtering, fixture dynamics, and metric extraction.\\n7. PLA\\u2013TPU interface, conditioning, build layout, dimensional/mass deviations, and print-failure modes.\\n8. Scoped FEA rationale with citations or internal validation; possible analytical or multi-fidelity extensions.\\n9. Automation map and cycle-time breakdown.\\n10. Repeated-impact behavior if reusability remains in the opening.\\n\\n## Merged, ordered presenter TODO list\\n\\n### Must fix before the talk\\n\\n1. **Freeze the scientific specification.** Reconcile the number and type of decision variables, whether FDM parameters are optimized or controlled, the two objectives, the force constraint, treatment of print failures, and the measurement chain. Use one formulation across slides, abstract, paper, and Q&A.\\n2. **Set a results completion plan.** Establish dates and owners for campaign completion, repeat tests, data quality review, baseline analysis, and slide freeze. Predeclare the fallback claim if the baseline does not support \\u201cdozens rather than hundreds.\\u201d\\n3. **Build the three-slide results sequence.** Campaign ledger \\u2192 budget-matched baseline \\u2192 measured feasible Pareto front. Allocate approximately five minutes, as the agreed outline requires.\\n4. **Add the proxy statement to slide 2.** State plainly that the PLA\\u2013TPU T3 specimen is a workflow proxy and not flight hardware.\\n5. **Redesign slide 7.** Show the exact constrained multi-objective problem, test budget, small batches, uncertainty, noisy observations, and the role of qNEHVI.\\n6. **Remove unsupported result language.** Until supported, delete \\u201csignificantly\\u201d from slide 10 and avoid \\u201cdozens rather than hundreds\\u201d in the title, narration, or close.\\n7. **Clarify experiment-first positioning.** Merge the information-value argument into slide 4 and prepare an evidence-backed FEA backup slide.\\n8. **Make the experiment auditable.** On slide 9 or backup, reconcile accelerometers and the instrumented tup; show how all three performance metrics are calculated and how uncertainty is handled.\\n9. **De-risk every video.** Store local trimmed files, disable distracting loops, test them on presentation hardware, and include static fallback frames.\\n10. **Rehearse to 13:30\\u201314:00.** Protect the results and conclusion from being cut. Practice once without slides, consistent with Doumont\\u2019s preparation advice.\\n\\n### Should fix\\n\\n11. **Replace slide 8\\u2019s generic print emphasis with mechanism and materials evidence.** Label PLA, TPU, interface strategy, prestress status, deformation, post-processing, and failure modes.\\n12. **Quantify slide 4.** Give at least one campaign-relevant cost, duration, or candidate-count estimate.\\n13. **Show automation boundaries on slide 10.** Candidate selection and surrogate updating may be automated; slicing, handling, test setup, and testing remain manual according to the abstract.\\n14. **Convert slide 12 to a gated roadmap.** Separate demonstrated T3-proxy results from future lattices, flight materials, and terrestrial applications.\\n15. **Turn slide 13 into the conclusion.** Leave the audience with the best result and transfer rule while taking questions.\\n16. **Prepare technical backup slides.** Prioritize baseline fairness, noise, force-cap rationale, print failures, materials repeatability, and FEA positioning.\\n\\n### Polish\\n\\n17. Replace the slide 1 title with a specific message, such as **\\u201cPhysical Bayesian optimization searches multi-material tensegrity absorbers one small test batch at a time.\\u201d**\\n18. Left-align titles and use intentional line breaks; check every slide at six-per-page scale.\\n19. Use one purposeful visual per slide. Avoid decorative loading or elbow animations.\\n20. Standardize terminology: \\u201cBayesian optimization,\\u201d not \\u201cBays Opt\\u201d; \\u201ctensegrity-inspired\\u201d where appropriate; define specific energy absorption once.\\n21. Add citations directly beside borrowed footage, images, and quantitative claims.\\n\\n## Who the deck serves best and worst\\n\\n- **Best served:** **P6, the friendly industry generalist.** The visible sequence is visually intuitive, practical, and easy to reduce to \\u201cprint \\u2192 test \\u2192 learn \\u2192 repeat.\\u201d\\n- **Worst served:** **P1, the skeptical BO insider.** The current deck omits nearly every item needed to judge a DAC-10 contribution: exact problem formulation, budget, baseline, noise model, constraint handling, and empirical adaptive advantage.\\n\\nThat is **not the right trade-off for DAC-10**. The deck should remain accessible to P4 and P6, but its technical center must satisfy P1 without turning into a BO lecture. One rigorous optimization slide, three quantitative results slides, and targeted backup material would achieve that balance.\\n\\n## Predicted reception\\n\\n- **As is: 4/10.** The physical workflow and application are memorable, but the absent scope statement, empty results section, vague BO slide, and unsupported acceleration claim leave the DAC contribution unproven.\\n- **After the must-fixes: 8/10.** A scoped proxy claim, reconciled methods, fair budget-matched comparison, feasible Pareto front, and measured conclusion would turn the same visual story into a credible design-optimization application talk.\\n\\n## Discretionary analytical decisions\\n\\n- Evaluated only the visible slide order for the simulated in-room reactions, while using hidden slides and presenter notes to judge revision options.\\n- Treated planned placeholder visuals as if implemented according to the presenter\\u2019s notes, but judged the selected visual concept and associated stage risk rather than absent graphic quality.\\n- Prioritized the Draft 3 outline over conflicting Draft 1 wording because the prompt identifies it as the agreed story arc, scope framing, results plan, and timing plan.\\n- Rated objections qualitatively as moderate, high, or critical rather than assigning unsupported numerical probabilities.\\n- Recommended three results slides rather than preserving one reserved slot because the agreed outline specifies three distinct evidentiary products and allocates five minutes to results.\\n- Recommended merging hidden slides 3, 5, and 6 rather than simply unhiding them because their necessary ideas can be conveyed with higher signal-to-noise on existing slides.\\n- Treated the six-versus-seven-variable and optimized-versus-fixed-process-parameter differences as unresolved specification conflicts requiring reconciliation, rather than inferring which document is correct.\\n- Assessed claims against the supplied deck extraction, Draft 3 outline, Doumont notes, and submitted abstract only; no independent validation of cited literature or unseen experimental data was attempted.\"}", + "name": "submit_answer" + } + } + ] + }, + "logprob": null + } + } + }, + "created_at": "2026-07-26T02:31:54.352678Z" + }, + { + "id": "1687b88d-8523-490b-90de-11bc6797a42c", + "trajectory_id": "afade0b1-f49e-4031-a86d-cb5ec4652536", + "trajectory_timestep": 0, + "step": "before_transition", + "state": { + "transition": { + "timestep": 0, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ], + "info": null + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Tensegrity Slides \u2014 Draft 1 (extracted content)\n 2\u2192\n 3\u2192Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4\u2192\n 5\u2192\n 6\u2192## Slide 1 \u2014 layout: Title Only\n 7\u2192\n 8\u2192**On-slide text:**\n 9\u2192- **Title 1**: Let\u2019s build better tensegrity structures faster\n 10\u2192- **TextBox 4**: Marcus Madsen\n 11\u2192Undergraduate researcher under Sterling Baird, Ph.D.\n 12\u2192- **TextBox 6**: IDETC 2026\n 13\u2192\n 14\u2192**Non-text shapes / media / placeholders:**\n 15\u2192- [PICTURE (13)] Picture 3\n 16\u2192- [PICTURE (13)] Picture 8\n 17\u2192\n 18\u2192**Presenter notes:**\n 19\u2192> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20\u2192> \n 21\u2192> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22\u2192\n 23\u2192\n 24\u2192## Slide 2 \u2014 layout: Title Only\n 25\u2192\n 26\u2192**On-slide text:**\n 27\u2192- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings\u2014and to problems closer to home.\n 28\u2192\n 29\u2192**Non-text shapes / media / placeholders:**\n 30\u2192- [MEDIA (16)] Online Media 5\n 31\u2192\n 32\u2192**Presenter notes:**\n 33\u2192> Replace this video with a downloaded and trimmed version of just the fall. \n 34\u2192> \n 35\u2192> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle \u2014 thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander\u00a0is\u00a0the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit\u00a0and get back up\u00a0\u2014 it survives multiple drops and keeps working.\n 36\u2192\n 37\u2192\n 38\u2192## Slide 3 (HIDDEN) \u2014 layout: Title Only\n 39\u2192\n 40\u2192**On-slide text:**\n 41\u2192- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42\u2192- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43\u2192- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44\u2192\n 45\u2192**Presenter notes:**\n 46\u2192> This might be better explained purely vocally while on the previous slide.\n 47\u2192\n 48\u2192\n 49\u2192## Slide 4 \u2014 layout: Title Only\n 50\u2192\n 51\u2192**On-slide text:**\n 52\u2192- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53\u2192- **TextBox 3**: Iteration\n 54\u2192- **TextBox 5**: Building\n 55\u2192- **TextBox 7**: Testing\n 56\u2192\n 57\u2192**Non-text shapes / media / placeholders:**\n 58\u2192- [MEDIA (16)] AdobeStock_207717222\n 59\u2192- [MEDIA (16)] Online Media 2\n 60\u2192- [PICTURE (13)] Picture 14\n 61\u2192- [PICTURE (13)] Picture 16\n 62\u2192- [PICTURE (13)] Picture 18\n 63\u2192\n 64\u2192**Presenter notes:**\n 65\u2192> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn\u2019t become distracting because it\u2019s fun to watch it spin.\n 66\u2192> \n 67\u2192> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68\u2192\n 69\u2192\n 70\u2192## Slide 5 (HIDDEN) \u2014 layout: Title Only\n 71\u2192\n 72\u2192**On-slide text:**\n 73\u2192- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74\u2192- **TextBox 14**: Baird et al. Matter (2022)\n 75\u2192\n 76\u2192**Non-text shapes / media / placeholders:**\n 77\u2192- [GROUP (6)] Group 2\n 78\u2192\n 79\u2192**Presenter notes:**\n 80\u2192> Consult Sterling on this slide. I don\u2019t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it\u2019s well enough known that it\u2019d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81\u2192\n 82\u2192\n 83\u2192## Slide 6 (HIDDEN) \u2014 layout: Title Only\n 84\u2192\n 85\u2192**On-slide text:**\n 86\u2192- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87\u2192- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88\u2192\n 89\u2192**Presenter notes:**\n 90\u2192> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91\u2192\n 92\u2192\n 93\u2192## Slide 7 \u2014 layout: Title Only\n 94\u2192\n 95\u2192**On-slide text:**\n 96\u2192- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97\u2192- **TextBox 5**: 6 variables\n 98\u2192- **TextBox 7**: 2 types of input data\n 99\u2192- **TextBox 9**: 2 objectives\n100\u2192- **TextBox 17**: *image of blackbox*\n101\u2192\n102\u2192**Non-text shapes / media / placeholders:**\n103\u2192- [LINE (9)] Connector: Curved 11\n104\u2192- [LINE (9)] Connector: Curved 13\n105\u2192- [LINE (9)] Connector: Curved 15\n106\u2192\n107\u2192**Presenter notes:**\n108\u2192> Very rough slide as I\u2019m not entirely sure what graphic or text to put on this, partially because I\u2019m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109\u2192\n110\u2192\n111\u2192## Slide 8 \u2014 layout: Title Only\n112\u2192\n113\u2192**On-slide text:**\n114\u2192- **Title 1**: Multi-material additive manufacturing supplies that data fast \u2014 single-build co-fabrication, with no joining of parts.\n115\u2192\n116\u2192**Non-text shapes / media / placeholders:**\n117\u2192- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118\u2192- [PICTURE (13)] Picture 4\n119\u2192\n120\u2192**Presenter notes:**\n121\u2192> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122\u2192\n123\u2192\n124\u2192## Slide 9 \u2014 layout: Title Only\n125\u2192\n126\u2192**On-slide text:**\n127\u2192- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128\u2192- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129\u2192- **TextBox 5**: *placeholder for slo motion of drop test*\n130\u2192\n131\u2192\n132\u2192## Slide 10 \u2014 layout: Title Only\n133\u2192\n134\u2192**On-slide text:**\n135\u2192- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136\u2192- **TextBox 3**: Printing\n137\u2192- **TextBox 5**: Testing\n138\u2192- **TextBox 7**: BO feed\n139\u2192\n140\u2192**Non-text shapes / media / placeholders:**\n141\u2192- [PICTURE (13)] Picture 14\n142\u2192- [PICTURE (13)] Picture 16\n143\u2192- [PICTURE (13)] Picture 18\n144\u2192- [PICTURE (13)] Graphic 8\n145\u2192\n146\u2192**Presenter notes:**\n147\u2192> I think it might be better if we replaced \u201cprinting\u201d with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148\u2192\n149\u2192\n150\u2192## Slide 11 (HIDDEN) \u2014 layout: Title Only\n151\u2192\n152\u2192**On-slide text:**\n153\u2192- **Title 1**: Reserved for results\n154\u2192\n155\u2192**Presenter notes:**\n156\u2192> Would be helpful if we could show that this process works in this case.\n157\u2192\n158\u2192\n159\u2192## Slide 12 \u2014 layout: Title Only\n160\u2192\n161\u2192**On-slide text:**\n162\u2192- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163\u2192- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164\u2192- **TextBox 3**: *image to show lattice structure\n165\u2192\n166\u2192\n167\u2192## Slide 13 \u2014 layout: Title Only\n168\u2192\n169\u2192**Non-text shapes / media / placeholders:**\n170\u2192- [PICTURE (13)] Picture 3\n171\u2192\n172\u2192**Presenter notes:**\n173\u2192> Slide for questions at the end\n174\u2192\n175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "info": { + "start_ts": 8147.330900226, + "end_ts": 8147.337548875 + } + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Presentation \u2014 Doumont Presentation Structure Template\n 2\u2192\n 3\u2192Transcribed from Jean-luc Doumont's presentation-structure template\n 4\u2192([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5\u2192adapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\n 6\u2192All rights reserved. Can be downloaded from .\n 7\u2192\n 8\u2192> To design your presentation, write down your ideas for each component below.\n 9\u2192> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10\u2192\n 11\u2192**Draft 3** \u2014 applies the inline review comments from @sgbaird on PR #84 and the\n 12\u2192priority revisions from the\n 13\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14\u2192Summaries of what changed are at the bottom of this file\n 15\u2192([Draft 2 \u2192 3](#changes-from-draft-2), [Draft 1 \u2192 2](#changes-from-draft-1)).\n 16\u2192\n 17\u2192**Scope note (frame everything below with this):** the study presented is the\n 18\u2192optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19\u2192processing parameters** (e.g., nozzle temperature, print speed). PLA\u2013TPU is a\n 20\u2192**proxy system** \u2014 not flight material \u2014 used to prototype a self-driving-lab\n 21\u2192style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22\u2192The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23\u2192beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24\u2192\n 25\u2192## Opening\n 26\u2192\n 27\u2192### Attention getter\n 28\u2192\n 29\u2192*A way to lead the audience to the need efficiently*\n 30\u2192\n 31\u2192Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32\u2192landings \u2014 and to problems closer to home.\n 33\u2192\n 34\u2192One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35\u2192clip) do the work:\n 36\u2192\n 37\u2192> Landing a payload on another planet means surviving an impact in conditions\n 38\u2192> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n 39\u2192> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40\u2192> structure: the lander *is* the energy absorber. And unlike crushable\n 41\u2192> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42\u2192> hit **and get back up** \u2014 it survives multiple drops and keeps working.\n 43\u2192\n 44\u2192Candidate Super Ball Bot videos to show (verified links):\n 45\u2192\n 46\u2192- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n 47\u2192 (official channel); the canonical overview.\n 48\u2192- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49\u2192 \u2014 Wall Street Journal; prototype drop footage.\n 50\u2192- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51\u2192 \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n 52\u2192\n 53\u2192Immediately after the hook, state the present study scope in one sentence\n 54\u2192(per the scope note above): *we study a printed PLA\u2013TPU T3 prism as a proxy\n 55\u2192system for prototyping an autonomous tensegrity-optimization workflow* \u2014 so\n 56\u2192the planetary framing never outruns the demonstrated scope.\n 57\u2192\n 58\u2192\n 60\u2192\n 61\u2192### Need\n 62\u2192\n 63\u2192*A difference between actual and desired situations*\n 64\u2192\n 65\u2192**Actual situation** (a two-sided pincer \u2014 stated as a scoped engineering\n 66\u2192judgment, not an absolute):\n 67\u2192\n 68\u2192- For these structures, obtaining trustworthy objective values from a\n 69\u2192 validated high-fidelity model would require substantial calibration of\n 70\u2192 interface, rate, contact, and defect behavior \u2014 and even heavy-duty\n 71\u2192 simulation falls short of the experiments we are running: peak transmitted\n 72\u2192 shock is a millisecond-scale transient that is notoriously difficult to\n 73\u2192 resolve numerically. This holds even for idealized cable\u2013strut tensegrities\n 74\u2192 in their actual engineering materials (steel or similar), not just for\n 75\u2192 printed PLA\u2013TPU.\n 76\u2192 \n 79\u2192- Exhaustive physical search fails on cost: a traditional design of\n 80\u2192 experiments over this relatively high-dimensional space (geometry + FDM\n 81\u2192 process parameters) would demand hundreds of specimens, each costing print\n 82\u2192 and test time.\n 83\u2192- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84\u2192 more complex structures \u2014 especially tensegrity *lattices* \u2014 become\n 85\u2192 extremely difficult and ultimately infeasible to hand-build.\n 86\u2192\n 87\u2192**Desired situation:**\n 88\u2192\n 89\u2192- Trustworthy performance data without hand assembly (easy creation of\n 90\u2192 tensegrity structures, scalable beyond a single prism).\n 91\u2192- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n 92\u2192 not the hundreds a traditional design of experiments would require.\n 93\u2192\n 94\u2192### Task\n 95\u2192\n 96\u2192*What I decided/was asked to do to address the need*\n 97\u2192\n 98\u2192Build a closed-loop design\u2013print\u2013test workflow that optimizes a **T3-prism\n 99\u2192tensegrity-inspired energy absorber together with its FDM processing\n100\u2192parameters** (e.g., temperature, print speed) directly from real, measured\n101\u2192impact data \u2014 maximizing energy absorbed per gram (specific energy\n102\u2192absorption) and compaction efficiency, subject to a cap on the peak force\n103\u2192transmitted to the payload. Extensions beyond the T3 prism are future work.\n104\u2192\n105\u2192### Main message\n106\u2192\n107\u2192*The one sentence I want my audience to remember*\n108\u2192\n109\u2192By closing the loop between multi-material 3D printing and Bayesian\n110\u2192optimization, we can optimize tensegrity-inspired energy absorbers directly\n111\u2192from real impact data \u2014 in dozens of prints, not the hundreds a traditional\n112\u2192design of experiments would demand.\n113\u2192\n114\u2192\n118\u2192\n119\u2192### Preview\n120\u2192\n121\u2192*A map of the body (ideally three points, max. five)*\n122\u2192\n123\u21921. When model calibration and exhaustive testing are both costly, each\n124\u2192 physical specimen must be chosen for its information value \u2014 so the\n125\u2192 measured experiment is our source of truth.\n126\u21922. Bayesian optimization makes physical experimentation affordable: it handles\n127\u2192 noisy measurements, small batches, and the constrained multi-objective\n128\u2192 trade-off \u2014 and Honegumi let us scaffold it in minutes.\n129\u21923. Multi-material additive manufacturing closes the loop: single-build PLA\u2013TPU\n130\u2192 co-fabrication turns each proposed design into test data in hours, with no\n131\u2192 joining of parts.\n132\u21924. *(Optional fourth point, if time allows)* The same closed loop points at\n133\u2192 what comes next: tensegrity lattices, flight-relevant materials, and\n134\u2192 safety hardware on Earth.\n135\u2192\n136\u2192## Body\n137\u2192\n138\u2192### Point 1\n139\u2192\n140\u2192When model calibration and exhaustive testing are both costly, each physical\n141\u2192specimen must be chosen for its information value.\n142\u2192\n143\u2192- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144\u2192 these structures, a sufficiently calibrated multiphysics model is expensive\n145\u2192 to build relative to the design campaign itself \u2014 and the hardest quantity,\n146\u2192 peak transmitted shock, is a millisecond-scale transient that even\n147\u2192 heavy-duty simulation struggles to resolve. The gap persists in the actual\n148\u2192 engineering materials a real lander would use (steel struts and cables),\n149\u2192 so this is not merely an artifact of printed PLA\u2013TPU.\n150\u2192- The alternative \u2014 sweeping the design space physically \u2014 fails on cost: a\n151\u2192 traditional design of experiments over geometry plus FDM process parameters\n152\u2192 would take hundreds of specimens, each needing print + test time.\n153\u2192- Therefore: treat the physical experiment as the authoritative source of\n154\u2192 objective values, and be ruthless about *which* experiments to run.\n155\u2192- Anticipate the FEA-veteran pushback (\"inconvenient \u2260 untrustworthy\"): frame\n156\u2192 it as a cost-and-dependence decision, and keep a backup slide with the\n157\u2192 literature (or our own validation) on simulation\u2013experiment discrepancy for\n158\u2192 impact transients.\n159\u2192\n160\u2192### Transition\n161\u2192\n162\u2192If every data point must be a real experiment, we need a method that extracts\n163\u2192the most from every specimen \u2014 we need to make the process radically more\n164\u2192sample-efficient.\n165\u2192\n166\u2192### Point 2\n167\u2192\n168\u2192Bayesian optimization makes physical experimentation affordable, even with\n169\u2192limited, noisy data.\n170\u2192\n171\u2192- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172\u2192 visual in the main talk; implementation detail goes to backup slides.\n173\u2192 Spoken version:\n174\u2192 > \"After each batch, the model estimates both expected performance and\n175\u2192 > uncertainty across the design space. The acquisition rule selects a small\n176\u2192 > next batch that is most likely to expand the feasible trade-off frontier,\n177\u2192 > while accounting for noisy tests and print failures.\"\n178\u2192- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179\u2192 motivate a noise-aware acquisition function (qNEHVI) rather than\n180\u2192 non-noise-aware alternatives. (Backup slide: how observation noise is\n181\u2192 actually modeled \u2014 replicates, noise identification \u2014 and budget-matched\n182\u2192 baselines such as Sobol/random sampling.)\n183\u2192- Small parallel batches: the surrogate proposes the next batch of specimens\n184\u2192 to print, balancing exploration and exploitation.\n185\u2192- Constrained multi-objective by construction: **objectives** = maximize\n186\u2192 specific energy absorption and compaction efficiency; **constraint** = peak\n187\u2192 transmitted force must stay under a cap; the deliverable is the *feasible*\n188\u2192 Pareto front between the two objectives among force-cap-satisfying designs.\n189\u2192 (Keep this formulation consistent everywhere \u2014 do not present \"SEA vs. peak\n190\u2192 force\" as the trade-off.)\n191\u2192- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192\u2192 this advanced task (noisy, batched, constrained, multi-objective) in\n193\u2192 minutes using [Honegumi](https://honegumi.readthedocs.io/) \u2014 the audience\n194\u2192 can do the same for their own problems; show the code-template picker\n195\u2192 briefly so they see how low the barrier is.\n196\u2192\n197\u2192### Transition\n198\u2192\n199\u2192The optimizer is no longer the bottleneck \u2014 the limit is now providing real\n200\u2192data quickly enough.\n201\u2192\n202\u2192### Point 3\n203\u2192\n204\u2192Multi-material additive manufacturing supplies that data fast \u2014 single-build\n205\u2192co-fabrication, with no joining of parts.\n206\u2192\n207\u2192- Single-build PLA\u2013TPU co-fabrication: rigid struts and flexible tension\n208\u2192 network in one print \u2014 no joining or hand assembly of members, which closes\n209\u2192 the \"easy creation\" gap from the Need and is what makes future lattices\n210\u2192 feasible at all. (Be precise: if support removal, trimming, or conditioning\n211\u2192 remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212\u2192 assembly.\")\n213\u2192- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214\u2192 as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215\u2192 pre-tension status) plus a short deformation sequence \u2014 so the EDL engineer\n216\u2192 doesn't file it as a \"crush structure\" and the grad student can tell it from\n217\u2192 an ordinary lattice.\n218\u2192- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219\u2192 tested specimen (quasi-static compression + instrumented drop-weight impact)\n220\u2192 in hours. (Backup slide: representative print-to-result time breakdown and\n221\u2192 which steps remain manual \u2014 candidate selection is automated; slicing,\n222\u2192 handling, and testing are not yet.)\n223\u2192- Now we have the data we need to complete the loop.\n224\u2192\n225\u2192### Point 4 (optional \u2014 future applications)\n226\u2192\n227\u2192*Include if timing allows; otherwise fold into the Conclusion.*\n228\u2192\n229\u2192The same closed loop transfers wherever tests are authoritative, each test is\n230\u2192costly, the design space is mixed, and several performance goals conflict:\n231\u2192\n232\u2192- Tensegrity *lattices* and more complex architectures \u2014 exactly where hand\n233\u2192 assembly becomes infeasible and single-build co-fabrication pays off.\n234\u2192- Migration from the PLA\u2013TPU proxy toward flight-relevant materials and\n235\u2192 impact regimes.\n236\u2192- Safety hardware on Earth (e.g., the tensegrity crutch tip \u2014 future work).\n237\u2192- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238\u2192 handling, testing) in the loop.\n239\u2192\n240\u2192### Evidence / results\n241\u2192\n242\u2192*What we found \u2014 ground the conclusion in shown data*\n243\u2192\n244\u2192Build the talk around a quantitative three-slide sequence (per the\n245\u2192mock-audience report; insert observed values once the campaign completes):\n246\u2192\n247\u21921. **Campaign ledger** \u2014 \"The campaign used N specimens across B adaptive\n248\u2192 batches under a fixed physical-test budget\": initialization vs. adaptive\n249\u2192 batches, replicates, print failures, cycle time, and the numerical value\n250\u2192 (and rationale) of the force cap.\n251\u21922. **Budget-matched baseline** \u2014 \"Adaptive selection improved the feasible\n252\u2192 design set faster than [predeclared baseline]\": performance vs. number of\n253\u2192 physical tests against Sobol/random sampling at the same budget, with\n254\u2192 uncertainty where available.\n255\u21923. **Measured feasible Pareto front** \u2014 specific energy absorption vs.\n256\u2192 compaction efficiency among designs satisfying the force cap, with\n257\u2192 replicate spread, marked infeasible designs, and specimen photos or\n258\u2192 force\u2013time traces for representative Pareto designs.\n259\u2192\n260\u2192Supporting raw-data candidates: drop-test force\u2013time traces, Sobol /\n261\u2192first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262\u2192\n263\u2192### Suggested 15-minute allocation\n264\u2192\n265\u2192*(From the mock-audience report \u2014 results get the largest block.)*\n266\u2192\n267\u2192| Time | Content |\n268\u2192|---|---|\n269\u2192| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270\u2192| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271\u2192| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272\u2192| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273\u2192| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274\u2192| 1 min | Limitations and transfer conditions |\n275\u2192| 1 min | Measured conclusion + bookend |\n276\u2192\n277\u2192Move kernel choices, encodings, and other implementation detail to backup\n278\u2192slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279\u2192[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280\u2192\n281\u2192## Closing\n282\u2192\n283\u2192### Review\n284\u2192\n285\u2192*A recap of the body, leading into the conclusion*\n286\u2192\n287\u2192Using real, measured impact data from additively manufactured\n288\u2192tensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\n289\u2192optimization routine \u2014 we rapidly iterate toward better energy absorbers.\n290\u2192This feedback loop makes tensegrity optimization dramatically faster: dozens\n291\u2192of prints instead of the hundreds a traditional design of experiments over\n292\u2192this design space would require.\n293\u2192\n294\u2192### Conclusion\n295\u2192\n296\u2192*What the above means to the audience in the end*\n297\u2192\n298\u2192Because the loop runs on physical measurements, it is useful precisely where\n299\u2192model calibration is not worth its cost \u2014 and the same closed-loop pattern\n300\u2192should transfer (a conditional claim, pending evidence) to other additively\n301\u2192manufactured architectures whose performance is dominated by\n302\u2192hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303\u2192*use this loop when tests are authoritative, each test is costly, the design\n304\u2192space is mixed, and several performance goals conflict.* For tensegrity\n305\u2192specifically, it opens a path from this T3 proxy study toward lattices,\n306\u2192flight-relevant materials, payload protection on other planets, and safety\n307\u2192hardware here on Earth (e.g., the crutch tip, as future work).\n308\u2192\n309\u2192### Close\n310\u2192\n311\u2192*A way to end the presentation clearly and elegantly*\n312\u2192\n313\u2192Return to the opening image: the next planetary lander's energy absorber \u2014 a\n314\u2192structure that has to survive not one impact but many \u2014 doesn't have to take\n315\u2192years of hand-built prototypes; with this loop, candidate designs can be\n316\u2192designed, printed, and tested against real impacts in weeks instead of months\n317\u2192or longer. End on the measured engineering result and its transfer condition,\n318\u2192then the bookend: every method that compresses design time moves our ventures\n319\u2192\u2014 on this planet and beyond \u2014 forward.\n320\u2192\n321\u2192---\n322\u2192\n323\u2192## Changes from Draft 2\n324\u2192\n325\u2192Applied from @sgbaird's inline review comments on PR #84 and the\n326\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327\u2192\n328\u2192**From the inline review comments:**\n329\u2192\n330\u21921. **Reusability in the hook** (line 22): the tensegrity lander survives\n331\u2192 multiple drops, unlike crushable/sacrificial alternatives.\n332\u21922. **Proxy-system framing** (lines 49, 87): PLA\u2013TPU would never fly; it is a\n333\u2192 proxy for prototyping a self-driving-lab-style workflow \u2014 stated in a\n334\u2192 scope note up front and re-stated right after the hook.\n335\u21923. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336\u2192 heavy-duty simulation falls short of the experiments (millisecond-scale\n337\u2192 peak shock), not that it merely costs too much.\n338\u21924. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339\u2192 complex structures and especially lattices are not.\n340\u21925. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341\u2192 (temperature, print speed, \u2026) only; the rest is explicitly future work.\n342\u21926. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343\u2192 literature search (or validation of experiments against simulation), plus\n344\u2192 the millisecond-resolution peak-shock argument.\n345\u21927. **Optional Point 4 added** (line 97): future applications, with a note it\n346\u2192 can fold into the Conclusion if time is short.\n347\u21928. **Steel-materials point** (line 104): simulations fall short for the\n348\u2192 actual engineering materials (steel struts/cables), not just printed\n349\u2192 PLA\u2013TPU.\n350\u21929. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351\u2192 in minutes, pitched so the audience sees they can use it too.\n352\u219210. **\"Thousands\" \u2192 \"hundreds\"** (line 173): baseline is now a traditional\n353\u2192 design of experiments over a high-dimensional space (hundreds), in the\n354\u2192 main message, Need, Point 1, and Review.\n355\u219211. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356\u2192 or longer\" for candidate designs.\n357\u2192\n358\u2192**From the mock-audience report's priority revisions:**\n359\u2192\n360\u219212. **Quantitative three-slide results sequence** (Priority 1): campaign\n361\u2192 ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front, with a\n362\u2192 fallback main-message wording if the campaign can't yet support the\n363\u2192 efficiency claim.\n364\u219213. **Anti-simulation premise replaced with a scoped cost decision**\n365\u2192 (Priority 2): Point 1 retitled to the information-value message;\n366\u2192 \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367\u2192 anticipated with a backup slide.\n368\u219214. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369\u2192 implementation detail moved to backup slides; Q&A prep pointed at the\n370\u2192 report's claims-vs-evidence table.\n371\u219215. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372\u2192 the main talk, machinery in backup slides; the misleading\n373\u2192 \"qNEHVI over standard expected improvement\" comparison replaced with\n374\u2192 \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375\u219216. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376\u2192 sequence in Point 3, so the structure isn't remembered as a generic crush\n377\u2192 structure or lattice.\n378\u219217. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379\u2192 co-fabrication / no joining of parts,\" with a note to list remaining\n380\u2192 manual steps and an automation-status caveat.\n381\u219218. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382\u2192 compaction efficiency, constraint = force cap, deliverable = *feasible*\n383\u2192 Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384\u219219. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385\u2192 transfer claim marked conditional, and the close ends on the measured\n386\u2192 result before the bookend.\n387\u2192\n388\u2192## Changes from Draft 1\n389\u2192\n390\u2192Feedback from the PR #84 review, applied in Draft 2:\n391\u2192\n392\u21921. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n393\u2192 dozens-not-thousands claim); body and message now carry the same story.\n394\u21922. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n395\u2192 manuscript's positioning (#75/#76) is that the physical experiment is the\n396\u2192 source of truth, explicitly avoiding calibrated FE simulation.\n397\u21923. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n398\u2192 parametric design space and avoid an over-claim to a design-automation\n399\u2192 audience.\n400\u21924. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401\u2192 energy absorption and compaction efficiency, cap peak transmitted force.\n402\u21925. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403\u2192 *and* the space is too big to brute-force \u2014 which makes BO + AM the\n404\u2192 inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405\u2192 above.)*\n406\u21926. **Point 1 given its own content** (the *why* behind the inefficiency)\n407\u2192 instead of restating the Need.\n408\u21927. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409\u2192 the specifics (noise-aware acquisition, small batches, multi-objective).\n410\u21928. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411\u2192 (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412\u2192 above.)*\n413\u21929. **Evidence/results slot added** before the Closing, so the Conclusion is\n414\u2192 grounded in shown data rather than three hedged \"may\"s.\n415\u219210. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n416\u2192 Ball Bot visual; verified video links included.\n417\u219211. **Close bookends the opening** (the next lander's absorber, designed in\n418\u2192 weeks) before the broader humankind line.\n419\u219212. **Spelling**: Doumont (was \"Duomont\").", + "name": "read", + "tool_call_id": "call_93haurOMDGiH2mQFFbaMaYKn", + "info": { + "start_ts": 8147.331065886, + "end_ts": 8147.336467845 + } + }, + { + "role": "tool", + "content": " 1\u2192# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n 2\u2192\n 3\u2192Video: (assigned in #83)\n 4\u2192\n 5\u2192Transcribed from @me-madsen's handwritten notes\n 6\u2192([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7\u2192dated 2026-07-15).\n 8\u2192\n 9\u2192## Overview\n10\u2192\n11\u2192A presentation is all about the right content, displayed the right way, at the\n12\u2192right time.\n13\u2192\n14\u2192- Verbal communication is sequential.\n15\u2192- Message is *interpretation* of the information.\n16\u2192 - Ensure the audience can get the most out of what you're doing.\n17\u2192 - Don't just give them information \u2014 help them understand it, interpret it,\n18\u2192 and internalize it.\n19\u2192\n20\u2192## Doumont's suggested rules for a presentation\n21\u2192\n22\u21920. Ensure you have **messages** for your audience, not just words.\n23\u21921. **Adapt to your audience.**\n24\u2192 - Think like the audience, not the speaker.\n25\u21922. **Maximize signal-to-noise ratio.**\n26\u2192 - Reduce noise (animations, laser pointers, anything that pulls attention\n27\u2192 away from your message).\n28\u21923. **Use effective redundancy.**\n29\u2192 - Tell your message in word *and* on slide: someone deaf should understand\n30\u2192 your messages just as well as someone blind.\n31\u2192 - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32\u2192 date).\n33\u2192\n34\u2192## When creating slides\n35\u2192\n36\u2192- If the audience doesn't need it, why put it on the slides?\n37\u2192- People shouldn't have to count the items on a slide to know how many are\n38\u2192 there.\n39\u2192- Don't use your slides as your \"presentation notes.\"\n40\u2192 - Don't show your notes to the audience.\n41\u2192 - **A bad slide is worse than no slide at all.**\n42\u2192- **One message per slide** is a good rule of thumb.\n43\u2192 - Prioritize the *so what* (or *therefore what*) of your message.\n44\u2192- Make it easy to interpret the information on the slide:\n45\u2192 - 3\u20135 items at most.\n46\u2192 - Align the title left.\n47\u2192 - Do not clutter slides with repetitive or unnecessary information: logos,\n48\u2192 affiliation, date, slide numbers.\n49\u2192- Doumont's suggested workflow:\n50\u2192 1. **Start with the title.**\n51\u2192 - Use a full sentence in the title.\n52\u2192 - The title is the main message of the slide.\n53\u2192 - Make it easy to read: left-aligned, with intentional line breaks.\n54\u2192 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55\u2192- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56\u2192\n57\u2192## General preparation\n58\u2192\n59\u2192- Planning for audience, content, and structure matters more than materials\n60\u2192 (i.e., slides).\n61\u2192- Try using one sheet of paper for presentation preparation.\n62\u2192- It's most important that your presentation is:\n63\u2192 - well planned, structured, and delivered;\n64\u2192 - **if you don't have a lot of time to prepare, don't focus on making\n65\u2192 slides.**\n66\u2192- Practice the presentation a few times. Practice at least once *without* your\n67\u2192 slideshow!\n68\u2192- If doing a handout, keep it separate from the slides you're preparing.\n69\u2192- Impress the audience with your body.\n70\u2192 - Use gestures, not laser pointers.\n71\u2192 - \"Control\" the audience with your body \u2014 keep their attention on your\n72\u2192 presentation.\n73\u2192\n74\u2192## Impressions / what to apply to the IDETC presentation\n75\u2192\n76\u2192\n77\u2192\n78\u2192## Questions / things to discuss\n79\u2192\n80\u2192", + "name": "read", + "tool_call_id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "info": { + "start_ts": 8147.331133416, + "end_ts": 8147.338304015 + } + }, + { + "role": "tool", + "content": " 1\u2192\\documentclass[10pt]{article}\n 2\u2192\n 3\u2192% --- Packages ---\n 4\u2192\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5\u2192\\usepackage[hidelinks]{hyperref}\n 6\u2192\\usepackage{parskip}\n 7\u2192\\usepackage{graphicx}\n 8\u2192\\usepackage{caption}\n 9\u2192\\usepackage{titlesec}\n 10\u2192\\usepackage{wrapfig}\n 11\u2192\\usepackage[numbers,sort&compress]{natbib}\n 12\u2192\n 13\u2192% --- Formatting ---\n 14\u2192\\setlength{\\parskip}{0.2em}\n 15\u2192\\setlength{\\parindent}{0em}\n 16\u2192\\titleformat*{\\section}{\\large\\bfseries}\n 17\u2192\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18\u2192\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19\u2192\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20\u2192\\setlength{\\emergencystretch}{2em}\n 21\u2192\\setlength{\\intextsep}{0.2em}\n 22\u2192\\bibliographystyle{abbrvnat}\n 23\u2192\n 24\u2192\\begin{document}\n 25\u2192\n 26\u2192\\begin{center}\n 27\u2192{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28\u21923D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29\u2192\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30\u2192\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31\u2192\\end{center}\n 32\u2192\n 33\u2192\\vspace{0.4em}\n 34\u2192\n 35\u2192\\section*{Abstract}\n 36\u2192Tensegrity-inspired architectures---rigid struts suspended within a\n 37\u2192continuous flexible network---can exhibit tunable nonlinear\n 38\u2192force--displacement responses and favorable energy absorption per unit\n 39\u2192mass, motivating their use in lightweight energy-absorbing and protective\n 40\u2192structures\n 41\u2192\\citep{skelton2009tensegrity,pajunen2019design}.\n 42\u2192Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43\u2192struts and flexible (TPU) elements in a single build\n 44\u2192\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45\u2192space---strut geometry, tension-element cross-section, connectivity\n 46\u2192topology, and unit-cell tiling---is too large to explore by trial and\n 47\u2192error. We present a closed-loop experimental campaign that uses\n 48\u2192Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49\u2192to drive a design--print--test workflow operating directly on physical\n 50\u2192measurements, without relying on calibrated finite-element simulation\n 51\u2192for objective evaluation.\n 52\u2192\n 53\u2192\\section{Introduction and Motivation}\n 54\u2192\n 55\u2192\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56\u2192\\centering\n 57\u2192\\vspace{-0.4em}\n 58\u2192\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59\u2192\\captionsetup{font=small}\n 60\u2192\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61\u2192Candidate tensegrity-inspired unit cells are instantiated from a\n 62\u2192parameter vector, fabricated by multi-material FDM, and tested under\n 63\u2192quasi-static compression and drop-weight impact. Measured peak force,\n 64\u2192specific energy absorption (SEA), and compaction efficiency update a\n 65\u2192Gaussian-process surrogate that proposes the next batch of designs.}\n 66\u2192\\label{fig:overview}\n 67\u2192\\vspace{-0.4em}\n 68\u2192\\end{wrapfigure}\n 69\u2192\n 70\u2192Tensegrity structures combine compression-only members with a\n 71\u2192pre-tensioned tension network to achieve stiffness without rigid\n 72\u2192joinery~\\citep{skelton2009tensegrity}. Their\n 73\u2192nonlinear, often load-limiting force--displacement response makes them\n 74\u2192attractive for impact mitigation, packaging, and wearable protective\n 75\u2192gear~\\citep{pajunen2019design}. Idealized\n 76\u2192cable--strut tensegrities are difficult to manufacture at scale, but\n 77\u2192\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78\u21923D printing reproduce key tensegrity behaviors---including\n 79\u2192post-buckling stability and load-limiting force--displacement\n 80\u2192response~\\citep{pajunen2019design}---motivating our extension to\n 81\u2192multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82\u2192elements. PLA provides rigid struts; TPU provides flexible,\n 83\u2192rate-dependent tension elements whose viscoelasticity is expected to\n 84\u2192contribute hysteretic energy dissipation under impact loading,\n 85\u2192consistent with the energy-absorbing behavior reported for rigid--TPU\n 86\u2192multi-material prints~\\citep{khatri2024energy}.\n 87\u2192\n 88\u2192The challenge is that small geometric and topological changes can\n 89\u2192produce large changes in the measured response, and high-fidelity\n 90\u2192simulation of FDM specimens is computationally expensive and often\n 91\u2192struggles to capture physical defects such as interfacial slip and\n 92\u2192TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93\u2192trial-and-error sweeps over the joint design space are also infeasible:\n 94\u2192even modest parameter resolutions yield thousands of candidates, each\n 95\u2192requiring print and test time. We instead treat the physical\n 96\u2192experiment as the source of truth and use BO to select which specimens\n 97\u2192are most informative to fabricate next---an approach used in related\n 98\u2192closed-loop materials and structures\n 99\u2192campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100\u2192Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101\u2192set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102\u2192designs using parallel, noisy multi-objective BO driven by measured\n103\u2192$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104\u2192\n105\u2192\\section{Approach}\n106\u2192\\subsection{Closed-Loop Workflow}\n107\u2192Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108\u2192proceeds in four steps: (i)~a parameterized unit-cell design is\n109\u2192instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110\u2192is sliced and printed on a multi-material FDM system; (iii)~it is\n111\u2192tested under quasi-static compression and instrumented drop-weight\n112\u2192impact; and (iv)~the resulting performance metrics update a\n113\u2192Gaussian-process (GP) surrogate, which a batch acquisition function\n114\u2192queries to recommend the next set of candidates. Surrogate updating\n115\u2192and experiment selection are automated; specimen handling, slicing,\n116\u2192and test setup remain manual.\n117\u2192\n118\u2192\\subsection{Design Space}\n119\u2192We parameterize a family of unit cells using a core-wrapping\n120\u2192architecture in which rigid PLA struts are enclosed by continuous TPU\n121\u2192skins; the wrapping rationale follows multi-material FDM strategies in\n122\u2192which rigid panels are wrapped by stretchable soft material to prevent\n123\u2192delamination and improve interfacial bonding at the rigid--flexible\n124\u2192interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125\u2192comprises continuous geometric variables (strut diameter and length,\n126\u2192tension-element width and thickness), an integer strut count per unit\n127\u2192cell, and two categorical variables (connectivity topology, unit-cell\n128\u2192tiling pattern). Categorical choices are encoded explicitly rather\n129\u2192than embedded in a continuous metric, and bounds on the continuous\n130\u2192variables are chosen to remain within the printer's resolution and\n131\u2192overhang limits and to keep specimens in a common form factor for the\n132\u2192test fixtures.\n133\u2192\n134\u2192\\subsection{Surrogate and Acquisition}\n135\u2192The GP surrogate is trained on three measured performance metrics:\n136\u2192peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137\u2192(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138\u2192objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139\u2192likelihood with priors to avoid pathological length scales at small $n$.\n140\u2192Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141\u2192interfacial defects motivates our use of q-noisy expected hypervolume\n142\u2192improvement (qNEHVI), which explicitly accounts for observation noise\n143\u2192in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144\u2192expected-improvement variants. Print failures are treated as a\n145\u2192probabilistic feasibility constraint: the acquisition score for each\n146\u2192candidate is its qNEHVI improvement weighted by the GP-estimated\n147\u2192probability of successful fabrication, following recent constrained\n148\u2192multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149\u2192Where applicable, we use numerically stable log-space variants of\n150\u2192improvement-based acquisitions to mitigate vanishing-gradient\n151\u2192pathologies as data and constraints\n152\u2192accumulate~\\citep{ament2023logei}.\n153\u2192\n154\u2192\\section{Methods}\n155\u2192\\subsection{Fabrication}\n156\u2192Specimens are fabricated on a multi-material FDM system capable of\n157\u2192co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158\u2192Print parameters (temperature, line width, infill, retraction) are\n159\u2192held fixed within a batch to isolate the effect of the design\n160\u2192variables. Each specimen is weighed and dimensionally inspected prior\n161\u2192to testing so that mass and geometric deviations are available as\n162\u2192covariates.\n163\u2192\n164\u2192\\subsection{Mechanical Testing}\n165\u2192Quasi-static compression is performed on a screw-driven load frame at\n166\u2192a fixed nominal strain rate, with force--displacement recorded to\n167\u2192densification. Drop-weight impact testing uses an instrumented tup at\n168\u2192a fixed impact energy; transmitted force is captured at high sample\n169\u2192rate, and specimens are imaged before and after impact to document\n170\u2192failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171\u2192unit mass, and compaction efficiency $\\eta$, defined consistently\n172\u2192across the campaign. A control specimen from an earlier batch is\n173\u2192retested periodically to monitor TPU batch-to-batch and printer drift.\n174\u2192\n175\u2192\\section{Expected Outcomes and Discussion}\n176\u2192We will report how BO-selected designs evolve across iterations, how\n177\u2192GP surrogate predictions and uncertainties change as data accumulate,\n178\u2192and which Pareto-efficient trade-offs emerge between SEA and peak\n179\u2192transmitted force within the explored design space. We will also\n180\u2192discuss practical lessons for operating the closed-loop workflow,\n181\u2192including handling of print failures, batch-to-batch variability in\n182\u2192TPU response, the influence of the rigid--flexible interface on\n183\u2192energy absorption, and the exploration--exploitation balance of the\n184\u2192acquisition function under realistic experimental noise. We expect\n185\u2192the campaign to clarify where physical experimentation is genuinely\n186\u2192required versus where simpler analytical or multi-fidelity surrogate\n187\u2192shortcuts~\\citep{mo2023accelerated} could be\n188\u2192substituted in future work.\n189\u2192\n190\u2192\\section{Conclusion}\n191\u2192This work demonstrates a partially automated, experiment-driven\n192\u2192closed-loop pipeline for designing multi-material 3D-printed\n193\u2192tensegrity-inspired energy absorbers, with surrogate updating and\n194\u2192candidate selection automated and fabrication and testing kept manual.\n195\u2192By updating a GP surrogate directly from physical measurements, the\n196\u2192workflow avoids direct dependence on calibrated finite-element\n197\u2192simulation for objective evaluation while still providing principled,\n198\u2192multi-objective design recommendations. The same closed-loop pattern is\n199\u2192expected to transfer to other multi-material additively manufactured\n200\u2192architectures whose performance is dominated by hard-to-simulate\n201\u2192effects.\n202\u2192\n203\u2192{\\tiny\n204\u2192\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205\u2192\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206\u2192% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207\u2192% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208\u2192\\providecommand{\\doi}[1]{}\n209\u2192\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210\u2192\\bibliography{references}\n211\u2192}\n212\u2192\n213\u2192\\end{document}", + "name": "read", + "tool_call_id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "info": { + "start_ts": 8147.332633466, + "end_ts": 8147.339043095 + } + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\", \"offset\": 174, \"limit\": 100}", + "name": "read" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ], + "info": null + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Tensegrity Slides \u2014 Draft 1 (extracted content)\n 2\u2192\n 3\u2192Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4\u2192\n 5\u2192\n 6\u2192## Slide 1 \u2014 layout: Title Only\n 7\u2192\n 8\u2192**On-slide text:**\n 9\u2192- **Title 1**: Let\u2019s build better tensegrity structures faster\n 10\u2192- **TextBox 4**: Marcus Madsen\n 11\u2192Undergraduate researcher under Sterling Baird, Ph.D.\n 12\u2192- **TextBox 6**: IDETC 2026\n 13\u2192\n 14\u2192**Non-text shapes / media / placeholders:**\n 15\u2192- [PICTURE (13)] Picture 3\n 16\u2192- [PICTURE (13)] Picture 8\n 17\u2192\n 18\u2192**Presenter notes:**\n 19\u2192> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20\u2192> \n 21\u2192> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22\u2192\n 23\u2192\n 24\u2192## Slide 2 \u2014 layout: Title Only\n 25\u2192\n 26\u2192**On-slide text:**\n 27\u2192- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings\u2014and to problems closer to home.\n 28\u2192\n 29\u2192**Non-text shapes / media / placeholders:**\n 30\u2192- [MEDIA (16)] Online Media 5\n 31\u2192\n 32\u2192**Presenter notes:**\n 33\u2192> Replace this video with a downloaded and trimmed version of just the fall. \n 34\u2192> \n 35\u2192> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle \u2014 thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander\u00a0is\u00a0the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit\u00a0and get back up\u00a0\u2014 it survives multiple drops and keeps working.\n 36\u2192\n 37\u2192\n 38\u2192## Slide 3 (HIDDEN) \u2014 layout: Title Only\n 39\u2192\n 40\u2192**On-slide text:**\n 41\u2192- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42\u2192- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43\u2192- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44\u2192\n 45\u2192**Presenter notes:**\n 46\u2192> This might be better explained purely vocally while on the previous slide.\n 47\u2192\n 48\u2192\n 49\u2192## Slide 4 \u2014 layout: Title Only\n 50\u2192\n 51\u2192**On-slide text:**\n 52\u2192- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53\u2192- **TextBox 3**: Iteration\n 54\u2192- **TextBox 5**: Building\n 55\u2192- **TextBox 7**: Testing\n 56\u2192\n 57\u2192**Non-text shapes / media / placeholders:**\n 58\u2192- [MEDIA (16)] AdobeStock_207717222\n 59\u2192- [MEDIA (16)] Online Media 2\n 60\u2192- [PICTURE (13)] Picture 14\n 61\u2192- [PICTURE (13)] Picture 16\n 62\u2192- [PICTURE (13)] Picture 18\n 63\u2192\n 64\u2192**Presenter notes:**\n 65\u2192> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn\u2019t become distracting because it\u2019s fun to watch it spin.\n 66\u2192> \n 67\u2192> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68\u2192\n 69\u2192\n 70\u2192## Slide 5 (HIDDEN) \u2014 layout: Title Only\n 71\u2192\n 72\u2192**On-slide text:**\n 73\u2192- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74\u2192- **TextBox 14**: Baird et al. Matter (2022)\n 75\u2192\n 76\u2192**Non-text shapes / media / placeholders:**\n 77\u2192- [GROUP (6)] Group 2\n 78\u2192\n 79\u2192**Presenter notes:**\n 80\u2192> Consult Sterling on this slide. I don\u2019t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it\u2019s well enough known that it\u2019d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81\u2192\n 82\u2192\n 83\u2192## Slide 6 (HIDDEN) \u2014 layout: Title Only\n 84\u2192\n 85\u2192**On-slide text:**\n 86\u2192- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87\u2192- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88\u2192\n 89\u2192**Presenter notes:**\n 90\u2192> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91\u2192\n 92\u2192\n 93\u2192## Slide 7 \u2014 layout: Title Only\n 94\u2192\n 95\u2192**On-slide text:**\n 96\u2192- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97\u2192- **TextBox 5**: 6 variables\n 98\u2192- **TextBox 7**: 2 types of input data\n 99\u2192- **TextBox 9**: 2 objectives\n100\u2192- **TextBox 17**: *image of blackbox*\n101\u2192\n102\u2192**Non-text shapes / media / placeholders:**\n103\u2192- [LINE (9)] Connector: Curved 11\n104\u2192- [LINE (9)] Connector: Curved 13\n105\u2192- [LINE (9)] Connector: Curved 15\n106\u2192\n107\u2192**Presenter notes:**\n108\u2192> Very rough slide as I\u2019m not entirely sure what graphic or text to put on this, partially because I\u2019m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109\u2192\n110\u2192\n111\u2192## Slide 8 \u2014 layout: Title Only\n112\u2192\n113\u2192**On-slide text:**\n114\u2192- **Title 1**: Multi-material additive manufacturing supplies that data fast \u2014 single-build co-fabrication, with no joining of parts.\n115\u2192\n116\u2192**Non-text shapes / media / placeholders:**\n117\u2192- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118\u2192- [PICTURE (13)] Picture 4\n119\u2192\n120\u2192**Presenter notes:**\n121\u2192> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122\u2192\n123\u2192\n124\u2192## Slide 9 \u2014 layout: Title Only\n125\u2192\n126\u2192**On-slide text:**\n127\u2192- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128\u2192- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129\u2192- **TextBox 5**: *placeholder for slo motion of drop test*\n130\u2192\n131\u2192\n132\u2192## Slide 10 \u2014 layout: Title Only\n133\u2192\n134\u2192**On-slide text:**\n135\u2192- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136\u2192- **TextBox 3**: Printing\n137\u2192- **TextBox 5**: Testing\n138\u2192- **TextBox 7**: BO feed\n139\u2192\n140\u2192**Non-text shapes / media / placeholders:**\n141\u2192- [PICTURE (13)] Picture 14\n142\u2192- [PICTURE (13)] Picture 16\n143\u2192- [PICTURE (13)] Picture 18\n144\u2192- [PICTURE (13)] Graphic 8\n145\u2192\n146\u2192**Presenter notes:**\n147\u2192> I think it might be better if we replaced \u201cprinting\u201d with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148\u2192\n149\u2192\n150\u2192## Slide 11 (HIDDEN) \u2014 layout: Title Only\n151\u2192\n152\u2192**On-slide text:**\n153\u2192- **Title 1**: Reserved for results\n154\u2192\n155\u2192**Presenter notes:**\n156\u2192> Would be helpful if we could show that this process works in this case.\n157\u2192\n158\u2192\n159\u2192## Slide 12 \u2014 layout: Title Only\n160\u2192\n161\u2192**On-slide text:**\n162\u2192- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163\u2192- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164\u2192- **TextBox 3**: *image to show lattice structure\n165\u2192\n166\u2192\n167\u2192## Slide 13 \u2014 layout: Title Only\n168\u2192\n169\u2192**Non-text shapes / media / placeholders:**\n170\u2192- [PICTURE (13)] Picture 3\n171\u2192\n172\u2192**Presenter notes:**\n173\u2192> Slide for questions at the end\n174\u2192\n175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "info": { + "start_ts": 8147.330900226, + "end_ts": 8147.337548875 + } + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Presentation \u2014 Doumont Presentation Structure Template\n 2\u2192\n 3\u2192Transcribed from Jean-luc Doumont's presentation-structure template\n 4\u2192([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5\u2192adapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\n 6\u2192All rights reserved. Can be downloaded from .\n 7\u2192\n 8\u2192> To design your presentation, write down your ideas for each component below.\n 9\u2192> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10\u2192\n 11\u2192**Draft 3** \u2014 applies the inline review comments from @sgbaird on PR #84 and the\n 12\u2192priority revisions from the\n 13\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14\u2192Summaries of what changed are at the bottom of this file\n 15\u2192([Draft 2 \u2192 3](#changes-from-draft-2), [Draft 1 \u2192 2](#changes-from-draft-1)).\n 16\u2192\n 17\u2192**Scope note (frame everything below with this):** the study presented is the\n 18\u2192optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19\u2192processing parameters** (e.g., nozzle temperature, print speed). PLA\u2013TPU is a\n 20\u2192**proxy system** \u2014 not flight material \u2014 used to prototype a self-driving-lab\n 21\u2192style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22\u2192The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23\u2192beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24\u2192\n 25\u2192## Opening\n 26\u2192\n 27\u2192### Attention getter\n 28\u2192\n 29\u2192*A way to lead the audience to the need efficiently*\n 30\u2192\n 31\u2192Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32\u2192landings \u2014 and to problems closer to home.\n 33\u2192\n 34\u2192One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35\u2192clip) do the work:\n 36\u2192\n 37\u2192> Landing a payload on another planet means surviving an impact in conditions\n 38\u2192> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n 39\u2192> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40\u2192> structure: the lander *is* the energy absorber. And unlike crushable\n 41\u2192> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42\u2192> hit **and get back up** \u2014 it survives multiple drops and keeps working.\n 43\u2192\n 44\u2192Candidate Super Ball Bot videos to show (verified links):\n 45\u2192\n 46\u2192- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n 47\u2192 (official channel); the canonical overview.\n 48\u2192- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49\u2192 \u2014 Wall Street Journal; prototype drop footage.\n 50\u2192- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51\u2192 \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n 52\u2192\n 53\u2192Immediately after the hook, state the present study scope in one sentence\n 54\u2192(per the scope note above): *we study a printed PLA\u2013TPU T3 prism as a proxy\n 55\u2192system for prototyping an autonomous tensegrity-optimization workflow* \u2014 so\n 56\u2192the planetary framing never outruns the demonstrated scope.\n 57\u2192\n 58\u2192\n 60\u2192\n 61\u2192### Need\n 62\u2192\n 63\u2192*A difference between actual and desired situations*\n 64\u2192\n 65\u2192**Actual situation** (a two-sided pincer \u2014 stated as a scoped engineering\n 66\u2192judgment, not an absolute):\n 67\u2192\n 68\u2192- For these structures, obtaining trustworthy objective values from a\n 69\u2192 validated high-fidelity model would require substantial calibration of\n 70\u2192 interface, rate, contact, and defect behavior \u2014 and even heavy-duty\n 71\u2192 simulation falls short of the experiments we are running: peak transmitted\n 72\u2192 shock is a millisecond-scale transient that is notoriously difficult to\n 73\u2192 resolve numerically. This holds even for idealized cable\u2013strut tensegrities\n 74\u2192 in their actual engineering materials (steel or similar), not just for\n 75\u2192 printed PLA\u2013TPU.\n 76\u2192 \n 79\u2192- Exhaustive physical search fails on cost: a traditional design of\n 80\u2192 experiments over this relatively high-dimensional space (geometry + FDM\n 81\u2192 process parameters) would demand hundreds of specimens, each costing print\n 82\u2192 and test time.\n 83\u2192- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84\u2192 more complex structures \u2014 especially tensegrity *lattices* \u2014 become\n 85\u2192 extremely difficult and ultimately infeasible to hand-build.\n 86\u2192\n 87\u2192**Desired situation:**\n 88\u2192\n 89\u2192- Trustworthy performance data without hand assembly (easy creation of\n 90\u2192 tensegrity structures, scalable beyond a single prism).\n 91\u2192- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n 92\u2192 not the hundreds a traditional design of experiments would require.\n 93\u2192\n 94\u2192### Task\n 95\u2192\n 96\u2192*What I decided/was asked to do to address the need*\n 97\u2192\n 98\u2192Build a closed-loop design\u2013print\u2013test workflow that optimizes a **T3-prism\n 99\u2192tensegrity-inspired energy absorber together with its FDM processing\n100\u2192parameters** (e.g., temperature, print speed) directly from real, measured\n101\u2192impact data \u2014 maximizing energy absorbed per gram (specific energy\n102\u2192absorption) and compaction efficiency, subject to a cap on the peak force\n103\u2192transmitted to the payload. Extensions beyond the T3 prism are future work.\n104\u2192\n105\u2192### Main message\n106\u2192\n107\u2192*The one sentence I want my audience to remember*\n108\u2192\n109\u2192By closing the loop between multi-material 3D printing and Bayesian\n110\u2192optimization, we can optimize tensegrity-inspired energy absorbers directly\n111\u2192from real impact data \u2014 in dozens of prints, not the hundreds a traditional\n112\u2192design of experiments would demand.\n113\u2192\n114\u2192\n118\u2192\n119\u2192### Preview\n120\u2192\n121\u2192*A map of the body (ideally three points, max. five)*\n122\u2192\n123\u21921. When model calibration and exhaustive testing are both costly, each\n124\u2192 physical specimen must be chosen for its information value \u2014 so the\n125\u2192 measured experiment is our source of truth.\n126\u21922. Bayesian optimization makes physical experimentation affordable: it handles\n127\u2192 noisy measurements, small batches, and the constrained multi-objective\n128\u2192 trade-off \u2014 and Honegumi let us scaffold it in minutes.\n129\u21923. Multi-material additive manufacturing closes the loop: single-build PLA\u2013TPU\n130\u2192 co-fabrication turns each proposed design into test data in hours, with no\n131\u2192 joining of parts.\n132\u21924. *(Optional fourth point, if time allows)* The same closed loop points at\n133\u2192 what comes next: tensegrity lattices, flight-relevant materials, and\n134\u2192 safety hardware on Earth.\n135\u2192\n136\u2192## Body\n137\u2192\n138\u2192### Point 1\n139\u2192\n140\u2192When model calibration and exhaustive testing are both costly, each physical\n141\u2192specimen must be chosen for its information value.\n142\u2192\n143\u2192- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144\u2192 these structures, a sufficiently calibrated multiphysics model is expensive\n145\u2192 to build relative to the design campaign itself \u2014 and the hardest quantity,\n146\u2192 peak transmitted shock, is a millisecond-scale transient that even\n147\u2192 heavy-duty simulation struggles to resolve. The gap persists in the actual\n148\u2192 engineering materials a real lander would use (steel struts and cables),\n149\u2192 so this is not merely an artifact of printed PLA\u2013TPU.\n150\u2192- The alternative \u2014 sweeping the design space physically \u2014 fails on cost: a\n151\u2192 traditional design of experiments over geometry plus FDM process parameters\n152\u2192 would take hundreds of specimens, each needing print + test time.\n153\u2192- Therefore: treat the physical experiment as the authoritative source of\n154\u2192 objective values, and be ruthless about *which* experiments to run.\n155\u2192- Anticipate the FEA-veteran pushback (\"inconvenient \u2260 untrustworthy\"): frame\n156\u2192 it as a cost-and-dependence decision, and keep a backup slide with the\n157\u2192 literature (or our own validation) on simulation\u2013experiment discrepancy for\n158\u2192 impact transients.\n159\u2192\n160\u2192### Transition\n161\u2192\n162\u2192If every data point must be a real experiment, we need a method that extracts\n163\u2192the most from every specimen \u2014 we need to make the process radically more\n164\u2192sample-efficient.\n165\u2192\n166\u2192### Point 2\n167\u2192\n168\u2192Bayesian optimization makes physical experimentation affordable, even with\n169\u2192limited, noisy data.\n170\u2192\n171\u2192- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172\u2192 visual in the main talk; implementation detail goes to backup slides.\n173\u2192 Spoken version:\n174\u2192 > \"After each batch, the model estimates both expected performance and\n175\u2192 > uncertainty across the design space. The acquisition rule selects a small\n176\u2192 > next batch that is most likely to expand the feasible trade-off frontier,\n177\u2192 > while accounting for noisy tests and print failures.\"\n178\u2192- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179\u2192 motivate a noise-aware acquisition function (qNEHVI) rather than\n180\u2192 non-noise-aware alternatives. (Backup slide: how observation noise is\n181\u2192 actually modeled \u2014 replicates, noise identification \u2014 and budget-matched\n182\u2192 baselines such as Sobol/random sampling.)\n183\u2192- Small parallel batches: the surrogate proposes the next batch of specimens\n184\u2192 to print, balancing exploration and exploitation.\n185\u2192- Constrained multi-objective by construction: **objectives** = maximize\n186\u2192 specific energy absorption and compaction efficiency; **constraint** = peak\n187\u2192 transmitted force must stay under a cap; the deliverable is the *feasible*\n188\u2192 Pareto front between the two objectives among force-cap-satisfying designs.\n189\u2192 (Keep this formulation consistent everywhere \u2014 do not present \"SEA vs. peak\n190\u2192 force\" as the trade-off.)\n191\u2192- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192\u2192 this advanced task (noisy, batched, constrained, multi-objective) in\n193\u2192 minutes using [Honegumi](https://honegumi.readthedocs.io/) \u2014 the audience\n194\u2192 can do the same for their own problems; show the code-template picker\n195\u2192 briefly so they see how low the barrier is.\n196\u2192\n197\u2192### Transition\n198\u2192\n199\u2192The optimizer is no longer the bottleneck \u2014 the limit is now providing real\n200\u2192data quickly enough.\n201\u2192\n202\u2192### Point 3\n203\u2192\n204\u2192Multi-material additive manufacturing supplies that data fast \u2014 single-build\n205\u2192co-fabrication, with no joining of parts.\n206\u2192\n207\u2192- Single-build PLA\u2013TPU co-fabrication: rigid struts and flexible tension\n208\u2192 network in one print \u2014 no joining or hand assembly of members, which closes\n209\u2192 the \"easy creation\" gap from the Need and is what makes future lattices\n210\u2192 feasible at all. (Be precise: if support removal, trimming, or conditioning\n211\u2192 remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212\u2192 assembly.\")\n213\u2192- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214\u2192 as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215\u2192 pre-tension status) plus a short deformation sequence \u2014 so the EDL engineer\n216\u2192 doesn't file it as a \"crush structure\" and the grad student can tell it from\n217\u2192 an ordinary lattice.\n218\u2192- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219\u2192 tested specimen (quasi-static compression + instrumented drop-weight impact)\n220\u2192 in hours. (Backup slide: representative print-to-result time breakdown and\n221\u2192 which steps remain manual \u2014 candidate selection is automated; slicing,\n222\u2192 handling, and testing are not yet.)\n223\u2192- Now we have the data we need to complete the loop.\n224\u2192\n225\u2192### Point 4 (optional \u2014 future applications)\n226\u2192\n227\u2192*Include if timing allows; otherwise fold into the Conclusion.*\n228\u2192\n229\u2192The same closed loop transfers wherever tests are authoritative, each test is\n230\u2192costly, the design space is mixed, and several performance goals conflict:\n231\u2192\n232\u2192- Tensegrity *lattices* and more complex architectures \u2014 exactly where hand\n233\u2192 assembly becomes infeasible and single-build co-fabrication pays off.\n234\u2192- Migration from the PLA\u2013TPU proxy toward flight-relevant materials and\n235\u2192 impact regimes.\n236\u2192- Safety hardware on Earth (e.g., the tensegrity crutch tip \u2014 future work).\n237\u2192- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238\u2192 handling, testing) in the loop.\n239\u2192\n240\u2192### Evidence / results\n241\u2192\n242\u2192*What we found \u2014 ground the conclusion in shown data*\n243\u2192\n244\u2192Build the talk around a quantitative three-slide sequence (per the\n245\u2192mock-audience report; insert observed values once the campaign completes):\n246\u2192\n247\u21921. **Campaign ledger** \u2014 \"The campaign used N specimens across B adaptive\n248\u2192 batches under a fixed physical-test budget\": initialization vs. adaptive\n249\u2192 batches, replicates, print failures, cycle time, and the numerical value\n250\u2192 (and rationale) of the force cap.\n251\u21922. **Budget-matched baseline** \u2014 \"Adaptive selection improved the feasible\n252\u2192 design set faster than [predeclared baseline]\": performance vs. number of\n253\u2192 physical tests against Sobol/random sampling at the same budget, with\n254\u2192 uncertainty where available.\n255\u21923. **Measured feasible Pareto front** \u2014 specific energy absorption vs.\n256\u2192 compaction efficiency among designs satisfying the force cap, with\n257\u2192 replicate spread, marked infeasible designs, and specimen photos or\n258\u2192 force\u2013time traces for representative Pareto designs.\n259\u2192\n260\u2192Supporting raw-data candidates: drop-test force\u2013time traces, Sobol /\n261\u2192first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262\u2192\n263\u2192### Suggested 15-minute allocation\n264\u2192\n265\u2192*(From the mock-audience report \u2014 results get the largest block.)*\n266\u2192\n267\u2192| Time | Content |\n268\u2192|---|---|\n269\u2192| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270\u2192| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271\u2192| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272\u2192| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273\u2192| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274\u2192| 1 min | Limitations and transfer conditions |\n275\u2192| 1 min | Measured conclusion + bookend |\n276\u2192\n277\u2192Move kernel choices, encodings, and other implementation detail to backup\n278\u2192slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279\u2192[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280\u2192\n281\u2192## Closing\n282\u2192\n283\u2192### Review\n284\u2192\n285\u2192*A recap of the body, leading into the conclusion*\n286\u2192\n287\u2192Using real, measured impact data from additively manufactured\n288\u2192tensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\n289\u2192optimization routine \u2014 we rapidly iterate toward better energy absorbers.\n290\u2192This feedback loop makes tensegrity optimization dramatically faster: dozens\n291\u2192of prints instead of the hundreds a traditional design of experiments over\n292\u2192this design space would require.\n293\u2192\n294\u2192### Conclusion\n295\u2192\n296\u2192*What the above means to the audience in the end*\n297\u2192\n298\u2192Because the loop runs on physical measurements, it is useful precisely where\n299\u2192model calibration is not worth its cost \u2014 and the same closed-loop pattern\n300\u2192should transfer (a conditional claim, pending evidence) to other additively\n301\u2192manufactured architectures whose performance is dominated by\n302\u2192hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303\u2192*use this loop when tests are authoritative, each test is costly, the design\n304\u2192space is mixed, and several performance goals conflict.* For tensegrity\n305\u2192specifically, it opens a path from this T3 proxy study toward lattices,\n306\u2192flight-relevant materials, payload protection on other planets, and safety\n307\u2192hardware here on Earth (e.g., the crutch tip, as future work).\n308\u2192\n309\u2192### Close\n310\u2192\n311\u2192*A way to end the presentation clearly and elegantly*\n312\u2192\n313\u2192Return to the opening image: the next planetary lander's energy absorber \u2014 a\n314\u2192structure that has to survive not one impact but many \u2014 doesn't have to take\n315\u2192years of hand-built prototypes; with this loop, candidate designs can be\n316\u2192designed, printed, and tested against real impacts in weeks instead of months\n317\u2192or longer. End on the measured engineering result and its transfer condition,\n318\u2192then the bookend: every method that compresses design time moves our ventures\n319\u2192\u2014 on this planet and beyond \u2014 forward.\n320\u2192\n321\u2192---\n322\u2192\n323\u2192## Changes from Draft 2\n324\u2192\n325\u2192Applied from @sgbaird's inline review comments on PR #84 and the\n326\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327\u2192\n328\u2192**From the inline review comments:**\n329\u2192\n330\u21921. **Reusability in the hook** (line 22): the tensegrity lander survives\n331\u2192 multiple drops, unlike crushable/sacrificial alternatives.\n332\u21922. **Proxy-system framing** (lines 49, 87): PLA\u2013TPU would never fly; it is a\n333\u2192 proxy for prototyping a self-driving-lab-style workflow \u2014 stated in a\n334\u2192 scope note up front and re-stated right after the hook.\n335\u21923. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336\u2192 heavy-duty simulation falls short of the experiments (millisecond-scale\n337\u2192 peak shock), not that it merely costs too much.\n338\u21924. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339\u2192 complex structures and especially lattices are not.\n340\u21925. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341\u2192 (temperature, print speed, \u2026) only; the rest is explicitly future work.\n342\u21926. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343\u2192 literature search (or validation of experiments against simulation), plus\n344\u2192 the millisecond-resolution peak-shock argument.\n345\u21927. **Optional Point 4 added** (line 97): future applications, with a note it\n346\u2192 can fold into the Conclusion if time is short.\n347\u21928. **Steel-materials point** (line 104): simulations fall short for the\n348\u2192 actual engineering materials (steel struts/cables), not just printed\n349\u2192 PLA\u2013TPU.\n350\u21929. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351\u2192 in minutes, pitched so the audience sees they can use it too.\n352\u219210. **\"Thousands\" \u2192 \"hundreds\"** (line 173): baseline is now a traditional\n353\u2192 design of experiments over a high-dimensional space (hundreds), in the\n354\u2192 main message, Need, Point 1, and Review.\n355\u219211. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356\u2192 or longer\" for candidate designs.\n357\u2192\n358\u2192**From the mock-audience report's priority revisions:**\n359\u2192\n360\u219212. **Quantitative three-slide results sequence** (Priority 1): campaign\n361\u2192 ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front, with a\n362\u2192 fallback main-message wording if the campaign can't yet support the\n363\u2192 efficiency claim.\n364\u219213. **Anti-simulation premise replaced with a scoped cost decision**\n365\u2192 (Priority 2): Point 1 retitled to the information-value message;\n366\u2192 \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367\u2192 anticipated with a backup slide.\n368\u219214. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369\u2192 implementation detail moved to backup slides; Q&A prep pointed at the\n370\u2192 report's claims-vs-evidence table.\n371\u219215. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372\u2192 the main talk, machinery in backup slides; the misleading\n373\u2192 \"qNEHVI over standard expected improvement\" comparison replaced with\n374\u2192 \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375\u219216. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376\u2192 sequence in Point 3, so the structure isn't remembered as a generic crush\n377\u2192 structure or lattice.\n378\u219217. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379\u2192 co-fabrication / no joining of parts,\" with a note to list remaining\n380\u2192 manual steps and an automation-status caveat.\n381\u219218. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382\u2192 compaction efficiency, constraint = force cap, deliverable = *feasible*\n383\u2192 Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384\u219219. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385\u2192 transfer claim marked conditional, and the close ends on the measured\n386\u2192 result before the bookend.\n387\u2192\n388\u2192## Changes from Draft 1\n389\u2192\n390\u2192Feedback from the PR #84 review, applied in Draft 2:\n391\u2192\n392\u21921. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n393\u2192 dozens-not-thousands claim); body and message now carry the same story.\n394\u21922. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n395\u2192 manuscript's positioning (#75/#76) is that the physical experiment is the\n396\u2192 source of truth, explicitly avoiding calibrated FE simulation.\n397\u21923. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n398\u2192 parametric design space and avoid an over-claim to a design-automation\n399\u2192 audience.\n400\u21924. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401\u2192 energy absorption and compaction efficiency, cap peak transmitted force.\n402\u21925. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403\u2192 *and* the space is too big to brute-force \u2014 which makes BO + AM the\n404\u2192 inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405\u2192 above.)*\n406\u21926. **Point 1 given its own content** (the *why* behind the inefficiency)\n407\u2192 instead of restating the Need.\n408\u21927. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409\u2192 the specifics (noise-aware acquisition, small batches, multi-objective).\n410\u21928. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411\u2192 (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412\u2192 above.)*\n413\u21929. **Evidence/results slot added** before the Closing, so the Conclusion is\n414\u2192 grounded in shown data rather than three hedged \"may\"s.\n415\u219210. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n416\u2192 Ball Bot visual; verified video links included.\n417\u219211. **Close bookends the opening** (the next lander's absorber, designed in\n418\u2192 weeks) before the broader humankind line.\n419\u219212. **Spelling**: Doumont (was \"Duomont\").", + "name": "read", + "tool_call_id": "call_93haurOMDGiH2mQFFbaMaYKn", + "info": { + "start_ts": 8147.331065886, + "end_ts": 8147.336467845 + } + }, + { + "role": "tool", + "content": " 1\u2192# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n 2\u2192\n 3\u2192Video: (assigned in #83)\n 4\u2192\n 5\u2192Transcribed from @me-madsen's handwritten notes\n 6\u2192([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7\u2192dated 2026-07-15).\n 8\u2192\n 9\u2192## Overview\n10\u2192\n11\u2192A presentation is all about the right content, displayed the right way, at the\n12\u2192right time.\n13\u2192\n14\u2192- Verbal communication is sequential.\n15\u2192- Message is *interpretation* of the information.\n16\u2192 - Ensure the audience can get the most out of what you're doing.\n17\u2192 - Don't just give them information \u2014 help them understand it, interpret it,\n18\u2192 and internalize it.\n19\u2192\n20\u2192## Doumont's suggested rules for a presentation\n21\u2192\n22\u21920. Ensure you have **messages** for your audience, not just words.\n23\u21921. **Adapt to your audience.**\n24\u2192 - Think like the audience, not the speaker.\n25\u21922. **Maximize signal-to-noise ratio.**\n26\u2192 - Reduce noise (animations, laser pointers, anything that pulls attention\n27\u2192 away from your message).\n28\u21923. **Use effective redundancy.**\n29\u2192 - Tell your message in word *and* on slide: someone deaf should understand\n30\u2192 your messages just as well as someone blind.\n31\u2192 - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32\u2192 date).\n33\u2192\n34\u2192## When creating slides\n35\u2192\n36\u2192- If the audience doesn't need it, why put it on the slides?\n37\u2192- People shouldn't have to count the items on a slide to know how many are\n38\u2192 there.\n39\u2192- Don't use your slides as your \"presentation notes.\"\n40\u2192 - Don't show your notes to the audience.\n41\u2192 - **A bad slide is worse than no slide at all.**\n42\u2192- **One message per slide** is a good rule of thumb.\n43\u2192 - Prioritize the *so what* (or *therefore what*) of your message.\n44\u2192- Make it easy to interpret the information on the slide:\n45\u2192 - 3\u20135 items at most.\n46\u2192 - Align the title left.\n47\u2192 - Do not clutter slides with repetitive or unnecessary information: logos,\n48\u2192 affiliation, date, slide numbers.\n49\u2192- Doumont's suggested workflow:\n50\u2192 1. **Start with the title.**\n51\u2192 - Use a full sentence in the title.\n52\u2192 - The title is the main message of the slide.\n53\u2192 - Make it easy to read: left-aligned, with intentional line breaks.\n54\u2192 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55\u2192- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56\u2192\n57\u2192## General preparation\n58\u2192\n59\u2192- Planning for audience, content, and structure matters more than materials\n60\u2192 (i.e., slides).\n61\u2192- Try using one sheet of paper for presentation preparation.\n62\u2192- It's most important that your presentation is:\n63\u2192 - well planned, structured, and delivered;\n64\u2192 - **if you don't have a lot of time to prepare, don't focus on making\n65\u2192 slides.**\n66\u2192- Practice the presentation a few times. Practice at least once *without* your\n67\u2192 slideshow!\n68\u2192- If doing a handout, keep it separate from the slides you're preparing.\n69\u2192- Impress the audience with your body.\n70\u2192 - Use gestures, not laser pointers.\n71\u2192 - \"Control\" the audience with your body \u2014 keep their attention on your\n72\u2192 presentation.\n73\u2192\n74\u2192## Impressions / what to apply to the IDETC presentation\n75\u2192\n76\u2192\n77\u2192\n78\u2192## Questions / things to discuss\n79\u2192\n80\u2192", + "name": "read", + "tool_call_id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "info": { + "start_ts": 8147.331133416, + "end_ts": 8147.338304015 + } + }, + { + "role": "tool", + "content": " 1\u2192\\documentclass[10pt]{article}\n 2\u2192\n 3\u2192% --- Packages ---\n 4\u2192\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5\u2192\\usepackage[hidelinks]{hyperref}\n 6\u2192\\usepackage{parskip}\n 7\u2192\\usepackage{graphicx}\n 8\u2192\\usepackage{caption}\n 9\u2192\\usepackage{titlesec}\n 10\u2192\\usepackage{wrapfig}\n 11\u2192\\usepackage[numbers,sort&compress]{natbib}\n 12\u2192\n 13\u2192% --- Formatting ---\n 14\u2192\\setlength{\\parskip}{0.2em}\n 15\u2192\\setlength{\\parindent}{0em}\n 16\u2192\\titleformat*{\\section}{\\large\\bfseries}\n 17\u2192\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18\u2192\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19\u2192\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20\u2192\\setlength{\\emergencystretch}{2em}\n 21\u2192\\setlength{\\intextsep}{0.2em}\n 22\u2192\\bibliographystyle{abbrvnat}\n 23\u2192\n 24\u2192\\begin{document}\n 25\u2192\n 26\u2192\\begin{center}\n 27\u2192{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28\u21923D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29\u2192\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30\u2192\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31\u2192\\end{center}\n 32\u2192\n 33\u2192\\vspace{0.4em}\n 34\u2192\n 35\u2192\\section*{Abstract}\n 36\u2192Tensegrity-inspired architectures---rigid struts suspended within a\n 37\u2192continuous flexible network---can exhibit tunable nonlinear\n 38\u2192force--displacement responses and favorable energy absorption per unit\n 39\u2192mass, motivating their use in lightweight energy-absorbing and protective\n 40\u2192structures\n 41\u2192\\citep{skelton2009tensegrity,pajunen2019design}.\n 42\u2192Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43\u2192struts and flexible (TPU) elements in a single build\n 44\u2192\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45\u2192space---strut geometry, tension-element cross-section, connectivity\n 46\u2192topology, and unit-cell tiling---is too large to explore by trial and\n 47\u2192error. We present a closed-loop experimental campaign that uses\n 48\u2192Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49\u2192to drive a design--print--test workflow operating directly on physical\n 50\u2192measurements, without relying on calibrated finite-element simulation\n 51\u2192for objective evaluation.\n 52\u2192\n 53\u2192\\section{Introduction and Motivation}\n 54\u2192\n 55\u2192\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56\u2192\\centering\n 57\u2192\\vspace{-0.4em}\n 58\u2192\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59\u2192\\captionsetup{font=small}\n 60\u2192\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61\u2192Candidate tensegrity-inspired unit cells are instantiated from a\n 62\u2192parameter vector, fabricated by multi-material FDM, and tested under\n 63\u2192quasi-static compression and drop-weight impact. Measured peak force,\n 64\u2192specific energy absorption (SEA), and compaction efficiency update a\n 65\u2192Gaussian-process surrogate that proposes the next batch of designs.}\n 66\u2192\\label{fig:overview}\n 67\u2192\\vspace{-0.4em}\n 68\u2192\\end{wrapfigure}\n 69\u2192\n 70\u2192Tensegrity structures combine compression-only members with a\n 71\u2192pre-tensioned tension network to achieve stiffness without rigid\n 72\u2192joinery~\\citep{skelton2009tensegrity}. Their\n 73\u2192nonlinear, often load-limiting force--displacement response makes them\n 74\u2192attractive for impact mitigation, packaging, and wearable protective\n 75\u2192gear~\\citep{pajunen2019design}. Idealized\n 76\u2192cable--strut tensegrities are difficult to manufacture at scale, but\n 77\u2192\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78\u21923D printing reproduce key tensegrity behaviors---including\n 79\u2192post-buckling stability and load-limiting force--displacement\n 80\u2192response~\\citep{pajunen2019design}---motivating our extension to\n 81\u2192multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82\u2192elements. PLA provides rigid struts; TPU provides flexible,\n 83\u2192rate-dependent tension elements whose viscoelasticity is expected to\n 84\u2192contribute hysteretic energy dissipation under impact loading,\n 85\u2192consistent with the energy-absorbing behavior reported for rigid--TPU\n 86\u2192multi-material prints~\\citep{khatri2024energy}.\n 87\u2192\n 88\u2192The challenge is that small geometric and topological changes can\n 89\u2192produce large changes in the measured response, and high-fidelity\n 90\u2192simulation of FDM specimens is computationally expensive and often\n 91\u2192struggles to capture physical defects such as interfacial slip and\n 92\u2192TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93\u2192trial-and-error sweeps over the joint design space are also infeasible:\n 94\u2192even modest parameter resolutions yield thousands of candidates, each\n 95\u2192requiring print and test time. We instead treat the physical\n 96\u2192experiment as the source of truth and use BO to select which specimens\n 97\u2192are most informative to fabricate next---an approach used in related\n 98\u2192closed-loop materials and structures\n 99\u2192campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100\u2192Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101\u2192set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102\u2192designs using parallel, noisy multi-objective BO driven by measured\n103\u2192$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104\u2192\n105\u2192\\section{Approach}\n106\u2192\\subsection{Closed-Loop Workflow}\n107\u2192Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108\u2192proceeds in four steps: (i)~a parameterized unit-cell design is\n109\u2192instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110\u2192is sliced and printed on a multi-material FDM system; (iii)~it is\n111\u2192tested under quasi-static compression and instrumented drop-weight\n112\u2192impact; and (iv)~the resulting performance metrics update a\n113\u2192Gaussian-process (GP) surrogate, which a batch acquisition function\n114\u2192queries to recommend the next set of candidates. Surrogate updating\n115\u2192and experiment selection are automated; specimen handling, slicing,\n116\u2192and test setup remain manual.\n117\u2192\n118\u2192\\subsection{Design Space}\n119\u2192We parameterize a family of unit cells using a core-wrapping\n120\u2192architecture in which rigid PLA struts are enclosed by continuous TPU\n121\u2192skins; the wrapping rationale follows multi-material FDM strategies in\n122\u2192which rigid panels are wrapped by stretchable soft material to prevent\n123\u2192delamination and improve interfacial bonding at the rigid--flexible\n124\u2192interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125\u2192comprises continuous geometric variables (strut diameter and length,\n126\u2192tension-element width and thickness), an integer strut count per unit\n127\u2192cell, and two categorical variables (connectivity topology, unit-cell\n128\u2192tiling pattern). Categorical choices are encoded explicitly rather\n129\u2192than embedded in a continuous metric, and bounds on the continuous\n130\u2192variables are chosen to remain within the printer's resolution and\n131\u2192overhang limits and to keep specimens in a common form factor for the\n132\u2192test fixtures.\n133\u2192\n134\u2192\\subsection{Surrogate and Acquisition}\n135\u2192The GP surrogate is trained on three measured performance metrics:\n136\u2192peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137\u2192(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138\u2192objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139\u2192likelihood with priors to avoid pathological length scales at small $n$.\n140\u2192Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141\u2192interfacial defects motivates our use of q-noisy expected hypervolume\n142\u2192improvement (qNEHVI), which explicitly accounts for observation noise\n143\u2192in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144\u2192expected-improvement variants. Print failures are treated as a\n145\u2192probabilistic feasibility constraint: the acquisition score for each\n146\u2192candidate is its qNEHVI improvement weighted by the GP-estimated\n147\u2192probability of successful fabrication, following recent constrained\n148\u2192multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149\u2192Where applicable, we use numerically stable log-space variants of\n150\u2192improvement-based acquisitions to mitigate vanishing-gradient\n151\u2192pathologies as data and constraints\n152\u2192accumulate~\\citep{ament2023logei}.\n153\u2192\n154\u2192\\section{Methods}\n155\u2192\\subsection{Fabrication}\n156\u2192Specimens are fabricated on a multi-material FDM system capable of\n157\u2192co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158\u2192Print parameters (temperature, line width, infill, retraction) are\n159\u2192held fixed within a batch to isolate the effect of the design\n160\u2192variables. Each specimen is weighed and dimensionally inspected prior\n161\u2192to testing so that mass and geometric deviations are available as\n162\u2192covariates.\n163\u2192\n164\u2192\\subsection{Mechanical Testing}\n165\u2192Quasi-static compression is performed on a screw-driven load frame at\n166\u2192a fixed nominal strain rate, with force--displacement recorded to\n167\u2192densification. Drop-weight impact testing uses an instrumented tup at\n168\u2192a fixed impact energy; transmitted force is captured at high sample\n169\u2192rate, and specimens are imaged before and after impact to document\n170\u2192failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171\u2192unit mass, and compaction efficiency $\\eta$, defined consistently\n172\u2192across the campaign. A control specimen from an earlier batch is\n173\u2192retested periodically to monitor TPU batch-to-batch and printer drift.\n174\u2192\n175\u2192\\section{Expected Outcomes and Discussion}\n176\u2192We will report how BO-selected designs evolve across iterations, how\n177\u2192GP surrogate predictions and uncertainties change as data accumulate,\n178\u2192and which Pareto-efficient trade-offs emerge between SEA and peak\n179\u2192transmitted force within the explored design space. We will also\n180\u2192discuss practical lessons for operating the closed-loop workflow,\n181\u2192including handling of print failures, batch-to-batch variability in\n182\u2192TPU response, the influence of the rigid--flexible interface on\n183\u2192energy absorption, and the exploration--exploitation balance of the\n184\u2192acquisition function under realistic experimental noise. We expect\n185\u2192the campaign to clarify where physical experimentation is genuinely\n186\u2192required versus where simpler analytical or multi-fidelity surrogate\n187\u2192shortcuts~\\citep{mo2023accelerated} could be\n188\u2192substituted in future work.\n189\u2192\n190\u2192\\section{Conclusion}\n191\u2192This work demonstrates a partially automated, experiment-driven\n192\u2192closed-loop pipeline for designing multi-material 3D-printed\n193\u2192tensegrity-inspired energy absorbers, with surrogate updating and\n194\u2192candidate selection automated and fabrication and testing kept manual.\n195\u2192By updating a GP surrogate directly from physical measurements, the\n196\u2192workflow avoids direct dependence on calibrated finite-element\n197\u2192simulation for objective evaluation while still providing principled,\n198\u2192multi-objective design recommendations. The same closed-loop pattern is\n199\u2192expected to transfer to other multi-material additively manufactured\n200\u2192architectures whose performance is dominated by hard-to-simulate\n201\u2192effects.\n202\u2192\n203\u2192{\\tiny\n204\u2192\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205\u2192\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206\u2192% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207\u2192% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208\u2192\\providecommand{\\doi}[1]{}\n209\u2192\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210\u2192\\bibliography{references}\n211\u2192}\n212\u2192\n213\u2192\\end{document}", + "name": "read", + "tool_call_id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "info": { + "start_ts": 8147.332633466, + "end_ts": 8147.339043095 + } + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\", \"offset\": 174, \"limit\": 100}", + "name": "read" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "tool", + "content": "175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "info": { + "start_ts": 8153.825818486, + "end_ts": 8153.828437556 + } + } + ], + "next_observation": [ + { + "role": "tool", + "content": "175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "info": { + "start_ts": 8153.825818486, + "end_ts": 8153.828437556 + } + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "callback_type": "before_transition" + }, + "action": { + "call_id": { + "run_id": "f8b11daf-a506-414f-887c-a22c881e34cd", + "fwd_id": "38d5e2c5-ee05-46de-9864-cfd697621798" + }, + "op_name": "IdentityOp-10b149563727", + "op_class_name": "ldp.graph.common_ops.IdentityOp", + "value": { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\", \"offset\": 174, \"limit\": 100}", + "name": "read" + } + } + ] + }, + "logprob": null + } + } + }, + "created_at": "2026-07-26T02:29:29.352694Z" + }, + { + "id": "20509215-9a10-4123-b877-1e574d1b25df", + "trajectory_id": "afade0b1-f49e-4031-a86d-cb5ec4652536", + "trajectory_timestep": 1, + "step": "after_transition", + "state": { + "transition": { + "timestep": 1, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ], + "info": null + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Tensegrity Slides \u2014 Draft 1 (extracted content)\n 2\u2192\n 3\u2192Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4\u2192\n 5\u2192\n 6\u2192## Slide 1 \u2014 layout: Title Only\n 7\u2192\n 8\u2192**On-slide text:**\n 9\u2192- **Title 1**: Let\u2019s build better tensegrity structures faster\n 10\u2192- **TextBox 4**: Marcus Madsen\n 11\u2192Undergraduate researcher under Sterling Baird, Ph.D.\n 12\u2192- **TextBox 6**: IDETC 2026\n 13\u2192\n 14\u2192**Non-text shapes / media / placeholders:**\n 15\u2192- [PICTURE (13)] Picture 3\n 16\u2192- [PICTURE (13)] Picture 8\n 17\u2192\n 18\u2192**Presenter notes:**\n 19\u2192> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20\u2192> \n 21\u2192> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22\u2192\n 23\u2192\n 24\u2192## Slide 2 \u2014 layout: Title Only\n 25\u2192\n 26\u2192**On-slide text:**\n 27\u2192- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings\u2014and to problems closer to home.\n 28\u2192\n 29\u2192**Non-text shapes / media / placeholders:**\n 30\u2192- [MEDIA (16)] Online Media 5\n 31\u2192\n 32\u2192**Presenter notes:**\n 33\u2192> Replace this video with a downloaded and trimmed version of just the fall. \n 34\u2192> \n 35\u2192> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle \u2014 thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander\u00a0is\u00a0the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit\u00a0and get back up\u00a0\u2014 it survives multiple drops and keeps working.\n 36\u2192\n 37\u2192\n 38\u2192## Slide 3 (HIDDEN) \u2014 layout: Title Only\n 39\u2192\n 40\u2192**On-slide text:**\n 41\u2192- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42\u2192- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43\u2192- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44\u2192\n 45\u2192**Presenter notes:**\n 46\u2192> This might be better explained purely vocally while on the previous slide.\n 47\u2192\n 48\u2192\n 49\u2192## Slide 4 \u2014 layout: Title Only\n 50\u2192\n 51\u2192**On-slide text:**\n 52\u2192- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53\u2192- **TextBox 3**: Iteration\n 54\u2192- **TextBox 5**: Building\n 55\u2192- **TextBox 7**: Testing\n 56\u2192\n 57\u2192**Non-text shapes / media / placeholders:**\n 58\u2192- [MEDIA (16)] AdobeStock_207717222\n 59\u2192- [MEDIA (16)] Online Media 2\n 60\u2192- [PICTURE (13)] Picture 14\n 61\u2192- [PICTURE (13)] Picture 16\n 62\u2192- [PICTURE (13)] Picture 18\n 63\u2192\n 64\u2192**Presenter notes:**\n 65\u2192> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn\u2019t become distracting because it\u2019s fun to watch it spin.\n 66\u2192> \n 67\u2192> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68\u2192\n 69\u2192\n 70\u2192## Slide 5 (HIDDEN) \u2014 layout: Title Only\n 71\u2192\n 72\u2192**On-slide text:**\n 73\u2192- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74\u2192- **TextBox 14**: Baird et al. Matter (2022)\n 75\u2192\n 76\u2192**Non-text shapes / media / placeholders:**\n 77\u2192- [GROUP (6)] Group 2\n 78\u2192\n 79\u2192**Presenter notes:**\n 80\u2192> Consult Sterling on this slide. I don\u2019t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it\u2019s well enough known that it\u2019d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81\u2192\n 82\u2192\n 83\u2192## Slide 6 (HIDDEN) \u2014 layout: Title Only\n 84\u2192\n 85\u2192**On-slide text:**\n 86\u2192- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87\u2192- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88\u2192\n 89\u2192**Presenter notes:**\n 90\u2192> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91\u2192\n 92\u2192\n 93\u2192## Slide 7 \u2014 layout: Title Only\n 94\u2192\n 95\u2192**On-slide text:**\n 96\u2192- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97\u2192- **TextBox 5**: 6 variables\n 98\u2192- **TextBox 7**: 2 types of input data\n 99\u2192- **TextBox 9**: 2 objectives\n100\u2192- **TextBox 17**: *image of blackbox*\n101\u2192\n102\u2192**Non-text shapes / media / placeholders:**\n103\u2192- [LINE (9)] Connector: Curved 11\n104\u2192- [LINE (9)] Connector: Curved 13\n105\u2192- [LINE (9)] Connector: Curved 15\n106\u2192\n107\u2192**Presenter notes:**\n108\u2192> Very rough slide as I\u2019m not entirely sure what graphic or text to put on this, partially because I\u2019m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109\u2192\n110\u2192\n111\u2192## Slide 8 \u2014 layout: Title Only\n112\u2192\n113\u2192**On-slide text:**\n114\u2192- **Title 1**: Multi-material additive manufacturing supplies that data fast \u2014 single-build co-fabrication, with no joining of parts.\n115\u2192\n116\u2192**Non-text shapes / media / placeholders:**\n117\u2192- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118\u2192- [PICTURE (13)] Picture 4\n119\u2192\n120\u2192**Presenter notes:**\n121\u2192> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122\u2192\n123\u2192\n124\u2192## Slide 9 \u2014 layout: Title Only\n125\u2192\n126\u2192**On-slide text:**\n127\u2192- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128\u2192- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129\u2192- **TextBox 5**: *placeholder for slo motion of drop test*\n130\u2192\n131\u2192\n132\u2192## Slide 10 \u2014 layout: Title Only\n133\u2192\n134\u2192**On-slide text:**\n135\u2192- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136\u2192- **TextBox 3**: Printing\n137\u2192- **TextBox 5**: Testing\n138\u2192- **TextBox 7**: BO feed\n139\u2192\n140\u2192**Non-text shapes / media / placeholders:**\n141\u2192- [PICTURE (13)] Picture 14\n142\u2192- [PICTURE (13)] Picture 16\n143\u2192- [PICTURE (13)] Picture 18\n144\u2192- [PICTURE (13)] Graphic 8\n145\u2192\n146\u2192**Presenter notes:**\n147\u2192> I think it might be better if we replaced \u201cprinting\u201d with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148\u2192\n149\u2192\n150\u2192## Slide 11 (HIDDEN) \u2014 layout: Title Only\n151\u2192\n152\u2192**On-slide text:**\n153\u2192- **Title 1**: Reserved for results\n154\u2192\n155\u2192**Presenter notes:**\n156\u2192> Would be helpful if we could show that this process works in this case.\n157\u2192\n158\u2192\n159\u2192## Slide 12 \u2014 layout: Title Only\n160\u2192\n161\u2192**On-slide text:**\n162\u2192- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163\u2192- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164\u2192- **TextBox 3**: *image to show lattice structure\n165\u2192\n166\u2192\n167\u2192## Slide 13 \u2014 layout: Title Only\n168\u2192\n169\u2192**Non-text shapes / media / placeholders:**\n170\u2192- [PICTURE (13)] Picture 3\n171\u2192\n172\u2192**Presenter notes:**\n173\u2192> Slide for questions at the end\n174\u2192\n175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "info": { + "start_ts": 8147.330900226, + "end_ts": 8147.337548875 + } + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Presentation \u2014 Doumont Presentation Structure Template\n 2\u2192\n 3\u2192Transcribed from Jean-luc Doumont's presentation-structure template\n 4\u2192([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5\u2192adapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\n 6\u2192All rights reserved. Can be downloaded from .\n 7\u2192\n 8\u2192> To design your presentation, write down your ideas for each component below.\n 9\u2192> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10\u2192\n 11\u2192**Draft 3** \u2014 applies the inline review comments from @sgbaird on PR #84 and the\n 12\u2192priority revisions from the\n 13\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14\u2192Summaries of what changed are at the bottom of this file\n 15\u2192([Draft 2 \u2192 3](#changes-from-draft-2), [Draft 1 \u2192 2](#changes-from-draft-1)).\n 16\u2192\n 17\u2192**Scope note (frame everything below with this):** the study presented is the\n 18\u2192optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19\u2192processing parameters** (e.g., nozzle temperature, print speed). PLA\u2013TPU is a\n 20\u2192**proxy system** \u2014 not flight material \u2014 used to prototype a self-driving-lab\n 21\u2192style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22\u2192The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23\u2192beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24\u2192\n 25\u2192## Opening\n 26\u2192\n 27\u2192### Attention getter\n 28\u2192\n 29\u2192*A way to lead the audience to the need efficiently*\n 30\u2192\n 31\u2192Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32\u2192landings \u2014 and to problems closer to home.\n 33\u2192\n 34\u2192One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35\u2192clip) do the work:\n 36\u2192\n 37\u2192> Landing a payload on another planet means surviving an impact in conditions\n 38\u2192> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n 39\u2192> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40\u2192> structure: the lander *is* the energy absorber. And unlike crushable\n 41\u2192> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42\u2192> hit **and get back up** \u2014 it survives multiple drops and keeps working.\n 43\u2192\n 44\u2192Candidate Super Ball Bot videos to show (verified links):\n 45\u2192\n 46\u2192- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n 47\u2192 (official channel); the canonical overview.\n 48\u2192- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49\u2192 \u2014 Wall Street Journal; prototype drop footage.\n 50\u2192- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51\u2192 \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n 52\u2192\n 53\u2192Immediately after the hook, state the present study scope in one sentence\n 54\u2192(per the scope note above): *we study a printed PLA\u2013TPU T3 prism as a proxy\n 55\u2192system for prototyping an autonomous tensegrity-optimization workflow* \u2014 so\n 56\u2192the planetary framing never outruns the demonstrated scope.\n 57\u2192\n 58\u2192\n 60\u2192\n 61\u2192### Need\n 62\u2192\n 63\u2192*A difference between actual and desired situations*\n 64\u2192\n 65\u2192**Actual situation** (a two-sided pincer \u2014 stated as a scoped engineering\n 66\u2192judgment, not an absolute):\n 67\u2192\n 68\u2192- For these structures, obtaining trustworthy objective values from a\n 69\u2192 validated high-fidelity model would require substantial calibration of\n 70\u2192 interface, rate, contact, and defect behavior \u2014 and even heavy-duty\n 71\u2192 simulation falls short of the experiments we are running: peak transmitted\n 72\u2192 shock is a millisecond-scale transient that is notoriously difficult to\n 73\u2192 resolve numerically. This holds even for idealized cable\u2013strut tensegrities\n 74\u2192 in their actual engineering materials (steel or similar), not just for\n 75\u2192 printed PLA\u2013TPU.\n 76\u2192 \n 79\u2192- Exhaustive physical search fails on cost: a traditional design of\n 80\u2192 experiments over this relatively high-dimensional space (geometry + FDM\n 81\u2192 process parameters) would demand hundreds of specimens, each costing print\n 82\u2192 and test time.\n 83\u2192- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84\u2192 more complex structures \u2014 especially tensegrity *lattices* \u2014 become\n 85\u2192 extremely difficult and ultimately infeasible to hand-build.\n 86\u2192\n 87\u2192**Desired situation:**\n 88\u2192\n 89\u2192- Trustworthy performance data without hand assembly (easy creation of\n 90\u2192 tensegrity structures, scalable beyond a single prism).\n 91\u2192- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n 92\u2192 not the hundreds a traditional design of experiments would require.\n 93\u2192\n 94\u2192### Task\n 95\u2192\n 96\u2192*What I decided/was asked to do to address the need*\n 97\u2192\n 98\u2192Build a closed-loop design\u2013print\u2013test workflow that optimizes a **T3-prism\n 99\u2192tensegrity-inspired energy absorber together with its FDM processing\n100\u2192parameters** (e.g., temperature, print speed) directly from real, measured\n101\u2192impact data \u2014 maximizing energy absorbed per gram (specific energy\n102\u2192absorption) and compaction efficiency, subject to a cap on the peak force\n103\u2192transmitted to the payload. Extensions beyond the T3 prism are future work.\n104\u2192\n105\u2192### Main message\n106\u2192\n107\u2192*The one sentence I want my audience to remember*\n108\u2192\n109\u2192By closing the loop between multi-material 3D printing and Bayesian\n110\u2192optimization, we can optimize tensegrity-inspired energy absorbers directly\n111\u2192from real impact data \u2014 in dozens of prints, not the hundreds a traditional\n112\u2192design of experiments would demand.\n113\u2192\n114\u2192\n118\u2192\n119\u2192### Preview\n120\u2192\n121\u2192*A map of the body (ideally three points, max. five)*\n122\u2192\n123\u21921. When model calibration and exhaustive testing are both costly, each\n124\u2192 physical specimen must be chosen for its information value \u2014 so the\n125\u2192 measured experiment is our source of truth.\n126\u21922. Bayesian optimization makes physical experimentation affordable: it handles\n127\u2192 noisy measurements, small batches, and the constrained multi-objective\n128\u2192 trade-off \u2014 and Honegumi let us scaffold it in minutes.\n129\u21923. Multi-material additive manufacturing closes the loop: single-build PLA\u2013TPU\n130\u2192 co-fabrication turns each proposed design into test data in hours, with no\n131\u2192 joining of parts.\n132\u21924. *(Optional fourth point, if time allows)* The same closed loop points at\n133\u2192 what comes next: tensegrity lattices, flight-relevant materials, and\n134\u2192 safety hardware on Earth.\n135\u2192\n136\u2192## Body\n137\u2192\n138\u2192### Point 1\n139\u2192\n140\u2192When model calibration and exhaustive testing are both costly, each physical\n141\u2192specimen must be chosen for its information value.\n142\u2192\n143\u2192- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144\u2192 these structures, a sufficiently calibrated multiphysics model is expensive\n145\u2192 to build relative to the design campaign itself \u2014 and the hardest quantity,\n146\u2192 peak transmitted shock, is a millisecond-scale transient that even\n147\u2192 heavy-duty simulation struggles to resolve. The gap persists in the actual\n148\u2192 engineering materials a real lander would use (steel struts and cables),\n149\u2192 so this is not merely an artifact of printed PLA\u2013TPU.\n150\u2192- The alternative \u2014 sweeping the design space physically \u2014 fails on cost: a\n151\u2192 traditional design of experiments over geometry plus FDM process parameters\n152\u2192 would take hundreds of specimens, each needing print + test time.\n153\u2192- Therefore: treat the physical experiment as the authoritative source of\n154\u2192 objective values, and be ruthless about *which* experiments to run.\n155\u2192- Anticipate the FEA-veteran pushback (\"inconvenient \u2260 untrustworthy\"): frame\n156\u2192 it as a cost-and-dependence decision, and keep a backup slide with the\n157\u2192 literature (or our own validation) on simulation\u2013experiment discrepancy for\n158\u2192 impact transients.\n159\u2192\n160\u2192### Transition\n161\u2192\n162\u2192If every data point must be a real experiment, we need a method that extracts\n163\u2192the most from every specimen \u2014 we need to make the process radically more\n164\u2192sample-efficient.\n165\u2192\n166\u2192### Point 2\n167\u2192\n168\u2192Bayesian optimization makes physical experimentation affordable, even with\n169\u2192limited, noisy data.\n170\u2192\n171\u2192- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172\u2192 visual in the main talk; implementation detail goes to backup slides.\n173\u2192 Spoken version:\n174\u2192 > \"After each batch, the model estimates both expected performance and\n175\u2192 > uncertainty across the design space. The acquisition rule selects a small\n176\u2192 > next batch that is most likely to expand the feasible trade-off frontier,\n177\u2192 > while accounting for noisy tests and print failures.\"\n178\u2192- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179\u2192 motivate a noise-aware acquisition function (qNEHVI) rather than\n180\u2192 non-noise-aware alternatives. (Backup slide: how observation noise is\n181\u2192 actually modeled \u2014 replicates, noise identification \u2014 and budget-matched\n182\u2192 baselines such as Sobol/random sampling.)\n183\u2192- Small parallel batches: the surrogate proposes the next batch of specimens\n184\u2192 to print, balancing exploration and exploitation.\n185\u2192- Constrained multi-objective by construction: **objectives** = maximize\n186\u2192 specific energy absorption and compaction efficiency; **constraint** = peak\n187\u2192 transmitted force must stay under a cap; the deliverable is the *feasible*\n188\u2192 Pareto front between the two objectives among force-cap-satisfying designs.\n189\u2192 (Keep this formulation consistent everywhere \u2014 do not present \"SEA vs. peak\n190\u2192 force\" as the trade-off.)\n191\u2192- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192\u2192 this advanced task (noisy, batched, constrained, multi-objective) in\n193\u2192 minutes using [Honegumi](https://honegumi.readthedocs.io/) \u2014 the audience\n194\u2192 can do the same for their own problems; show the code-template picker\n195\u2192 briefly so they see how low the barrier is.\n196\u2192\n197\u2192### Transition\n198\u2192\n199\u2192The optimizer is no longer the bottleneck \u2014 the limit is now providing real\n200\u2192data quickly enough.\n201\u2192\n202\u2192### Point 3\n203\u2192\n204\u2192Multi-material additive manufacturing supplies that data fast \u2014 single-build\n205\u2192co-fabrication, with no joining of parts.\n206\u2192\n207\u2192- Single-build PLA\u2013TPU co-fabrication: rigid struts and flexible tension\n208\u2192 network in one print \u2014 no joining or hand assembly of members, which closes\n209\u2192 the \"easy creation\" gap from the Need and is what makes future lattices\n210\u2192 feasible at all. (Be precise: if support removal, trimming, or conditioning\n211\u2192 remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212\u2192 assembly.\")\n213\u2192- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214\u2192 as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215\u2192 pre-tension status) plus a short deformation sequence \u2014 so the EDL engineer\n216\u2192 doesn't file it as a \"crush structure\" and the grad student can tell it from\n217\u2192 an ordinary lattice.\n218\u2192- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219\u2192 tested specimen (quasi-static compression + instrumented drop-weight impact)\n220\u2192 in hours. (Backup slide: representative print-to-result time breakdown and\n221\u2192 which steps remain manual \u2014 candidate selection is automated; slicing,\n222\u2192 handling, and testing are not yet.)\n223\u2192- Now we have the data we need to complete the loop.\n224\u2192\n225\u2192### Point 4 (optional \u2014 future applications)\n226\u2192\n227\u2192*Include if timing allows; otherwise fold into the Conclusion.*\n228\u2192\n229\u2192The same closed loop transfers wherever tests are authoritative, each test is\n230\u2192costly, the design space is mixed, and several performance goals conflict:\n231\u2192\n232\u2192- Tensegrity *lattices* and more complex architectures \u2014 exactly where hand\n233\u2192 assembly becomes infeasible and single-build co-fabrication pays off.\n234\u2192- Migration from the PLA\u2013TPU proxy toward flight-relevant materials and\n235\u2192 impact regimes.\n236\u2192- Safety hardware on Earth (e.g., the tensegrity crutch tip \u2014 future work).\n237\u2192- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238\u2192 handling, testing) in the loop.\n239\u2192\n240\u2192### Evidence / results\n241\u2192\n242\u2192*What we found \u2014 ground the conclusion in shown data*\n243\u2192\n244\u2192Build the talk around a quantitative three-slide sequence (per the\n245\u2192mock-audience report; insert observed values once the campaign completes):\n246\u2192\n247\u21921. **Campaign ledger** \u2014 \"The campaign used N specimens across B adaptive\n248\u2192 batches under a fixed physical-test budget\": initialization vs. adaptive\n249\u2192 batches, replicates, print failures, cycle time, and the numerical value\n250\u2192 (and rationale) of the force cap.\n251\u21922. **Budget-matched baseline** \u2014 \"Adaptive selection improved the feasible\n252\u2192 design set faster than [predeclared baseline]\": performance vs. number of\n253\u2192 physical tests against Sobol/random sampling at the same budget, with\n254\u2192 uncertainty where available.\n255\u21923. **Measured feasible Pareto front** \u2014 specific energy absorption vs.\n256\u2192 compaction efficiency among designs satisfying the force cap, with\n257\u2192 replicate spread, marked infeasible designs, and specimen photos or\n258\u2192 force\u2013time traces for representative Pareto designs.\n259\u2192\n260\u2192Supporting raw-data candidates: drop-test force\u2013time traces, Sobol /\n261\u2192first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262\u2192\n263\u2192### Suggested 15-minute allocation\n264\u2192\n265\u2192*(From the mock-audience report \u2014 results get the largest block.)*\n266\u2192\n267\u2192| Time | Content |\n268\u2192|---|---|\n269\u2192| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270\u2192| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271\u2192| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272\u2192| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273\u2192| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274\u2192| 1 min | Limitations and transfer conditions |\n275\u2192| 1 min | Measured conclusion + bookend |\n276\u2192\n277\u2192Move kernel choices, encodings, and other implementation detail to backup\n278\u2192slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279\u2192[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280\u2192\n281\u2192## Closing\n282\u2192\n283\u2192### Review\n284\u2192\n285\u2192*A recap of the body, leading into the conclusion*\n286\u2192\n287\u2192Using real, measured impact data from additively manufactured\n288\u2192tensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\n289\u2192optimization routine \u2014 we rapidly iterate toward better energy absorbers.\n290\u2192This feedback loop makes tensegrity optimization dramatically faster: dozens\n291\u2192of prints instead of the hundreds a traditional design of experiments over\n292\u2192this design space would require.\n293\u2192\n294\u2192### Conclusion\n295\u2192\n296\u2192*What the above means to the audience in the end*\n297\u2192\n298\u2192Because the loop runs on physical measurements, it is useful precisely where\n299\u2192model calibration is not worth its cost \u2014 and the same closed-loop pattern\n300\u2192should transfer (a conditional claim, pending evidence) to other additively\n301\u2192manufactured architectures whose performance is dominated by\n302\u2192hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303\u2192*use this loop when tests are authoritative, each test is costly, the design\n304\u2192space is mixed, and several performance goals conflict.* For tensegrity\n305\u2192specifically, it opens a path from this T3 proxy study toward lattices,\n306\u2192flight-relevant materials, payload protection on other planets, and safety\n307\u2192hardware here on Earth (e.g., the crutch tip, as future work).\n308\u2192\n309\u2192### Close\n310\u2192\n311\u2192*A way to end the presentation clearly and elegantly*\n312\u2192\n313\u2192Return to the opening image: the next planetary lander's energy absorber \u2014 a\n314\u2192structure that has to survive not one impact but many \u2014 doesn't have to take\n315\u2192years of hand-built prototypes; with this loop, candidate designs can be\n316\u2192designed, printed, and tested against real impacts in weeks instead of months\n317\u2192or longer. End on the measured engineering result and its transfer condition,\n318\u2192then the bookend: every method that compresses design time moves our ventures\n319\u2192\u2014 on this planet and beyond \u2014 forward.\n320\u2192\n321\u2192---\n322\u2192\n323\u2192## Changes from Draft 2\n324\u2192\n325\u2192Applied from @sgbaird's inline review comments on PR #84 and the\n326\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327\u2192\n328\u2192**From the inline review comments:**\n329\u2192\n330\u21921. **Reusability in the hook** (line 22): the tensegrity lander survives\n331\u2192 multiple drops, unlike crushable/sacrificial alternatives.\n332\u21922. **Proxy-system framing** (lines 49, 87): PLA\u2013TPU would never fly; it is a\n333\u2192 proxy for prototyping a self-driving-lab-style workflow \u2014 stated in a\n334\u2192 scope note up front and re-stated right after the hook.\n335\u21923. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336\u2192 heavy-duty simulation falls short of the experiments (millisecond-scale\n337\u2192 peak shock), not that it merely costs too much.\n338\u21924. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339\u2192 complex structures and especially lattices are not.\n340\u21925. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341\u2192 (temperature, print speed, \u2026) only; the rest is explicitly future work.\n342\u21926. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343\u2192 literature search (or validation of experiments against simulation), plus\n344\u2192 the millisecond-resolution peak-shock argument.\n345\u21927. **Optional Point 4 added** (line 97): future applications, with a note it\n346\u2192 can fold into the Conclusion if time is short.\n347\u21928. **Steel-materials point** (line 104): simulations fall short for the\n348\u2192 actual engineering materials (steel struts/cables), not just printed\n349\u2192 PLA\u2013TPU.\n350\u21929. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351\u2192 in minutes, pitched so the audience sees they can use it too.\n352\u219210. **\"Thousands\" \u2192 \"hundreds\"** (line 173): baseline is now a traditional\n353\u2192 design of experiments over a high-dimensional space (hundreds), in the\n354\u2192 main message, Need, Point 1, and Review.\n355\u219211. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356\u2192 or longer\" for candidate designs.\n357\u2192\n358\u2192**From the mock-audience report's priority revisions:**\n359\u2192\n360\u219212. **Quantitative three-slide results sequence** (Priority 1): campaign\n361\u2192 ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front, with a\n362\u2192 fallback main-message wording if the campaign can't yet support the\n363\u2192 efficiency claim.\n364\u219213. **Anti-simulation premise replaced with a scoped cost decision**\n365\u2192 (Priority 2): Point 1 retitled to the information-value message;\n366\u2192 \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367\u2192 anticipated with a backup slide.\n368\u219214. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369\u2192 implementation detail moved to backup slides; Q&A prep pointed at the\n370\u2192 report's claims-vs-evidence table.\n371\u219215. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372\u2192 the main talk, machinery in backup slides; the misleading\n373\u2192 \"qNEHVI over standard expected improvement\" comparison replaced with\n374\u2192 \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375\u219216. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376\u2192 sequence in Point 3, so the structure isn't remembered as a generic crush\n377\u2192 structure or lattice.\n378\u219217. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379\u2192 co-fabrication / no joining of parts,\" with a note to list remaining\n380\u2192 manual steps and an automation-status caveat.\n381\u219218. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382\u2192 compaction efficiency, constraint = force cap, deliverable = *feasible*\n383\u2192 Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384\u219219. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385\u2192 transfer claim marked conditional, and the close ends on the measured\n386\u2192 result before the bookend.\n387\u2192\n388\u2192## Changes from Draft 1\n389\u2192\n390\u2192Feedback from the PR #84 review, applied in Draft 2:\n391\u2192\n392\u21921. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n393\u2192 dozens-not-thousands claim); body and message now carry the same story.\n394\u21922. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n395\u2192 manuscript's positioning (#75/#76) is that the physical experiment is the\n396\u2192 source of truth, explicitly avoiding calibrated FE simulation.\n397\u21923. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n398\u2192 parametric design space and avoid an over-claim to a design-automation\n399\u2192 audience.\n400\u21924. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401\u2192 energy absorption and compaction efficiency, cap peak transmitted force.\n402\u21925. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403\u2192 *and* the space is too big to brute-force \u2014 which makes BO + AM the\n404\u2192 inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405\u2192 above.)*\n406\u21926. **Point 1 given its own content** (the *why* behind the inefficiency)\n407\u2192 instead of restating the Need.\n408\u21927. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409\u2192 the specifics (noise-aware acquisition, small batches, multi-objective).\n410\u21928. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411\u2192 (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412\u2192 above.)*\n413\u21929. **Evidence/results slot added** before the Closing, so the Conclusion is\n414\u2192 grounded in shown data rather than three hedged \"may\"s.\n415\u219210. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n416\u2192 Ball Bot visual; verified video links included.\n417\u219211. **Close bookends the opening** (the next lander's absorber, designed in\n418\u2192 weeks) before the broader humankind line.\n419\u219212. **Spelling**: Doumont (was \"Duomont\").", + "name": "read", + "tool_call_id": "call_93haurOMDGiH2mQFFbaMaYKn", + "info": { + "start_ts": 8147.331065886, + "end_ts": 8147.336467845 + } + }, + { + "role": "tool", + "content": " 1\u2192# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n 2\u2192\n 3\u2192Video: (assigned in #83)\n 4\u2192\n 5\u2192Transcribed from @me-madsen's handwritten notes\n 6\u2192([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7\u2192dated 2026-07-15).\n 8\u2192\n 9\u2192## Overview\n10\u2192\n11\u2192A presentation is all about the right content, displayed the right way, at the\n12\u2192right time.\n13\u2192\n14\u2192- Verbal communication is sequential.\n15\u2192- Message is *interpretation* of the information.\n16\u2192 - Ensure the audience can get the most out of what you're doing.\n17\u2192 - Don't just give them information \u2014 help them understand it, interpret it,\n18\u2192 and internalize it.\n19\u2192\n20\u2192## Doumont's suggested rules for a presentation\n21\u2192\n22\u21920. Ensure you have **messages** for your audience, not just words.\n23\u21921. **Adapt to your audience.**\n24\u2192 - Think like the audience, not the speaker.\n25\u21922. **Maximize signal-to-noise ratio.**\n26\u2192 - Reduce noise (animations, laser pointers, anything that pulls attention\n27\u2192 away from your message).\n28\u21923. **Use effective redundancy.**\n29\u2192 - Tell your message in word *and* on slide: someone deaf should understand\n30\u2192 your messages just as well as someone blind.\n31\u2192 - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32\u2192 date).\n33\u2192\n34\u2192## When creating slides\n35\u2192\n36\u2192- If the audience doesn't need it, why put it on the slides?\n37\u2192- People shouldn't have to count the items on a slide to know how many are\n38\u2192 there.\n39\u2192- Don't use your slides as your \"presentation notes.\"\n40\u2192 - Don't show your notes to the audience.\n41\u2192 - **A bad slide is worse than no slide at all.**\n42\u2192- **One message per slide** is a good rule of thumb.\n43\u2192 - Prioritize the *so what* (or *therefore what*) of your message.\n44\u2192- Make it easy to interpret the information on the slide:\n45\u2192 - 3\u20135 items at most.\n46\u2192 - Align the title left.\n47\u2192 - Do not clutter slides with repetitive or unnecessary information: logos,\n48\u2192 affiliation, date, slide numbers.\n49\u2192- Doumont's suggested workflow:\n50\u2192 1. **Start with the title.**\n51\u2192 - Use a full sentence in the title.\n52\u2192 - The title is the main message of the slide.\n53\u2192 - Make it easy to read: left-aligned, with intentional line breaks.\n54\u2192 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55\u2192- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56\u2192\n57\u2192## General preparation\n58\u2192\n59\u2192- Planning for audience, content, and structure matters more than materials\n60\u2192 (i.e., slides).\n61\u2192- Try using one sheet of paper for presentation preparation.\n62\u2192- It's most important that your presentation is:\n63\u2192 - well planned, structured, and delivered;\n64\u2192 - **if you don't have a lot of time to prepare, don't focus on making\n65\u2192 slides.**\n66\u2192- Practice the presentation a few times. Practice at least once *without* your\n67\u2192 slideshow!\n68\u2192- If doing a handout, keep it separate from the slides you're preparing.\n69\u2192- Impress the audience with your body.\n70\u2192 - Use gestures, not laser pointers.\n71\u2192 - \"Control\" the audience with your body \u2014 keep their attention on your\n72\u2192 presentation.\n73\u2192\n74\u2192## Impressions / what to apply to the IDETC presentation\n75\u2192\n76\u2192\n77\u2192\n78\u2192## Questions / things to discuss\n79\u2192\n80\u2192", + "name": "read", + "tool_call_id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "info": { + "start_ts": 8147.331133416, + "end_ts": 8147.338304015 + } + }, + { + "role": "tool", + "content": " 1\u2192\\documentclass[10pt]{article}\n 2\u2192\n 3\u2192% --- Packages ---\n 4\u2192\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5\u2192\\usepackage[hidelinks]{hyperref}\n 6\u2192\\usepackage{parskip}\n 7\u2192\\usepackage{graphicx}\n 8\u2192\\usepackage{caption}\n 9\u2192\\usepackage{titlesec}\n 10\u2192\\usepackage{wrapfig}\n 11\u2192\\usepackage[numbers,sort&compress]{natbib}\n 12\u2192\n 13\u2192% --- Formatting ---\n 14\u2192\\setlength{\\parskip}{0.2em}\n 15\u2192\\setlength{\\parindent}{0em}\n 16\u2192\\titleformat*{\\section}{\\large\\bfseries}\n 17\u2192\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18\u2192\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19\u2192\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20\u2192\\setlength{\\emergencystretch}{2em}\n 21\u2192\\setlength{\\intextsep}{0.2em}\n 22\u2192\\bibliographystyle{abbrvnat}\n 23\u2192\n 24\u2192\\begin{document}\n 25\u2192\n 26\u2192\\begin{center}\n 27\u2192{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28\u21923D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29\u2192\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30\u2192\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31\u2192\\end{center}\n 32\u2192\n 33\u2192\\vspace{0.4em}\n 34\u2192\n 35\u2192\\section*{Abstract}\n 36\u2192Tensegrity-inspired architectures---rigid struts suspended within a\n 37\u2192continuous flexible network---can exhibit tunable nonlinear\n 38\u2192force--displacement responses and favorable energy absorption per unit\n 39\u2192mass, motivating their use in lightweight energy-absorbing and protective\n 40\u2192structures\n 41\u2192\\citep{skelton2009tensegrity,pajunen2019design}.\n 42\u2192Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43\u2192struts and flexible (TPU) elements in a single build\n 44\u2192\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45\u2192space---strut geometry, tension-element cross-section, connectivity\n 46\u2192topology, and unit-cell tiling---is too large to explore by trial and\n 47\u2192error. We present a closed-loop experimental campaign that uses\n 48\u2192Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49\u2192to drive a design--print--test workflow operating directly on physical\n 50\u2192measurements, without relying on calibrated finite-element simulation\n 51\u2192for objective evaluation.\n 52\u2192\n 53\u2192\\section{Introduction and Motivation}\n 54\u2192\n 55\u2192\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56\u2192\\centering\n 57\u2192\\vspace{-0.4em}\n 58\u2192\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59\u2192\\captionsetup{font=small}\n 60\u2192\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61\u2192Candidate tensegrity-inspired unit cells are instantiated from a\n 62\u2192parameter vector, fabricated by multi-material FDM, and tested under\n 63\u2192quasi-static compression and drop-weight impact. Measured peak force,\n 64\u2192specific energy absorption (SEA), and compaction efficiency update a\n 65\u2192Gaussian-process surrogate that proposes the next batch of designs.}\n 66\u2192\\label{fig:overview}\n 67\u2192\\vspace{-0.4em}\n 68\u2192\\end{wrapfigure}\n 69\u2192\n 70\u2192Tensegrity structures combine compression-only members with a\n 71\u2192pre-tensioned tension network to achieve stiffness without rigid\n 72\u2192joinery~\\citep{skelton2009tensegrity}. Their\n 73\u2192nonlinear, often load-limiting force--displacement response makes them\n 74\u2192attractive for impact mitigation, packaging, and wearable protective\n 75\u2192gear~\\citep{pajunen2019design}. Idealized\n 76\u2192cable--strut tensegrities are difficult to manufacture at scale, but\n 77\u2192\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78\u21923D printing reproduce key tensegrity behaviors---including\n 79\u2192post-buckling stability and load-limiting force--displacement\n 80\u2192response~\\citep{pajunen2019design}---motivating our extension to\n 81\u2192multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82\u2192elements. PLA provides rigid struts; TPU provides flexible,\n 83\u2192rate-dependent tension elements whose viscoelasticity is expected to\n 84\u2192contribute hysteretic energy dissipation under impact loading,\n 85\u2192consistent with the energy-absorbing behavior reported for rigid--TPU\n 86\u2192multi-material prints~\\citep{khatri2024energy}.\n 87\u2192\n 88\u2192The challenge is that small geometric and topological changes can\n 89\u2192produce large changes in the measured response, and high-fidelity\n 90\u2192simulation of FDM specimens is computationally expensive and often\n 91\u2192struggles to capture physical defects such as interfacial slip and\n 92\u2192TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93\u2192trial-and-error sweeps over the joint design space are also infeasible:\n 94\u2192even modest parameter resolutions yield thousands of candidates, each\n 95\u2192requiring print and test time. We instead treat the physical\n 96\u2192experiment as the source of truth and use BO to select which specimens\n 97\u2192are most informative to fabricate next---an approach used in related\n 98\u2192closed-loop materials and structures\n 99\u2192campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100\u2192Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101\u2192set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102\u2192designs using parallel, noisy multi-objective BO driven by measured\n103\u2192$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104\u2192\n105\u2192\\section{Approach}\n106\u2192\\subsection{Closed-Loop Workflow}\n107\u2192Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108\u2192proceeds in four steps: (i)~a parameterized unit-cell design is\n109\u2192instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110\u2192is sliced and printed on a multi-material FDM system; (iii)~it is\n111\u2192tested under quasi-static compression and instrumented drop-weight\n112\u2192impact; and (iv)~the resulting performance metrics update a\n113\u2192Gaussian-process (GP) surrogate, which a batch acquisition function\n114\u2192queries to recommend the next set of candidates. Surrogate updating\n115\u2192and experiment selection are automated; specimen handling, slicing,\n116\u2192and test setup remain manual.\n117\u2192\n118\u2192\\subsection{Design Space}\n119\u2192We parameterize a family of unit cells using a core-wrapping\n120\u2192architecture in which rigid PLA struts are enclosed by continuous TPU\n121\u2192skins; the wrapping rationale follows multi-material FDM strategies in\n122\u2192which rigid panels are wrapped by stretchable soft material to prevent\n123\u2192delamination and improve interfacial bonding at the rigid--flexible\n124\u2192interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125\u2192comprises continuous geometric variables (strut diameter and length,\n126\u2192tension-element width and thickness), an integer strut count per unit\n127\u2192cell, and two categorical variables (connectivity topology, unit-cell\n128\u2192tiling pattern). Categorical choices are encoded explicitly rather\n129\u2192than embedded in a continuous metric, and bounds on the continuous\n130\u2192variables are chosen to remain within the printer's resolution and\n131\u2192overhang limits and to keep specimens in a common form factor for the\n132\u2192test fixtures.\n133\u2192\n134\u2192\\subsection{Surrogate and Acquisition}\n135\u2192The GP surrogate is trained on three measured performance metrics:\n136\u2192peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137\u2192(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138\u2192objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139\u2192likelihood with priors to avoid pathological length scales at small $n$.\n140\u2192Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141\u2192interfacial defects motivates our use of q-noisy expected hypervolume\n142\u2192improvement (qNEHVI), which explicitly accounts for observation noise\n143\u2192in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144\u2192expected-improvement variants. Print failures are treated as a\n145\u2192probabilistic feasibility constraint: the acquisition score for each\n146\u2192candidate is its qNEHVI improvement weighted by the GP-estimated\n147\u2192probability of successful fabrication, following recent constrained\n148\u2192multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149\u2192Where applicable, we use numerically stable log-space variants of\n150\u2192improvement-based acquisitions to mitigate vanishing-gradient\n151\u2192pathologies as data and constraints\n152\u2192accumulate~\\citep{ament2023logei}.\n153\u2192\n154\u2192\\section{Methods}\n155\u2192\\subsection{Fabrication}\n156\u2192Specimens are fabricated on a multi-material FDM system capable of\n157\u2192co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158\u2192Print parameters (temperature, line width, infill, retraction) are\n159\u2192held fixed within a batch to isolate the effect of the design\n160\u2192variables. Each specimen is weighed and dimensionally inspected prior\n161\u2192to testing so that mass and geometric deviations are available as\n162\u2192covariates.\n163\u2192\n164\u2192\\subsection{Mechanical Testing}\n165\u2192Quasi-static compression is performed on a screw-driven load frame at\n166\u2192a fixed nominal strain rate, with force--displacement recorded to\n167\u2192densification. Drop-weight impact testing uses an instrumented tup at\n168\u2192a fixed impact energy; transmitted force is captured at high sample\n169\u2192rate, and specimens are imaged before and after impact to document\n170\u2192failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171\u2192unit mass, and compaction efficiency $\\eta$, defined consistently\n172\u2192across the campaign. A control specimen from an earlier batch is\n173\u2192retested periodically to monitor TPU batch-to-batch and printer drift.\n174\u2192\n175\u2192\\section{Expected Outcomes and Discussion}\n176\u2192We will report how BO-selected designs evolve across iterations, how\n177\u2192GP surrogate predictions and uncertainties change as data accumulate,\n178\u2192and which Pareto-efficient trade-offs emerge between SEA and peak\n179\u2192transmitted force within the explored design space. We will also\n180\u2192discuss practical lessons for operating the closed-loop workflow,\n181\u2192including handling of print failures, batch-to-batch variability in\n182\u2192TPU response, the influence of the rigid--flexible interface on\n183\u2192energy absorption, and the exploration--exploitation balance of the\n184\u2192acquisition function under realistic experimental noise. We expect\n185\u2192the campaign to clarify where physical experimentation is genuinely\n186\u2192required versus where simpler analytical or multi-fidelity surrogate\n187\u2192shortcuts~\\citep{mo2023accelerated} could be\n188\u2192substituted in future work.\n189\u2192\n190\u2192\\section{Conclusion}\n191\u2192This work demonstrates a partially automated, experiment-driven\n192\u2192closed-loop pipeline for designing multi-material 3D-printed\n193\u2192tensegrity-inspired energy absorbers, with surrogate updating and\n194\u2192candidate selection automated and fabrication and testing kept manual.\n195\u2192By updating a GP surrogate directly from physical measurements, the\n196\u2192workflow avoids direct dependence on calibrated finite-element\n197\u2192simulation for objective evaluation while still providing principled,\n198\u2192multi-objective design recommendations. The same closed-loop pattern is\n199\u2192expected to transfer to other multi-material additively manufactured\n200\u2192architectures whose performance is dominated by hard-to-simulate\n201\u2192effects.\n202\u2192\n203\u2192{\\tiny\n204\u2192\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205\u2192\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206\u2192% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207\u2192% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208\u2192\\providecommand{\\doi}[1]{}\n209\u2192\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210\u2192\\bibliography{references}\n211\u2192}\n212\u2192\n213\u2192\\end{document}", + "name": "read", + "tool_call_id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "info": { + "start_ts": 8147.332633466, + "end_ts": 8147.339043095 + } + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\", \"offset\": 174, \"limit\": 100}", + "name": "read" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "tool", + "content": " 1\u2192# IDETC Tensegrity Slides \u2014 Draft 1 (extracted content)\n 2\u2192\n 3\u2192Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4\u2192\n 5\u2192\n 6\u2192## Slide 1 \u2014 layout: Title Only\n 7\u2192\n 8\u2192**On-slide text:**\n 9\u2192- **Title 1**: Let\u2019s build better tensegrity structures faster\n 10\u2192- **TextBox 4**: Marcus Madsen\n 11\u2192Undergraduate researcher under Sterling Baird, Ph.D.\n 12\u2192- **TextBox 6**: IDETC 2026\n 13\u2192\n 14\u2192**Non-text shapes / media / placeholders:**\n 15\u2192- [PICTURE (13)] Picture 3\n 16\u2192- [PICTURE (13)] Picture 8\n 17\u2192\n 18\u2192**Presenter notes:**\n 19\u2192> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20\u2192> \n 21\u2192> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22\u2192\n 23\u2192\n 24\u2192## Slide 2 \u2014 layout: Title Only\n 25\u2192\n 26\u2192**On-slide text:**\n 27\u2192- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings\u2014and to problems closer to home.\n 28\u2192\n 29\u2192**Non-text shapes / media / placeholders:**\n 30\u2192- [MEDIA (16)] Online Media 5\n 31\u2192\n 32\u2192**Presenter notes:**\n 33\u2192> Replace this video with a downloaded and trimmed version of just the fall. \n 34\u2192> \n 35\u2192> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle \u2014 thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander\u00a0is\u00a0the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit\u00a0and get back up\u00a0\u2014 it survives multiple drops and keeps working.\n 36\u2192\n 37\u2192\n 38\u2192## Slide 3 (HIDDEN) \u2014 layout: Title Only\n 39\u2192\n 40\u2192**On-slide text:**\n 41\u2192- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42\u2192- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43\u2192- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44\u2192\n 45\u2192**Presenter notes:**\n 46\u2192> This might be better explained purely vocally while on the previous slide.\n 47\u2192\n 48\u2192\n 49\u2192## Slide 4 \u2014 layout: Title Only\n 50\u2192\n 51\u2192**On-slide text:**\n 52\u2192- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53\u2192- **TextBox 3**: Iteration\n 54\u2192- **TextBox 5**: Building\n 55\u2192- **TextBox 7**: Testing\n 56\u2192\n 57\u2192**Non-text shapes / media / placeholders:**\n 58\u2192- [MEDIA (16)] AdobeStock_207717222\n 59\u2192- [MEDIA (16)] Online Media 2\n 60\u2192- [PICTURE (13)] Picture 14\n 61\u2192- [PICTURE (13)] Picture 16\n 62\u2192- [PICTURE (13)] Picture 18\n 63\u2192\n 64\u2192**Presenter notes:**\n 65\u2192> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn\u2019t become distracting because it\u2019s fun to watch it spin.\n 66\u2192> \n 67\u2192> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68\u2192\n 69\u2192\n 70\u2192## Slide 5 (HIDDEN) \u2014 layout: Title Only\n 71\u2192\n 72\u2192**On-slide text:**\n 73\u2192- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74\u2192- **TextBox 14**: Baird et al. Matter (2022)\n 75\u2192\n 76\u2192**Non-text shapes / media / placeholders:**\n 77\u2192- [GROUP (6)] Group 2\n 78\u2192\n 79\u2192**Presenter notes:**\n 80\u2192> Consult Sterling on this slide. I don\u2019t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it\u2019s well enough known that it\u2019d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81\u2192\n 82\u2192\n 83\u2192## Slide 6 (HIDDEN) \u2014 layout: Title Only\n 84\u2192\n 85\u2192**On-slide text:**\n 86\u2192- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87\u2192- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88\u2192\n 89\u2192**Presenter notes:**\n 90\u2192> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91\u2192\n 92\u2192\n 93\u2192## Slide 7 \u2014 layout: Title Only\n 94\u2192\n 95\u2192**On-slide text:**\n 96\u2192- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97\u2192- **TextBox 5**: 6 variables\n 98\u2192- **TextBox 7**: 2 types of input data\n 99\u2192- **TextBox 9**: 2 objectives\n100\u2192- **TextBox 17**: *image of blackbox*\n101\u2192\n102\u2192**Non-text shapes / media / placeholders:**\n103\u2192- [LINE (9)] Connector: Curved 11\n104\u2192- [LINE (9)] Connector: Curved 13\n105\u2192- [LINE (9)] Connector: Curved 15\n106\u2192\n107\u2192**Presenter notes:**\n108\u2192> Very rough slide as I\u2019m not entirely sure what graphic or text to put on this, partially because I\u2019m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109\u2192\n110\u2192\n111\u2192## Slide 8 \u2014 layout: Title Only\n112\u2192\n113\u2192**On-slide text:**\n114\u2192- **Title 1**: Multi-material additive manufacturing supplies that data fast \u2014 single-build co-fabrication, with no joining of parts.\n115\u2192\n116\u2192**Non-text shapes / media / placeholders:**\n117\u2192- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118\u2192- [PICTURE (13)] Picture 4\n119\u2192\n120\u2192**Presenter notes:**\n121\u2192> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122\u2192\n123\u2192\n124\u2192## Slide 9 \u2014 layout: Title Only\n125\u2192\n126\u2192**On-slide text:**\n127\u2192- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128\u2192- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129\u2192- **TextBox 5**: *placeholder for slo motion of drop test*\n130\u2192\n131\u2192\n132\u2192## Slide 10 \u2014 layout: Title Only\n133\u2192\n134\u2192**On-slide text:**\n135\u2192- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136\u2192- **TextBox 3**: Printing\n137\u2192- **TextBox 5**: Testing\n138\u2192- **TextBox 7**: BO feed\n139\u2192\n140\u2192**Non-text shapes / media / placeholders:**\n141\u2192- [PICTURE (13)] Picture 14\n142\u2192- [PICTURE (13)] Picture 16\n143\u2192- [PICTURE (13)] Picture 18\n144\u2192- [PICTURE (13)] Graphic 8\n145\u2192\n146\u2192**Presenter notes:**\n147\u2192> I think it might be better if we replaced \u201cprinting\u201d with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148\u2192\n149\u2192\n150\u2192## Slide 11 (HIDDEN) \u2014 layout: Title Only\n151\u2192\n152\u2192**On-slide text:**\n153\u2192- **Title 1**: Reserved for results\n154\u2192\n155\u2192**Presenter notes:**\n156\u2192> Would be helpful if we could show that this process works in this case.\n157\u2192\n158\u2192\n159\u2192## Slide 12 \u2014 layout: Title Only\n160\u2192\n161\u2192**On-slide text:**\n162\u2192- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163\u2192- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164\u2192- **TextBox 3**: *image to show lattice structure\n165\u2192\n166\u2192\n167\u2192## Slide 13 \u2014 layout: Title Only\n168\u2192\n169\u2192**Non-text shapes / media / placeholders:**\n170\u2192- [PICTURE (13)] Picture 3\n171\u2192\n172\u2192**Presenter notes:**\n173\u2192> Slide for questions at the end\n174\u2192\n175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "info": { + "start_ts": 8147.330900226, + "end_ts": 8147.337548875 + } + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Presentation \u2014 Doumont Presentation Structure Template\n 2\u2192\n 3\u2192Transcribed from Jean-luc Doumont's presentation-structure template\n 4\u2192([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5\u2192adapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\n 6\u2192All rights reserved. Can be downloaded from .\n 7\u2192\n 8\u2192> To design your presentation, write down your ideas for each component below.\n 9\u2192> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10\u2192\n 11\u2192**Draft 3** \u2014 applies the inline review comments from @sgbaird on PR #84 and the\n 12\u2192priority revisions from the\n 13\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14\u2192Summaries of what changed are at the bottom of this file\n 15\u2192([Draft 2 \u2192 3](#changes-from-draft-2), [Draft 1 \u2192 2](#changes-from-draft-1)).\n 16\u2192\n 17\u2192**Scope note (frame everything below with this):** the study presented is the\n 18\u2192optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19\u2192processing parameters** (e.g., nozzle temperature, print speed). PLA\u2013TPU is a\n 20\u2192**proxy system** \u2014 not flight material \u2014 used to prototype a self-driving-lab\n 21\u2192style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22\u2192The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23\u2192beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24\u2192\n 25\u2192## Opening\n 26\u2192\n 27\u2192### Attention getter\n 28\u2192\n 29\u2192*A way to lead the audience to the need efficiently*\n 30\u2192\n 31\u2192Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32\u2192landings \u2014 and to problems closer to home.\n 33\u2192\n 34\u2192One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35\u2192clip) do the work:\n 36\u2192\n 37\u2192> Landing a payload on another planet means surviving an impact in conditions\n 38\u2192> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n 39\u2192> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40\u2192> structure: the lander *is* the energy absorber. And unlike crushable\n 41\u2192> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42\u2192> hit **and get back up** \u2014 it survives multiple drops and keeps working.\n 43\u2192\n 44\u2192Candidate Super Ball Bot videos to show (verified links):\n 45\u2192\n 46\u2192- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n 47\u2192 (official channel); the canonical overview.\n 48\u2192- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49\u2192 \u2014 Wall Street Journal; prototype drop footage.\n 50\u2192- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51\u2192 \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n 52\u2192\n 53\u2192Immediately after the hook, state the present study scope in one sentence\n 54\u2192(per the scope note above): *we study a printed PLA\u2013TPU T3 prism as a proxy\n 55\u2192system for prototyping an autonomous tensegrity-optimization workflow* \u2014 so\n 56\u2192the planetary framing never outruns the demonstrated scope.\n 57\u2192\n 58\u2192\n 60\u2192\n 61\u2192### Need\n 62\u2192\n 63\u2192*A difference between actual and desired situations*\n 64\u2192\n 65\u2192**Actual situation** (a two-sided pincer \u2014 stated as a scoped engineering\n 66\u2192judgment, not an absolute):\n 67\u2192\n 68\u2192- For these structures, obtaining trustworthy objective values from a\n 69\u2192 validated high-fidelity model would require substantial calibration of\n 70\u2192 interface, rate, contact, and defect behavior \u2014 and even heavy-duty\n 71\u2192 simulation falls short of the experiments we are running: peak transmitted\n 72\u2192 shock is a millisecond-scale transient that is notoriously difficult to\n 73\u2192 resolve numerically. This holds even for idealized cable\u2013strut tensegrities\n 74\u2192 in their actual engineering materials (steel or similar), not just for\n 75\u2192 printed PLA\u2013TPU.\n 76\u2192 \n 79\u2192- Exhaustive physical search fails on cost: a traditional design of\n 80\u2192 experiments over this relatively high-dimensional space (geometry + FDM\n 81\u2192 process parameters) would demand hundreds of specimens, each costing print\n 82\u2192 and test time.\n 83\u2192- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84\u2192 more complex structures \u2014 especially tensegrity *lattices* \u2014 become\n 85\u2192 extremely difficult and ultimately infeasible to hand-build.\n 86\u2192\n 87\u2192**Desired situation:**\n 88\u2192\n 89\u2192- Trustworthy performance data without hand assembly (easy creation of\n 90\u2192 tensegrity structures, scalable beyond a single prism).\n 91\u2192- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n 92\u2192 not the hundreds a traditional design of experiments would require.\n 93\u2192\n 94\u2192### Task\n 95\u2192\n 96\u2192*What I decided/was asked to do to address the need*\n 97\u2192\n 98\u2192Build a closed-loop design\u2013print\u2013test workflow that optimizes a **T3-prism\n 99\u2192tensegrity-inspired energy absorber together with its FDM processing\n100\u2192parameters** (e.g., temperature, print speed) directly from real, measured\n101\u2192impact data \u2014 maximizing energy absorbed per gram (specific energy\n102\u2192absorption) and compaction efficiency, subject to a cap on the peak force\n103\u2192transmitted to the payload. Extensions beyond the T3 prism are future work.\n104\u2192\n105\u2192### Main message\n106\u2192\n107\u2192*The one sentence I want my audience to remember*\n108\u2192\n109\u2192By closing the loop between multi-material 3D printing and Bayesian\n110\u2192optimization, we can optimize tensegrity-inspired energy absorbers directly\n111\u2192from real impact data \u2014 in dozens of prints, not the hundreds a traditional\n112\u2192design of experiments would demand.\n113\u2192\n114\u2192\n118\u2192\n119\u2192### Preview\n120\u2192\n121\u2192*A map of the body (ideally three points, max. five)*\n122\u2192\n123\u21921. When model calibration and exhaustive testing are both costly, each\n124\u2192 physical specimen must be chosen for its information value \u2014 so the\n125\u2192 measured experiment is our source of truth.\n126\u21922. Bayesian optimization makes physical experimentation affordable: it handles\n127\u2192 noisy measurements, small batches, and the constrained multi-objective\n128\u2192 trade-off \u2014 and Honegumi let us scaffold it in minutes.\n129\u21923. Multi-material additive manufacturing closes the loop: single-build PLA\u2013TPU\n130\u2192 co-fabrication turns each proposed design into test data in hours, with no\n131\u2192 joining of parts.\n132\u21924. *(Optional fourth point, if time allows)* The same closed loop points at\n133\u2192 what comes next: tensegrity lattices, flight-relevant materials, and\n134\u2192 safety hardware on Earth.\n135\u2192\n136\u2192## Body\n137\u2192\n138\u2192### Point 1\n139\u2192\n140\u2192When model calibration and exhaustive testing are both costly, each physical\n141\u2192specimen must be chosen for its information value.\n142\u2192\n143\u2192- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144\u2192 these structures, a sufficiently calibrated multiphysics model is expensive\n145\u2192 to build relative to the design campaign itself \u2014 and the hardest quantity,\n146\u2192 peak transmitted shock, is a millisecond-scale transient that even\n147\u2192 heavy-duty simulation struggles to resolve. The gap persists in the actual\n148\u2192 engineering materials a real lander would use (steel struts and cables),\n149\u2192 so this is not merely an artifact of printed PLA\u2013TPU.\n150\u2192- The alternative \u2014 sweeping the design space physically \u2014 fails on cost: a\n151\u2192 traditional design of experiments over geometry plus FDM process parameters\n152\u2192 would take hundreds of specimens, each needing print + test time.\n153\u2192- Therefore: treat the physical experiment as the authoritative source of\n154\u2192 objective values, and be ruthless about *which* experiments to run.\n155\u2192- Anticipate the FEA-veteran pushback (\"inconvenient \u2260 untrustworthy\"): frame\n156\u2192 it as a cost-and-dependence decision, and keep a backup slide with the\n157\u2192 literature (or our own validation) on simulation\u2013experiment discrepancy for\n158\u2192 impact transients.\n159\u2192\n160\u2192### Transition\n161\u2192\n162\u2192If every data point must be a real experiment, we need a method that extracts\n163\u2192the most from every specimen \u2014 we need to make the process radically more\n164\u2192sample-efficient.\n165\u2192\n166\u2192### Point 2\n167\u2192\n168\u2192Bayesian optimization makes physical experimentation affordable, even with\n169\u2192limited, noisy data.\n170\u2192\n171\u2192- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172\u2192 visual in the main talk; implementation detail goes to backup slides.\n173\u2192 Spoken version:\n174\u2192 > \"After each batch, the model estimates both expected performance and\n175\u2192 > uncertainty across the design space. The acquisition rule selects a small\n176\u2192 > next batch that is most likely to expand the feasible trade-off frontier,\n177\u2192 > while accounting for noisy tests and print failures.\"\n178\u2192- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179\u2192 motivate a noise-aware acquisition function (qNEHVI) rather than\n180\u2192 non-noise-aware alternatives. (Backup slide: how observation noise is\n181\u2192 actually modeled \u2014 replicates, noise identification \u2014 and budget-matched\n182\u2192 baselines such as Sobol/random sampling.)\n183\u2192- Small parallel batches: the surrogate proposes the next batch of specimens\n184\u2192 to print, balancing exploration and exploitation.\n185\u2192- Constrained multi-objective by construction: **objectives** = maximize\n186\u2192 specific energy absorption and compaction efficiency; **constraint** = peak\n187\u2192 transmitted force must stay under a cap; the deliverable is the *feasible*\n188\u2192 Pareto front between the two objectives among force-cap-satisfying designs.\n189\u2192 (Keep this formulation consistent everywhere \u2014 do not present \"SEA vs. peak\n190\u2192 force\" as the trade-off.)\n191\u2192- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192\u2192 this advanced task (noisy, batched, constrained, multi-objective) in\n193\u2192 minutes using [Honegumi](https://honegumi.readthedocs.io/) \u2014 the audience\n194\u2192 can do the same for their own problems; show the code-template picker\n195\u2192 briefly so they see how low the barrier is.\n196\u2192\n197\u2192### Transition\n198\u2192\n199\u2192The optimizer is no longer the bottleneck \u2014 the limit is now providing real\n200\u2192data quickly enough.\n201\u2192\n202\u2192### Point 3\n203\u2192\n204\u2192Multi-material additive manufacturing supplies that data fast \u2014 single-build\n205\u2192co-fabrication, with no joining of parts.\n206\u2192\n207\u2192- Single-build PLA\u2013TPU co-fabrication: rigid struts and flexible tension\n208\u2192 network in one print \u2014 no joining or hand assembly of members, which closes\n209\u2192 the \"easy creation\" gap from the Need and is what makes future lattices\n210\u2192 feasible at all. (Be precise: if support removal, trimming, or conditioning\n211\u2192 remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212\u2192 assembly.\")\n213\u2192- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214\u2192 as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215\u2192 pre-tension status) plus a short deformation sequence \u2014 so the EDL engineer\n216\u2192 doesn't file it as a \"crush structure\" and the grad student can tell it from\n217\u2192 an ordinary lattice.\n218\u2192- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219\u2192 tested specimen (quasi-static compression + instrumented drop-weight impact)\n220\u2192 in hours. (Backup slide: representative print-to-result time breakdown and\n221\u2192 which steps remain manual \u2014 candidate selection is automated; slicing,\n222\u2192 handling, and testing are not yet.)\n223\u2192- Now we have the data we need to complete the loop.\n224\u2192\n225\u2192### Point 4 (optional \u2014 future applications)\n226\u2192\n227\u2192*Include if timing allows; otherwise fold into the Conclusion.*\n228\u2192\n229\u2192The same closed loop transfers wherever tests are authoritative, each test is\n230\u2192costly, the design space is mixed, and several performance goals conflict:\n231\u2192\n232\u2192- Tensegrity *lattices* and more complex architectures \u2014 exactly where hand\n233\u2192 assembly becomes infeasible and single-build co-fabrication pays off.\n234\u2192- Migration from the PLA\u2013TPU proxy toward flight-relevant materials and\n235\u2192 impact regimes.\n236\u2192- Safety hardware on Earth (e.g., the tensegrity crutch tip \u2014 future work).\n237\u2192- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238\u2192 handling, testing) in the loop.\n239\u2192\n240\u2192### Evidence / results\n241\u2192\n242\u2192*What we found \u2014 ground the conclusion in shown data*\n243\u2192\n244\u2192Build the talk around a quantitative three-slide sequence (per the\n245\u2192mock-audience report; insert observed values once the campaign completes):\n246\u2192\n247\u21921. **Campaign ledger** \u2014 \"The campaign used N specimens across B adaptive\n248\u2192 batches under a fixed physical-test budget\": initialization vs. adaptive\n249\u2192 batches, replicates, print failures, cycle time, and the numerical value\n250\u2192 (and rationale) of the force cap.\n251\u21922. **Budget-matched baseline** \u2014 \"Adaptive selection improved the feasible\n252\u2192 design set faster than [predeclared baseline]\": performance vs. number of\n253\u2192 physical tests against Sobol/random sampling at the same budget, with\n254\u2192 uncertainty where available.\n255\u21923. **Measured feasible Pareto front** \u2014 specific energy absorption vs.\n256\u2192 compaction efficiency among designs satisfying the force cap, with\n257\u2192 replicate spread, marked infeasible designs, and specimen photos or\n258\u2192 force\u2013time traces for representative Pareto designs.\n259\u2192\n260\u2192Supporting raw-data candidates: drop-test force\u2013time traces, Sobol /\n261\u2192first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262\u2192\n263\u2192### Suggested 15-minute allocation\n264\u2192\n265\u2192*(From the mock-audience report \u2014 results get the largest block.)*\n266\u2192\n267\u2192| Time | Content |\n268\u2192|---|---|\n269\u2192| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270\u2192| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271\u2192| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272\u2192| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273\u2192| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274\u2192| 1 min | Limitations and transfer conditions |\n275\u2192| 1 min | Measured conclusion + bookend |\n276\u2192\n277\u2192Move kernel choices, encodings, and other implementation detail to backup\n278\u2192slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279\u2192[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280\u2192\n281\u2192## Closing\n282\u2192\n283\u2192### Review\n284\u2192\n285\u2192*A recap of the body, leading into the conclusion*\n286\u2192\n287\u2192Using real, measured impact data from additively manufactured\n288\u2192tensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\n289\u2192optimization routine \u2014 we rapidly iterate toward better energy absorbers.\n290\u2192This feedback loop makes tensegrity optimization dramatically faster: dozens\n291\u2192of prints instead of the hundreds a traditional design of experiments over\n292\u2192this design space would require.\n293\u2192\n294\u2192### Conclusion\n295\u2192\n296\u2192*What the above means to the audience in the end*\n297\u2192\n298\u2192Because the loop runs on physical measurements, it is useful precisely where\n299\u2192model calibration is not worth its cost \u2014 and the same closed-loop pattern\n300\u2192should transfer (a conditional claim, pending evidence) to other additively\n301\u2192manufactured architectures whose performance is dominated by\n302\u2192hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303\u2192*use this loop when tests are authoritative, each test is costly, the design\n304\u2192space is mixed, and several performance goals conflict.* For tensegrity\n305\u2192specifically, it opens a path from this T3 proxy study toward lattices,\n306\u2192flight-relevant materials, payload protection on other planets, and safety\n307\u2192hardware here on Earth (e.g., the crutch tip, as future work).\n308\u2192\n309\u2192### Close\n310\u2192\n311\u2192*A way to end the presentation clearly and elegantly*\n312\u2192\n313\u2192Return to the opening image: the next planetary lander's energy absorber \u2014 a\n314\u2192structure that has to survive not one impact but many \u2014 doesn't have to take\n315\u2192years of hand-built prototypes; with this loop, candidate designs can be\n316\u2192designed, printed, and tested against real impacts in weeks instead of months\n317\u2192or longer. End on the measured engineering result and its transfer condition,\n318\u2192then the bookend: every method that compresses design time moves our ventures\n319\u2192\u2014 on this planet and beyond \u2014 forward.\n320\u2192\n321\u2192---\n322\u2192\n323\u2192## Changes from Draft 2\n324\u2192\n325\u2192Applied from @sgbaird's inline review comments on PR #84 and the\n326\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327\u2192\n328\u2192**From the inline review comments:**\n329\u2192\n330\u21921. **Reusability in the hook** (line 22): the tensegrity lander survives\n331\u2192 multiple drops, unlike crushable/sacrificial alternatives.\n332\u21922. **Proxy-system framing** (lines 49, 87): PLA\u2013TPU would never fly; it is a\n333\u2192 proxy for prototyping a self-driving-lab-style workflow \u2014 stated in a\n334\u2192 scope note up front and re-stated right after the hook.\n335\u21923. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336\u2192 heavy-duty simulation falls short of the experiments (millisecond-scale\n337\u2192 peak shock), not that it merely costs too much.\n338\u21924. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339\u2192 complex structures and especially lattices are not.\n340\u21925. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341\u2192 (temperature, print speed, \u2026) only; the rest is explicitly future work.\n342\u21926. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343\u2192 literature search (or validation of experiments against simulation), plus\n344\u2192 the millisecond-resolution peak-shock argument.\n345\u21927. **Optional Point 4 added** (line 97): future applications, with a note it\n346\u2192 can fold into the Conclusion if time is short.\n347\u21928. **Steel-materials point** (line 104): simulations fall short for the\n348\u2192 actual engineering materials (steel struts/cables), not just printed\n349\u2192 PLA\u2013TPU.\n350\u21929. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351\u2192 in minutes, pitched so the audience sees they can use it too.\n352\u219210. **\"Thousands\" \u2192 \"hundreds\"** (line 173): baseline is now a traditional\n353\u2192 design of experiments over a high-dimensional space (hundreds), in the\n354\u2192 main message, Need, Point 1, and Review.\n355\u219211. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356\u2192 or longer\" for candidate designs.\n357\u2192\n358\u2192**From the mock-audience report's priority revisions:**\n359\u2192\n360\u219212. **Quantitative three-slide results sequence** (Priority 1): campaign\n361\u2192 ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front, with a\n362\u2192 fallback main-message wording if the campaign can't yet support the\n363\u2192 efficiency claim.\n364\u219213. **Anti-simulation premise replaced with a scoped cost decision**\n365\u2192 (Priority 2): Point 1 retitled to the information-value message;\n366\u2192 \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367\u2192 anticipated with a backup slide.\n368\u219214. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369\u2192 implementation detail moved to backup slides; Q&A prep pointed at the\n370\u2192 report's claims-vs-evidence table.\n371\u219215. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372\u2192 the main talk, machinery in backup slides; the misleading\n373\u2192 \"qNEHVI over standard expected improvement\" comparison replaced with\n374\u2192 \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375\u219216. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376\u2192 sequence in Point 3, so the structure isn't remembered as a generic crush\n377\u2192 structure or lattice.\n378\u219217. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379\u2192 co-fabrication / no joining of parts,\" with a note to list remaining\n380\u2192 manual steps and an automation-status caveat.\n381\u219218. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382\u2192 compaction efficiency, constraint = force cap, deliverable = *feasible*\n383\u2192 Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384\u219219. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385\u2192 transfer claim marked conditional, and the close ends on the measured\n386\u2192 result before the bookend.\n387\u2192\n388\u2192## Changes from Draft 1\n389\u2192\n390\u2192Feedback from the PR #84 review, applied in Draft 2:\n391\u2192\n392\u21921. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n393\u2192 dozens-not-thousands claim); body and message now carry the same story.\n394\u21922. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n395\u2192 manuscript's positioning (#75/#76) is that the physical experiment is the\n396\u2192 source of truth, explicitly avoiding calibrated FE simulation.\n397\u21923. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n398\u2192 parametric design space and avoid an over-claim to a design-automation\n399\u2192 audience.\n400\u21924. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401\u2192 energy absorption and compaction efficiency, cap peak transmitted force.\n402\u21925. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403\u2192 *and* the space is too big to brute-force \u2014 which makes BO + AM the\n404\u2192 inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405\u2192 above.)*\n406\u21926. **Point 1 given its own content** (the *why* behind the inefficiency)\n407\u2192 instead of restating the Need.\n408\u21927. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409\u2192 the specifics (noise-aware acquisition, small batches, multi-objective).\n410\u21928. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411\u2192 (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412\u2192 above.)*\n413\u21929. **Evidence/results slot added** before the Closing, so the Conclusion is\n414\u2192 grounded in shown data rather than three hedged \"may\"s.\n415\u219210. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n416\u2192 Ball Bot visual; verified video links included.\n417\u219211. **Close bookends the opening** (the next lander's absorber, designed in\n418\u2192 weeks) before the broader humankind line.\n419\u219212. **Spelling**: Doumont (was \"Duomont\").", + "name": "read", + "tool_call_id": "call_93haurOMDGiH2mQFFbaMaYKn", + "info": { + "start_ts": 8147.331065886, + "end_ts": 8147.336467845 + } + }, + { + "role": "tool", + "content": " 1\u2192# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n 2\u2192\n 3\u2192Video: (assigned in #83)\n 4\u2192\n 5\u2192Transcribed from @me-madsen's handwritten notes\n 6\u2192([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7\u2192dated 2026-07-15).\n 8\u2192\n 9\u2192## Overview\n10\u2192\n11\u2192A presentation is all about the right content, displayed the right way, at the\n12\u2192right time.\n13\u2192\n14\u2192- Verbal communication is sequential.\n15\u2192- Message is *interpretation* of the information.\n16\u2192 - Ensure the audience can get the most out of what you're doing.\n17\u2192 - Don't just give them information \u2014 help them understand it, interpret it,\n18\u2192 and internalize it.\n19\u2192\n20\u2192## Doumont's suggested rules for a presentation\n21\u2192\n22\u21920. Ensure you have **messages** for your audience, not just words.\n23\u21921. **Adapt to your audience.**\n24\u2192 - Think like the audience, not the speaker.\n25\u21922. **Maximize signal-to-noise ratio.**\n26\u2192 - Reduce noise (animations, laser pointers, anything that pulls attention\n27\u2192 away from your message).\n28\u21923. **Use effective redundancy.**\n29\u2192 - Tell your message in word *and* on slide: someone deaf should understand\n30\u2192 your messages just as well as someone blind.\n31\u2192 - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32\u2192 date).\n33\u2192\n34\u2192## When creating slides\n35\u2192\n36\u2192- If the audience doesn't need it, why put it on the slides?\n37\u2192- People shouldn't have to count the items on a slide to know how many are\n38\u2192 there.\n39\u2192- Don't use your slides as your \"presentation notes.\"\n40\u2192 - Don't show your notes to the audience.\n41\u2192 - **A bad slide is worse than no slide at all.**\n42\u2192- **One message per slide** is a good rule of thumb.\n43\u2192 - Prioritize the *so what* (or *therefore what*) of your message.\n44\u2192- Make it easy to interpret the information on the slide:\n45\u2192 - 3\u20135 items at most.\n46\u2192 - Align the title left.\n47\u2192 - Do not clutter slides with repetitive or unnecessary information: logos,\n48\u2192 affiliation, date, slide numbers.\n49\u2192- Doumont's suggested workflow:\n50\u2192 1. **Start with the title.**\n51\u2192 - Use a full sentence in the title.\n52\u2192 - The title is the main message of the slide.\n53\u2192 - Make it easy to read: left-aligned, with intentional line breaks.\n54\u2192 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55\u2192- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56\u2192\n57\u2192## General preparation\n58\u2192\n59\u2192- Planning for audience, content, and structure matters more than materials\n60\u2192 (i.e., slides).\n61\u2192- Try using one sheet of paper for presentation preparation.\n62\u2192- It's most important that your presentation is:\n63\u2192 - well planned, structured, and delivered;\n64\u2192 - **if you don't have a lot of time to prepare, don't focus on making\n65\u2192 slides.**\n66\u2192- Practice the presentation a few times. Practice at least once *without* your\n67\u2192 slideshow!\n68\u2192- If doing a handout, keep it separate from the slides you're preparing.\n69\u2192- Impress the audience with your body.\n70\u2192 - Use gestures, not laser pointers.\n71\u2192 - \"Control\" the audience with your body \u2014 keep their attention on your\n72\u2192 presentation.\n73\u2192\n74\u2192## Impressions / what to apply to the IDETC presentation\n75\u2192\n76\u2192\n77\u2192\n78\u2192## Questions / things to discuss\n79\u2192\n80\u2192", + "name": "read", + "tool_call_id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "info": { + "start_ts": 8147.331133416, + "end_ts": 8147.338304015 + } + }, + { + "role": "tool", + "content": " 1\u2192\\documentclass[10pt]{article}\n 2\u2192\n 3\u2192% --- Packages ---\n 4\u2192\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5\u2192\\usepackage[hidelinks]{hyperref}\n 6\u2192\\usepackage{parskip}\n 7\u2192\\usepackage{graphicx}\n 8\u2192\\usepackage{caption}\n 9\u2192\\usepackage{titlesec}\n 10\u2192\\usepackage{wrapfig}\n 11\u2192\\usepackage[numbers,sort&compress]{natbib}\n 12\u2192\n 13\u2192% --- Formatting ---\n 14\u2192\\setlength{\\parskip}{0.2em}\n 15\u2192\\setlength{\\parindent}{0em}\n 16\u2192\\titleformat*{\\section}{\\large\\bfseries}\n 17\u2192\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18\u2192\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19\u2192\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20\u2192\\setlength{\\emergencystretch}{2em}\n 21\u2192\\setlength{\\intextsep}{0.2em}\n 22\u2192\\bibliographystyle{abbrvnat}\n 23\u2192\n 24\u2192\\begin{document}\n 25\u2192\n 26\u2192\\begin{center}\n 27\u2192{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28\u21923D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29\u2192\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30\u2192\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31\u2192\\end{center}\n 32\u2192\n 33\u2192\\vspace{0.4em}\n 34\u2192\n 35\u2192\\section*{Abstract}\n 36\u2192Tensegrity-inspired architectures---rigid struts suspended within a\n 37\u2192continuous flexible network---can exhibit tunable nonlinear\n 38\u2192force--displacement responses and favorable energy absorption per unit\n 39\u2192mass, motivating their use in lightweight energy-absorbing and protective\n 40\u2192structures\n 41\u2192\\citep{skelton2009tensegrity,pajunen2019design}.\n 42\u2192Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43\u2192struts and flexible (TPU) elements in a single build\n 44\u2192\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45\u2192space---strut geometry, tension-element cross-section, connectivity\n 46\u2192topology, and unit-cell tiling---is too large to explore by trial and\n 47\u2192error. We present a closed-loop experimental campaign that uses\n 48\u2192Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49\u2192to drive a design--print--test workflow operating directly on physical\n 50\u2192measurements, without relying on calibrated finite-element simulation\n 51\u2192for objective evaluation.\n 52\u2192\n 53\u2192\\section{Introduction and Motivation}\n 54\u2192\n 55\u2192\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56\u2192\\centering\n 57\u2192\\vspace{-0.4em}\n 58\u2192\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59\u2192\\captionsetup{font=small}\n 60\u2192\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61\u2192Candidate tensegrity-inspired unit cells are instantiated from a\n 62\u2192parameter vector, fabricated by multi-material FDM, and tested under\n 63\u2192quasi-static compression and drop-weight impact. Measured peak force,\n 64\u2192specific energy absorption (SEA), and compaction efficiency update a\n 65\u2192Gaussian-process surrogate that proposes the next batch of designs.}\n 66\u2192\\label{fig:overview}\n 67\u2192\\vspace{-0.4em}\n 68\u2192\\end{wrapfigure}\n 69\u2192\n 70\u2192Tensegrity structures combine compression-only members with a\n 71\u2192pre-tensioned tension network to achieve stiffness without rigid\n 72\u2192joinery~\\citep{skelton2009tensegrity}. Their\n 73\u2192nonlinear, often load-limiting force--displacement response makes them\n 74\u2192attractive for impact mitigation, packaging, and wearable protective\n 75\u2192gear~\\citep{pajunen2019design}. Idealized\n 76\u2192cable--strut tensegrities are difficult to manufacture at scale, but\n 77\u2192\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78\u21923D printing reproduce key tensegrity behaviors---including\n 79\u2192post-buckling stability and load-limiting force--displacement\n 80\u2192response~\\citep{pajunen2019design}---motivating our extension to\n 81\u2192multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82\u2192elements. PLA provides rigid struts; TPU provides flexible,\n 83\u2192rate-dependent tension elements whose viscoelasticity is expected to\n 84\u2192contribute hysteretic energy dissipation under impact loading,\n 85\u2192consistent with the energy-absorbing behavior reported for rigid--TPU\n 86\u2192multi-material prints~\\citep{khatri2024energy}.\n 87\u2192\n 88\u2192The challenge is that small geometric and topological changes can\n 89\u2192produce large changes in the measured response, and high-fidelity\n 90\u2192simulation of FDM specimens is computationally expensive and often\n 91\u2192struggles to capture physical defects such as interfacial slip and\n 92\u2192TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93\u2192trial-and-error sweeps over the joint design space are also infeasible:\n 94\u2192even modest parameter resolutions yield thousands of candidates, each\n 95\u2192requiring print and test time. We instead treat the physical\n 96\u2192experiment as the source of truth and use BO to select which specimens\n 97\u2192are most informative to fabricate next---an approach used in related\n 98\u2192closed-loop materials and structures\n 99\u2192campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100\u2192Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101\u2192set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102\u2192designs using parallel, noisy multi-objective BO driven by measured\n103\u2192$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104\u2192\n105\u2192\\section{Approach}\n106\u2192\\subsection{Closed-Loop Workflow}\n107\u2192Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108\u2192proceeds in four steps: (i)~a parameterized unit-cell design is\n109\u2192instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110\u2192is sliced and printed on a multi-material FDM system; (iii)~it is\n111\u2192tested under quasi-static compression and instrumented drop-weight\n112\u2192impact; and (iv)~the resulting performance metrics update a\n113\u2192Gaussian-process (GP) surrogate, which a batch acquisition function\n114\u2192queries to recommend the next set of candidates. Surrogate updating\n115\u2192and experiment selection are automated; specimen handling, slicing,\n116\u2192and test setup remain manual.\n117\u2192\n118\u2192\\subsection{Design Space}\n119\u2192We parameterize a family of unit cells using a core-wrapping\n120\u2192architecture in which rigid PLA struts are enclosed by continuous TPU\n121\u2192skins; the wrapping rationale follows multi-material FDM strategies in\n122\u2192which rigid panels are wrapped by stretchable soft material to prevent\n123\u2192delamination and improve interfacial bonding at the rigid--flexible\n124\u2192interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125\u2192comprises continuous geometric variables (strut diameter and length,\n126\u2192tension-element width and thickness), an integer strut count per unit\n127\u2192cell, and two categorical variables (connectivity topology, unit-cell\n128\u2192tiling pattern). Categorical choices are encoded explicitly rather\n129\u2192than embedded in a continuous metric, and bounds on the continuous\n130\u2192variables are chosen to remain within the printer's resolution and\n131\u2192overhang limits and to keep specimens in a common form factor for the\n132\u2192test fixtures.\n133\u2192\n134\u2192\\subsection{Surrogate and Acquisition}\n135\u2192The GP surrogate is trained on three measured performance metrics:\n136\u2192peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137\u2192(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138\u2192objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139\u2192likelihood with priors to avoid pathological length scales at small $n$.\n140\u2192Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141\u2192interfacial defects motivates our use of q-noisy expected hypervolume\n142\u2192improvement (qNEHVI), which explicitly accounts for observation noise\n143\u2192in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144\u2192expected-improvement variants. Print failures are treated as a\n145\u2192probabilistic feasibility constraint: the acquisition score for each\n146\u2192candidate is its qNEHVI improvement weighted by the GP-estimated\n147\u2192probability of successful fabrication, following recent constrained\n148\u2192multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149\u2192Where applicable, we use numerically stable log-space variants of\n150\u2192improvement-based acquisitions to mitigate vanishing-gradient\n151\u2192pathologies as data and constraints\n152\u2192accumulate~\\citep{ament2023logei}.\n153\u2192\n154\u2192\\section{Methods}\n155\u2192\\subsection{Fabrication}\n156\u2192Specimens are fabricated on a multi-material FDM system capable of\n157\u2192co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158\u2192Print parameters (temperature, line width, infill, retraction) are\n159\u2192held fixed within a batch to isolate the effect of the design\n160\u2192variables. Each specimen is weighed and dimensionally inspected prior\n161\u2192to testing so that mass and geometric deviations are available as\n162\u2192covariates.\n163\u2192\n164\u2192\\subsection{Mechanical Testing}\n165\u2192Quasi-static compression is performed on a screw-driven load frame at\n166\u2192a fixed nominal strain rate, with force--displacement recorded to\n167\u2192densification. Drop-weight impact testing uses an instrumented tup at\n168\u2192a fixed impact energy; transmitted force is captured at high sample\n169\u2192rate, and specimens are imaged before and after impact to document\n170\u2192failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171\u2192unit mass, and compaction efficiency $\\eta$, defined consistently\n172\u2192across the campaign. A control specimen from an earlier batch is\n173\u2192retested periodically to monitor TPU batch-to-batch and printer drift.\n174\u2192\n175\u2192\\section{Expected Outcomes and Discussion}\n176\u2192We will report how BO-selected designs evolve across iterations, how\n177\u2192GP surrogate predictions and uncertainties change as data accumulate,\n178\u2192and which Pareto-efficient trade-offs emerge between SEA and peak\n179\u2192transmitted force within the explored design space. We will also\n180\u2192discuss practical lessons for operating the closed-loop workflow,\n181\u2192including handling of print failures, batch-to-batch variability in\n182\u2192TPU response, the influence of the rigid--flexible interface on\n183\u2192energy absorption, and the exploration--exploitation balance of the\n184\u2192acquisition function under realistic experimental noise. We expect\n185\u2192the campaign to clarify where physical experimentation is genuinely\n186\u2192required versus where simpler analytical or multi-fidelity surrogate\n187\u2192shortcuts~\\citep{mo2023accelerated} could be\n188\u2192substituted in future work.\n189\u2192\n190\u2192\\section{Conclusion}\n191\u2192This work demonstrates a partially automated, experiment-driven\n192\u2192closed-loop pipeline for designing multi-material 3D-printed\n193\u2192tensegrity-inspired energy absorbers, with surrogate updating and\n194\u2192candidate selection automated and fabrication and testing kept manual.\n195\u2192By updating a GP surrogate directly from physical measurements, the\n196\u2192workflow avoids direct dependence on calibrated finite-element\n197\u2192simulation for objective evaluation while still providing principled,\n198\u2192multi-objective design recommendations. The same closed-loop pattern is\n199\u2192expected to transfer to other multi-material additively manufactured\n200\u2192architectures whose performance is dominated by hard-to-simulate\n201\u2192effects.\n202\u2192\n203\u2192{\\tiny\n204\u2192\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205\u2192\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206\u2192% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207\u2192% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208\u2192\\providecommand{\\doi}[1]{}\n209\u2192\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210\u2192\\bibliography{references}\n211\u2192}\n212\u2192\n213\u2192\\end{document}", + "name": "read", + "tool_call_id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "info": { + "start_ts": 8147.332633466, + "end_ts": 8147.339043095 + } + } + ], + "next_observation": [ + { + "role": "tool", + "content": "175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "info": { + "start_ts": 8153.825818486, + "end_ts": 8153.828437556 + } + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "time_elapsed_before_transition": 1.037811540999428, + "time_elapsed_agent_get_asv": 1.9872349249999388, + "time_elapsed_after_agent_get_asv": 0.9827260329993805, + "time_elapsed_env_step": 0.003144829000120808, + "time_elapsed_after_env_step": 0.8852970019997883 + }, + "action": { + "call_id": { + "run_id": "f8b11daf-a506-414f-887c-a22c881e34cd", + "fwd_id": "38d5e2c5-ee05-46de-9864-cfd697621798" + }, + "op_name": "IdentityOp-10b149563727", + "op_class_name": "ldp.graph.common_ops.IdentityOp", + "value": { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\", \"offset\": 174, \"limit\": 100}", + "name": "read" + } + } + ] + }, + "logprob": null + } + } + }, + "created_at": "2026-07-26T02:29:27.669571Z" + }, + { + "id": "fcf77b7b-0716-4299-918f-5263736ce658", + "trajectory_id": "afade0b1-f49e-4031-a86d-cb5ec4652536", + "trajectory_timestep": 0, + "step": "after_env_step", + "state": { + "transition": { + "timestep": 0, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ], + "info": null + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Tensegrity Slides \u2014 Draft 1 (extracted content)\n 2\u2192\n 3\u2192Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4\u2192\n 5\u2192\n 6\u2192## Slide 1 \u2014 layout: Title Only\n 7\u2192\n 8\u2192**On-slide text:**\n 9\u2192- **Title 1**: Let\u2019s build better tensegrity structures faster\n 10\u2192- **TextBox 4**: Marcus Madsen\n 11\u2192Undergraduate researcher under Sterling Baird, Ph.D.\n 12\u2192- **TextBox 6**: IDETC 2026\n 13\u2192\n 14\u2192**Non-text shapes / media / placeholders:**\n 15\u2192- [PICTURE (13)] Picture 3\n 16\u2192- [PICTURE (13)] Picture 8\n 17\u2192\n 18\u2192**Presenter notes:**\n 19\u2192> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20\u2192> \n 21\u2192> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22\u2192\n 23\u2192\n 24\u2192## Slide 2 \u2014 layout: Title Only\n 25\u2192\n 26\u2192**On-slide text:**\n 27\u2192- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings\u2014and to problems closer to home.\n 28\u2192\n 29\u2192**Non-text shapes / media / placeholders:**\n 30\u2192- [MEDIA (16)] Online Media 5\n 31\u2192\n 32\u2192**Presenter notes:**\n 33\u2192> Replace this video with a downloaded and trimmed version of just the fall. \n 34\u2192> \n 35\u2192> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle \u2014 thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander\u00a0is\u00a0the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit\u00a0and get back up\u00a0\u2014 it survives multiple drops and keeps working.\n 36\u2192\n 37\u2192\n 38\u2192## Slide 3 (HIDDEN) \u2014 layout: Title Only\n 39\u2192\n 40\u2192**On-slide text:**\n 41\u2192- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42\u2192- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43\u2192- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44\u2192\n 45\u2192**Presenter notes:**\n 46\u2192> This might be better explained purely vocally while on the previous slide.\n 47\u2192\n 48\u2192\n 49\u2192## Slide 4 \u2014 layout: Title Only\n 50\u2192\n 51\u2192**On-slide text:**\n 52\u2192- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53\u2192- **TextBox 3**: Iteration\n 54\u2192- **TextBox 5**: Building\n 55\u2192- **TextBox 7**: Testing\n 56\u2192\n 57\u2192**Non-text shapes / media / placeholders:**\n 58\u2192- [MEDIA (16)] AdobeStock_207717222\n 59\u2192- [MEDIA (16)] Online Media 2\n 60\u2192- [PICTURE (13)] Picture 14\n 61\u2192- [PICTURE (13)] Picture 16\n 62\u2192- [PICTURE (13)] Picture 18\n 63\u2192\n 64\u2192**Presenter notes:**\n 65\u2192> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn\u2019t become distracting because it\u2019s fun to watch it spin.\n 66\u2192> \n 67\u2192> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68\u2192\n 69\u2192\n 70\u2192## Slide 5 (HIDDEN) \u2014 layout: Title Only\n 71\u2192\n 72\u2192**On-slide text:**\n 73\u2192- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74\u2192- **TextBox 14**: Baird et al. Matter (2022)\n 75\u2192\n 76\u2192**Non-text shapes / media / placeholders:**\n 77\u2192- [GROUP (6)] Group 2\n 78\u2192\n 79\u2192**Presenter notes:**\n 80\u2192> Consult Sterling on this slide. I don\u2019t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it\u2019s well enough known that it\u2019d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81\u2192\n 82\u2192\n 83\u2192## Slide 6 (HIDDEN) \u2014 layout: Title Only\n 84\u2192\n 85\u2192**On-slide text:**\n 86\u2192- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87\u2192- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88\u2192\n 89\u2192**Presenter notes:**\n 90\u2192> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91\u2192\n 92\u2192\n 93\u2192## Slide 7 \u2014 layout: Title Only\n 94\u2192\n 95\u2192**On-slide text:**\n 96\u2192- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97\u2192- **TextBox 5**: 6 variables\n 98\u2192- **TextBox 7**: 2 types of input data\n 99\u2192- **TextBox 9**: 2 objectives\n100\u2192- **TextBox 17**: *image of blackbox*\n101\u2192\n102\u2192**Non-text shapes / media / placeholders:**\n103\u2192- [LINE (9)] Connector: Curved 11\n104\u2192- [LINE (9)] Connector: Curved 13\n105\u2192- [LINE (9)] Connector: Curved 15\n106\u2192\n107\u2192**Presenter notes:**\n108\u2192> Very rough slide as I\u2019m not entirely sure what graphic or text to put on this, partially because I\u2019m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109\u2192\n110\u2192\n111\u2192## Slide 8 \u2014 layout: Title Only\n112\u2192\n113\u2192**On-slide text:**\n114\u2192- **Title 1**: Multi-material additive manufacturing supplies that data fast \u2014 single-build co-fabrication, with no joining of parts.\n115\u2192\n116\u2192**Non-text shapes / media / placeholders:**\n117\u2192- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118\u2192- [PICTURE (13)] Picture 4\n119\u2192\n120\u2192**Presenter notes:**\n121\u2192> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122\u2192\n123\u2192\n124\u2192## Slide 9 \u2014 layout: Title Only\n125\u2192\n126\u2192**On-slide text:**\n127\u2192- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128\u2192- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129\u2192- **TextBox 5**: *placeholder for slo motion of drop test*\n130\u2192\n131\u2192\n132\u2192## Slide 10 \u2014 layout: Title Only\n133\u2192\n134\u2192**On-slide text:**\n135\u2192- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136\u2192- **TextBox 3**: Printing\n137\u2192- **TextBox 5**: Testing\n138\u2192- **TextBox 7**: BO feed\n139\u2192\n140\u2192**Non-text shapes / media / placeholders:**\n141\u2192- [PICTURE (13)] Picture 14\n142\u2192- [PICTURE (13)] Picture 16\n143\u2192- [PICTURE (13)] Picture 18\n144\u2192- [PICTURE (13)] Graphic 8\n145\u2192\n146\u2192**Presenter notes:**\n147\u2192> I think it might be better if we replaced \u201cprinting\u201d with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148\u2192\n149\u2192\n150\u2192## Slide 11 (HIDDEN) \u2014 layout: Title Only\n151\u2192\n152\u2192**On-slide text:**\n153\u2192- **Title 1**: Reserved for results\n154\u2192\n155\u2192**Presenter notes:**\n156\u2192> Would be helpful if we could show that this process works in this case.\n157\u2192\n158\u2192\n159\u2192## Slide 12 \u2014 layout: Title Only\n160\u2192\n161\u2192**On-slide text:**\n162\u2192- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163\u2192- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164\u2192- **TextBox 3**: *image to show lattice structure\n165\u2192\n166\u2192\n167\u2192## Slide 13 \u2014 layout: Title Only\n168\u2192\n169\u2192**Non-text shapes / media / placeholders:**\n170\u2192- [PICTURE (13)] Picture 3\n171\u2192\n172\u2192**Presenter notes:**\n173\u2192> Slide for questions at the end\n174\u2192\n175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "info": { + "start_ts": 8147.330900226, + "end_ts": 8147.337548875 + } + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Presentation \u2014 Doumont Presentation Structure Template\n 2\u2192\n 3\u2192Transcribed from Jean-luc Doumont's presentation-structure template\n 4\u2192([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5\u2192adapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\n 6\u2192All rights reserved. Can be downloaded from .\n 7\u2192\n 8\u2192> To design your presentation, write down your ideas for each component below.\n 9\u2192> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10\u2192\n 11\u2192**Draft 3** \u2014 applies the inline review comments from @sgbaird on PR #84 and the\n 12\u2192priority revisions from the\n 13\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14\u2192Summaries of what changed are at the bottom of this file\n 15\u2192([Draft 2 \u2192 3](#changes-from-draft-2), [Draft 1 \u2192 2](#changes-from-draft-1)).\n 16\u2192\n 17\u2192**Scope note (frame everything below with this):** the study presented is the\n 18\u2192optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19\u2192processing parameters** (e.g., nozzle temperature, print speed). PLA\u2013TPU is a\n 20\u2192**proxy system** \u2014 not flight material \u2014 used to prototype a self-driving-lab\n 21\u2192style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22\u2192The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23\u2192beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24\u2192\n 25\u2192## Opening\n 26\u2192\n 27\u2192### Attention getter\n 28\u2192\n 29\u2192*A way to lead the audience to the need efficiently*\n 30\u2192\n 31\u2192Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32\u2192landings \u2014 and to problems closer to home.\n 33\u2192\n 34\u2192One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35\u2192clip) do the work:\n 36\u2192\n 37\u2192> Landing a payload on another planet means surviving an impact in conditions\n 38\u2192> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n 39\u2192> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40\u2192> structure: the lander *is* the energy absorber. And unlike crushable\n 41\u2192> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42\u2192> hit **and get back up** \u2014 it survives multiple drops and keeps working.\n 43\u2192\n 44\u2192Candidate Super Ball Bot videos to show (verified links):\n 45\u2192\n 46\u2192- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n 47\u2192 (official channel); the canonical overview.\n 48\u2192- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49\u2192 \u2014 Wall Street Journal; prototype drop footage.\n 50\u2192- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51\u2192 \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n 52\u2192\n 53\u2192Immediately after the hook, state the present study scope in one sentence\n 54\u2192(per the scope note above): *we study a printed PLA\u2013TPU T3 prism as a proxy\n 55\u2192system for prototyping an autonomous tensegrity-optimization workflow* \u2014 so\n 56\u2192the planetary framing never outruns the demonstrated scope.\n 57\u2192\n 58\u2192\n 60\u2192\n 61\u2192### Need\n 62\u2192\n 63\u2192*A difference between actual and desired situations*\n 64\u2192\n 65\u2192**Actual situation** (a two-sided pincer \u2014 stated as a scoped engineering\n 66\u2192judgment, not an absolute):\n 67\u2192\n 68\u2192- For these structures, obtaining trustworthy objective values from a\n 69\u2192 validated high-fidelity model would require substantial calibration of\n 70\u2192 interface, rate, contact, and defect behavior \u2014 and even heavy-duty\n 71\u2192 simulation falls short of the experiments we are running: peak transmitted\n 72\u2192 shock is a millisecond-scale transient that is notoriously difficult to\n 73\u2192 resolve numerically. This holds even for idealized cable\u2013strut tensegrities\n 74\u2192 in their actual engineering materials (steel or similar), not just for\n 75\u2192 printed PLA\u2013TPU.\n 76\u2192 \n 79\u2192- Exhaustive physical search fails on cost: a traditional design of\n 80\u2192 experiments over this relatively high-dimensional space (geometry + FDM\n 81\u2192 process parameters) would demand hundreds of specimens, each costing print\n 82\u2192 and test time.\n 83\u2192- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84\u2192 more complex structures \u2014 especially tensegrity *lattices* \u2014 become\n 85\u2192 extremely difficult and ultimately infeasible to hand-build.\n 86\u2192\n 87\u2192**Desired situation:**\n 88\u2192\n 89\u2192- Trustworthy performance data without hand assembly (easy creation of\n 90\u2192 tensegrity structures, scalable beyond a single prism).\n 91\u2192- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n 92\u2192 not the hundreds a traditional design of experiments would require.\n 93\u2192\n 94\u2192### Task\n 95\u2192\n 96\u2192*What I decided/was asked to do to address the need*\n 97\u2192\n 98\u2192Build a closed-loop design\u2013print\u2013test workflow that optimizes a **T3-prism\n 99\u2192tensegrity-inspired energy absorber together with its FDM processing\n100\u2192parameters** (e.g., temperature, print speed) directly from real, measured\n101\u2192impact data \u2014 maximizing energy absorbed per gram (specific energy\n102\u2192absorption) and compaction efficiency, subject to a cap on the peak force\n103\u2192transmitted to the payload. Extensions beyond the T3 prism are future work.\n104\u2192\n105\u2192### Main message\n106\u2192\n107\u2192*The one sentence I want my audience to remember*\n108\u2192\n109\u2192By closing the loop between multi-material 3D printing and Bayesian\n110\u2192optimization, we can optimize tensegrity-inspired energy absorbers directly\n111\u2192from real impact data \u2014 in dozens of prints, not the hundreds a traditional\n112\u2192design of experiments would demand.\n113\u2192\n114\u2192\n118\u2192\n119\u2192### Preview\n120\u2192\n121\u2192*A map of the body (ideally three points, max. five)*\n122\u2192\n123\u21921. When model calibration and exhaustive testing are both costly, each\n124\u2192 physical specimen must be chosen for its information value \u2014 so the\n125\u2192 measured experiment is our source of truth.\n126\u21922. Bayesian optimization makes physical experimentation affordable: it handles\n127\u2192 noisy measurements, small batches, and the constrained multi-objective\n128\u2192 trade-off \u2014 and Honegumi let us scaffold it in minutes.\n129\u21923. Multi-material additive manufacturing closes the loop: single-build PLA\u2013TPU\n130\u2192 co-fabrication turns each proposed design into test data in hours, with no\n131\u2192 joining of parts.\n132\u21924. *(Optional fourth point, if time allows)* The same closed loop points at\n133\u2192 what comes next: tensegrity lattices, flight-relevant materials, and\n134\u2192 safety hardware on Earth.\n135\u2192\n136\u2192## Body\n137\u2192\n138\u2192### Point 1\n139\u2192\n140\u2192When model calibration and exhaustive testing are both costly, each physical\n141\u2192specimen must be chosen for its information value.\n142\u2192\n143\u2192- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144\u2192 these structures, a sufficiently calibrated multiphysics model is expensive\n145\u2192 to build relative to the design campaign itself \u2014 and the hardest quantity,\n146\u2192 peak transmitted shock, is a millisecond-scale transient that even\n147\u2192 heavy-duty simulation struggles to resolve. The gap persists in the actual\n148\u2192 engineering materials a real lander would use (steel struts and cables),\n149\u2192 so this is not merely an artifact of printed PLA\u2013TPU.\n150\u2192- The alternative \u2014 sweeping the design space physically \u2014 fails on cost: a\n151\u2192 traditional design of experiments over geometry plus FDM process parameters\n152\u2192 would take hundreds of specimens, each needing print + test time.\n153\u2192- Therefore: treat the physical experiment as the authoritative source of\n154\u2192 objective values, and be ruthless about *which* experiments to run.\n155\u2192- Anticipate the FEA-veteran pushback (\"inconvenient \u2260 untrustworthy\"): frame\n156\u2192 it as a cost-and-dependence decision, and keep a backup slide with the\n157\u2192 literature (or our own validation) on simulation\u2013experiment discrepancy for\n158\u2192 impact transients.\n159\u2192\n160\u2192### Transition\n161\u2192\n162\u2192If every data point must be a real experiment, we need a method that extracts\n163\u2192the most from every specimen \u2014 we need to make the process radically more\n164\u2192sample-efficient.\n165\u2192\n166\u2192### Point 2\n167\u2192\n168\u2192Bayesian optimization makes physical experimentation affordable, even with\n169\u2192limited, noisy data.\n170\u2192\n171\u2192- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172\u2192 visual in the main talk; implementation detail goes to backup slides.\n173\u2192 Spoken version:\n174\u2192 > \"After each batch, the model estimates both expected performance and\n175\u2192 > uncertainty across the design space. The acquisition rule selects a small\n176\u2192 > next batch that is most likely to expand the feasible trade-off frontier,\n177\u2192 > while accounting for noisy tests and print failures.\"\n178\u2192- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179\u2192 motivate a noise-aware acquisition function (qNEHVI) rather than\n180\u2192 non-noise-aware alternatives. (Backup slide: how observation noise is\n181\u2192 actually modeled \u2014 replicates, noise identification \u2014 and budget-matched\n182\u2192 baselines such as Sobol/random sampling.)\n183\u2192- Small parallel batches: the surrogate proposes the next batch of specimens\n184\u2192 to print, balancing exploration and exploitation.\n185\u2192- Constrained multi-objective by construction: **objectives** = maximize\n186\u2192 specific energy absorption and compaction efficiency; **constraint** = peak\n187\u2192 transmitted force must stay under a cap; the deliverable is the *feasible*\n188\u2192 Pareto front between the two objectives among force-cap-satisfying designs.\n189\u2192 (Keep this formulation consistent everywhere \u2014 do not present \"SEA vs. peak\n190\u2192 force\" as the trade-off.)\n191\u2192- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192\u2192 this advanced task (noisy, batched, constrained, multi-objective) in\n193\u2192 minutes using [Honegumi](https://honegumi.readthedocs.io/) \u2014 the audience\n194\u2192 can do the same for their own problems; show the code-template picker\n195\u2192 briefly so they see how low the barrier is.\n196\u2192\n197\u2192### Transition\n198\u2192\n199\u2192The optimizer is no longer the bottleneck \u2014 the limit is now providing real\n200\u2192data quickly enough.\n201\u2192\n202\u2192### Point 3\n203\u2192\n204\u2192Multi-material additive manufacturing supplies that data fast \u2014 single-build\n205\u2192co-fabrication, with no joining of parts.\n206\u2192\n207\u2192- Single-build PLA\u2013TPU co-fabrication: rigid struts and flexible tension\n208\u2192 network in one print \u2014 no joining or hand assembly of members, which closes\n209\u2192 the \"easy creation\" gap from the Need and is what makes future lattices\n210\u2192 feasible at all. (Be precise: if support removal, trimming, or conditioning\n211\u2192 remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212\u2192 assembly.\")\n213\u2192- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214\u2192 as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215\u2192 pre-tension status) plus a short deformation sequence \u2014 so the EDL engineer\n216\u2192 doesn't file it as a \"crush structure\" and the grad student can tell it from\n217\u2192 an ordinary lattice.\n218\u2192- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219\u2192 tested specimen (quasi-static compression + instrumented drop-weight impact)\n220\u2192 in hours. (Backup slide: representative print-to-result time breakdown and\n221\u2192 which steps remain manual \u2014 candidate selection is automated; slicing,\n222\u2192 handling, and testing are not yet.)\n223\u2192- Now we have the data we need to complete the loop.\n224\u2192\n225\u2192### Point 4 (optional \u2014 future applications)\n226\u2192\n227\u2192*Include if timing allows; otherwise fold into the Conclusion.*\n228\u2192\n229\u2192The same closed loop transfers wherever tests are authoritative, each test is\n230\u2192costly, the design space is mixed, and several performance goals conflict:\n231\u2192\n232\u2192- Tensegrity *lattices* and more complex architectures \u2014 exactly where hand\n233\u2192 assembly becomes infeasible and single-build co-fabrication pays off.\n234\u2192- Migration from the PLA\u2013TPU proxy toward flight-relevant materials and\n235\u2192 impact regimes.\n236\u2192- Safety hardware on Earth (e.g., the tensegrity crutch tip \u2014 future work).\n237\u2192- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238\u2192 handling, testing) in the loop.\n239\u2192\n240\u2192### Evidence / results\n241\u2192\n242\u2192*What we found \u2014 ground the conclusion in shown data*\n243\u2192\n244\u2192Build the talk around a quantitative three-slide sequence (per the\n245\u2192mock-audience report; insert observed values once the campaign completes):\n246\u2192\n247\u21921. **Campaign ledger** \u2014 \"The campaign used N specimens across B adaptive\n248\u2192 batches under a fixed physical-test budget\": initialization vs. adaptive\n249\u2192 batches, replicates, print failures, cycle time, and the numerical value\n250\u2192 (and rationale) of the force cap.\n251\u21922. **Budget-matched baseline** \u2014 \"Adaptive selection improved the feasible\n252\u2192 design set faster than [predeclared baseline]\": performance vs. number of\n253\u2192 physical tests against Sobol/random sampling at the same budget, with\n254\u2192 uncertainty where available.\n255\u21923. **Measured feasible Pareto front** \u2014 specific energy absorption vs.\n256\u2192 compaction efficiency among designs satisfying the force cap, with\n257\u2192 replicate spread, marked infeasible designs, and specimen photos or\n258\u2192 force\u2013time traces for representative Pareto designs.\n259\u2192\n260\u2192Supporting raw-data candidates: drop-test force\u2013time traces, Sobol /\n261\u2192first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262\u2192\n263\u2192### Suggested 15-minute allocation\n264\u2192\n265\u2192*(From the mock-audience report \u2014 results get the largest block.)*\n266\u2192\n267\u2192| Time | Content |\n268\u2192|---|---|\n269\u2192| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270\u2192| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271\u2192| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272\u2192| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273\u2192| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274\u2192| 1 min | Limitations and transfer conditions |\n275\u2192| 1 min | Measured conclusion + bookend |\n276\u2192\n277\u2192Move kernel choices, encodings, and other implementation detail to backup\n278\u2192slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279\u2192[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280\u2192\n281\u2192## Closing\n282\u2192\n283\u2192### Review\n284\u2192\n285\u2192*A recap of the body, leading into the conclusion*\n286\u2192\n287\u2192Using real, measured impact data from additively manufactured\n288\u2192tensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\n289\u2192optimization routine \u2014 we rapidly iterate toward better energy absorbers.\n290\u2192This feedback loop makes tensegrity optimization dramatically faster: dozens\n291\u2192of prints instead of the hundreds a traditional design of experiments over\n292\u2192this design space would require.\n293\u2192\n294\u2192### Conclusion\n295\u2192\n296\u2192*What the above means to the audience in the end*\n297\u2192\n298\u2192Because the loop runs on physical measurements, it is useful precisely where\n299\u2192model calibration is not worth its cost \u2014 and the same closed-loop pattern\n300\u2192should transfer (a conditional claim, pending evidence) to other additively\n301\u2192manufactured architectures whose performance is dominated by\n302\u2192hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303\u2192*use this loop when tests are authoritative, each test is costly, the design\n304\u2192space is mixed, and several performance goals conflict.* For tensegrity\n305\u2192specifically, it opens a path from this T3 proxy study toward lattices,\n306\u2192flight-relevant materials, payload protection on other planets, and safety\n307\u2192hardware here on Earth (e.g., the crutch tip, as future work).\n308\u2192\n309\u2192### Close\n310\u2192\n311\u2192*A way to end the presentation clearly and elegantly*\n312\u2192\n313\u2192Return to the opening image: the next planetary lander's energy absorber \u2014 a\n314\u2192structure that has to survive not one impact but many \u2014 doesn't have to take\n315\u2192years of hand-built prototypes; with this loop, candidate designs can be\n316\u2192designed, printed, and tested against real impacts in weeks instead of months\n317\u2192or longer. End on the measured engineering result and its transfer condition,\n318\u2192then the bookend: every method that compresses design time moves our ventures\n319\u2192\u2014 on this planet and beyond \u2014 forward.\n320\u2192\n321\u2192---\n322\u2192\n323\u2192## Changes from Draft 2\n324\u2192\n325\u2192Applied from @sgbaird's inline review comments on PR #84 and the\n326\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327\u2192\n328\u2192**From the inline review comments:**\n329\u2192\n330\u21921. **Reusability in the hook** (line 22): the tensegrity lander survives\n331\u2192 multiple drops, unlike crushable/sacrificial alternatives.\n332\u21922. **Proxy-system framing** (lines 49, 87): PLA\u2013TPU would never fly; it is a\n333\u2192 proxy for prototyping a self-driving-lab-style workflow \u2014 stated in a\n334\u2192 scope note up front and re-stated right after the hook.\n335\u21923. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336\u2192 heavy-duty simulation falls short of the experiments (millisecond-scale\n337\u2192 peak shock), not that it merely costs too much.\n338\u21924. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339\u2192 complex structures and especially lattices are not.\n340\u21925. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341\u2192 (temperature, print speed, \u2026) only; the rest is explicitly future work.\n342\u21926. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343\u2192 literature search (or validation of experiments against simulation), plus\n344\u2192 the millisecond-resolution peak-shock argument.\n345\u21927. **Optional Point 4 added** (line 97): future applications, with a note it\n346\u2192 can fold into the Conclusion if time is short.\n347\u21928. **Steel-materials point** (line 104): simulations fall short for the\n348\u2192 actual engineering materials (steel struts/cables), not just printed\n349\u2192 PLA\u2013TPU.\n350\u21929. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351\u2192 in minutes, pitched so the audience sees they can use it too.\n352\u219210. **\"Thousands\" \u2192 \"hundreds\"** (line 173): baseline is now a traditional\n353\u2192 design of experiments over a high-dimensional space (hundreds), in the\n354\u2192 main message, Need, Point 1, and Review.\n355\u219211. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356\u2192 or longer\" for candidate designs.\n357\u2192\n358\u2192**From the mock-audience report's priority revisions:**\n359\u2192\n360\u219212. **Quantitative three-slide results sequence** (Priority 1): campaign\n361\u2192 ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front, with a\n362\u2192 fallback main-message wording if the campaign can't yet support the\n363\u2192 efficiency claim.\n364\u219213. **Anti-simulation premise replaced with a scoped cost decision**\n365\u2192 (Priority 2): Point 1 retitled to the information-value message;\n366\u2192 \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367\u2192 anticipated with a backup slide.\n368\u219214. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369\u2192 implementation detail moved to backup slides; Q&A prep pointed at the\n370\u2192 report's claims-vs-evidence table.\n371\u219215. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372\u2192 the main talk, machinery in backup slides; the misleading\n373\u2192 \"qNEHVI over standard expected improvement\" comparison replaced with\n374\u2192 \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375\u219216. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376\u2192 sequence in Point 3, so the structure isn't remembered as a generic crush\n377\u2192 structure or lattice.\n378\u219217. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379\u2192 co-fabrication / no joining of parts,\" with a note to list remaining\n380\u2192 manual steps and an automation-status caveat.\n381\u219218. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382\u2192 compaction efficiency, constraint = force cap, deliverable = *feasible*\n383\u2192 Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384\u219219. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385\u2192 transfer claim marked conditional, and the close ends on the measured\n386\u2192 result before the bookend.\n387\u2192\n388\u2192## Changes from Draft 1\n389\u2192\n390\u2192Feedback from the PR #84 review, applied in Draft 2:\n391\u2192\n392\u21921. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n393\u2192 dozens-not-thousands claim); body and message now carry the same story.\n394\u21922. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n395\u2192 manuscript's positioning (#75/#76) is that the physical experiment is the\n396\u2192 source of truth, explicitly avoiding calibrated FE simulation.\n397\u21923. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n398\u2192 parametric design space and avoid an over-claim to a design-automation\n399\u2192 audience.\n400\u21924. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401\u2192 energy absorption and compaction efficiency, cap peak transmitted force.\n402\u21925. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403\u2192 *and* the space is too big to brute-force \u2014 which makes BO + AM the\n404\u2192 inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405\u2192 above.)*\n406\u21926. **Point 1 given its own content** (the *why* behind the inefficiency)\n407\u2192 instead of restating the Need.\n408\u21927. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409\u2192 the specifics (noise-aware acquisition, small batches, multi-objective).\n410\u21928. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411\u2192 (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412\u2192 above.)*\n413\u21929. **Evidence/results slot added** before the Closing, so the Conclusion is\n414\u2192 grounded in shown data rather than three hedged \"may\"s.\n415\u219210. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n416\u2192 Ball Bot visual; verified video links included.\n417\u219211. **Close bookends the opening** (the next lander's absorber, designed in\n418\u2192 weeks) before the broader humankind line.\n419\u219212. **Spelling**: Doumont (was \"Duomont\").", + "name": "read", + "tool_call_id": "call_93haurOMDGiH2mQFFbaMaYKn", + "info": { + "start_ts": 8147.331065886, + "end_ts": 8147.336467845 + } + }, + { + "role": "tool", + "content": " 1\u2192# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n 2\u2192\n 3\u2192Video: (assigned in #83)\n 4\u2192\n 5\u2192Transcribed from @me-madsen's handwritten notes\n 6\u2192([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7\u2192dated 2026-07-15).\n 8\u2192\n 9\u2192## Overview\n10\u2192\n11\u2192A presentation is all about the right content, displayed the right way, at the\n12\u2192right time.\n13\u2192\n14\u2192- Verbal communication is sequential.\n15\u2192- Message is *interpretation* of the information.\n16\u2192 - Ensure the audience can get the most out of what you're doing.\n17\u2192 - Don't just give them information \u2014 help them understand it, interpret it,\n18\u2192 and internalize it.\n19\u2192\n20\u2192## Doumont's suggested rules for a presentation\n21\u2192\n22\u21920. Ensure you have **messages** for your audience, not just words.\n23\u21921. **Adapt to your audience.**\n24\u2192 - Think like the audience, not the speaker.\n25\u21922. **Maximize signal-to-noise ratio.**\n26\u2192 - Reduce noise (animations, laser pointers, anything that pulls attention\n27\u2192 away from your message).\n28\u21923. **Use effective redundancy.**\n29\u2192 - Tell your message in word *and* on slide: someone deaf should understand\n30\u2192 your messages just as well as someone blind.\n31\u2192 - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32\u2192 date).\n33\u2192\n34\u2192## When creating slides\n35\u2192\n36\u2192- If the audience doesn't need it, why put it on the slides?\n37\u2192- People shouldn't have to count the items on a slide to know how many are\n38\u2192 there.\n39\u2192- Don't use your slides as your \"presentation notes.\"\n40\u2192 - Don't show your notes to the audience.\n41\u2192 - **A bad slide is worse than no slide at all.**\n42\u2192- **One message per slide** is a good rule of thumb.\n43\u2192 - Prioritize the *so what* (or *therefore what*) of your message.\n44\u2192- Make it easy to interpret the information on the slide:\n45\u2192 - 3\u20135 items at most.\n46\u2192 - Align the title left.\n47\u2192 - Do not clutter slides with repetitive or unnecessary information: logos,\n48\u2192 affiliation, date, slide numbers.\n49\u2192- Doumont's suggested workflow:\n50\u2192 1. **Start with the title.**\n51\u2192 - Use a full sentence in the title.\n52\u2192 - The title is the main message of the slide.\n53\u2192 - Make it easy to read: left-aligned, with intentional line breaks.\n54\u2192 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55\u2192- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56\u2192\n57\u2192## General preparation\n58\u2192\n59\u2192- Planning for audience, content, and structure matters more than materials\n60\u2192 (i.e., slides).\n61\u2192- Try using one sheet of paper for presentation preparation.\n62\u2192- It's most important that your presentation is:\n63\u2192 - well planned, structured, and delivered;\n64\u2192 - **if you don't have a lot of time to prepare, don't focus on making\n65\u2192 slides.**\n66\u2192- Practice the presentation a few times. Practice at least once *without* your\n67\u2192 slideshow!\n68\u2192- If doing a handout, keep it separate from the slides you're preparing.\n69\u2192- Impress the audience with your body.\n70\u2192 - Use gestures, not laser pointers.\n71\u2192 - \"Control\" the audience with your body \u2014 keep their attention on your\n72\u2192 presentation.\n73\u2192\n74\u2192## Impressions / what to apply to the IDETC presentation\n75\u2192\n76\u2192\n77\u2192\n78\u2192## Questions / things to discuss\n79\u2192\n80\u2192", + "name": "read", + "tool_call_id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "info": { + "start_ts": 8147.331133416, + "end_ts": 8147.338304015 + } + }, + { + "role": "tool", + "content": " 1\u2192\\documentclass[10pt]{article}\n 2\u2192\n 3\u2192% --- Packages ---\n 4\u2192\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5\u2192\\usepackage[hidelinks]{hyperref}\n 6\u2192\\usepackage{parskip}\n 7\u2192\\usepackage{graphicx}\n 8\u2192\\usepackage{caption}\n 9\u2192\\usepackage{titlesec}\n 10\u2192\\usepackage{wrapfig}\n 11\u2192\\usepackage[numbers,sort&compress]{natbib}\n 12\u2192\n 13\u2192% --- Formatting ---\n 14\u2192\\setlength{\\parskip}{0.2em}\n 15\u2192\\setlength{\\parindent}{0em}\n 16\u2192\\titleformat*{\\section}{\\large\\bfseries}\n 17\u2192\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18\u2192\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19\u2192\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20\u2192\\setlength{\\emergencystretch}{2em}\n 21\u2192\\setlength{\\intextsep}{0.2em}\n 22\u2192\\bibliographystyle{abbrvnat}\n 23\u2192\n 24\u2192\\begin{document}\n 25\u2192\n 26\u2192\\begin{center}\n 27\u2192{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28\u21923D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29\u2192\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30\u2192\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31\u2192\\end{center}\n 32\u2192\n 33\u2192\\vspace{0.4em}\n 34\u2192\n 35\u2192\\section*{Abstract}\n 36\u2192Tensegrity-inspired architectures---rigid struts suspended within a\n 37\u2192continuous flexible network---can exhibit tunable nonlinear\n 38\u2192force--displacement responses and favorable energy absorption per unit\n 39\u2192mass, motivating their use in lightweight energy-absorbing and protective\n 40\u2192structures\n 41\u2192\\citep{skelton2009tensegrity,pajunen2019design}.\n 42\u2192Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43\u2192struts and flexible (TPU) elements in a single build\n 44\u2192\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45\u2192space---strut geometry, tension-element cross-section, connectivity\n 46\u2192topology, and unit-cell tiling---is too large to explore by trial and\n 47\u2192error. We present a closed-loop experimental campaign that uses\n 48\u2192Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49\u2192to drive a design--print--test workflow operating directly on physical\n 50\u2192measurements, without relying on calibrated finite-element simulation\n 51\u2192for objective evaluation.\n 52\u2192\n 53\u2192\\section{Introduction and Motivation}\n 54\u2192\n 55\u2192\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56\u2192\\centering\n 57\u2192\\vspace{-0.4em}\n 58\u2192\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59\u2192\\captionsetup{font=small}\n 60\u2192\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61\u2192Candidate tensegrity-inspired unit cells are instantiated from a\n 62\u2192parameter vector, fabricated by multi-material FDM, and tested under\n 63\u2192quasi-static compression and drop-weight impact. Measured peak force,\n 64\u2192specific energy absorption (SEA), and compaction efficiency update a\n 65\u2192Gaussian-process surrogate that proposes the next batch of designs.}\n 66\u2192\\label{fig:overview}\n 67\u2192\\vspace{-0.4em}\n 68\u2192\\end{wrapfigure}\n 69\u2192\n 70\u2192Tensegrity structures combine compression-only members with a\n 71\u2192pre-tensioned tension network to achieve stiffness without rigid\n 72\u2192joinery~\\citep{skelton2009tensegrity}. Their\n 73\u2192nonlinear, often load-limiting force--displacement response makes them\n 74\u2192attractive for impact mitigation, packaging, and wearable protective\n 75\u2192gear~\\citep{pajunen2019design}. Idealized\n 76\u2192cable--strut tensegrities are difficult to manufacture at scale, but\n 77\u2192\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78\u21923D printing reproduce key tensegrity behaviors---including\n 79\u2192post-buckling stability and load-limiting force--displacement\n 80\u2192response~\\citep{pajunen2019design}---motivating our extension to\n 81\u2192multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82\u2192elements. PLA provides rigid struts; TPU provides flexible,\n 83\u2192rate-dependent tension elements whose viscoelasticity is expected to\n 84\u2192contribute hysteretic energy dissipation under impact loading,\n 85\u2192consistent with the energy-absorbing behavior reported for rigid--TPU\n 86\u2192multi-material prints~\\citep{khatri2024energy}.\n 87\u2192\n 88\u2192The challenge is that small geometric and topological changes can\n 89\u2192produce large changes in the measured response, and high-fidelity\n 90\u2192simulation of FDM specimens is computationally expensive and often\n 91\u2192struggles to capture physical defects such as interfacial slip and\n 92\u2192TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93\u2192trial-and-error sweeps over the joint design space are also infeasible:\n 94\u2192even modest parameter resolutions yield thousands of candidates, each\n 95\u2192requiring print and test time. We instead treat the physical\n 96\u2192experiment as the source of truth and use BO to select which specimens\n 97\u2192are most informative to fabricate next---an approach used in related\n 98\u2192closed-loop materials and structures\n 99\u2192campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100\u2192Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101\u2192set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102\u2192designs using parallel, noisy multi-objective BO driven by measured\n103\u2192$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104\u2192\n105\u2192\\section{Approach}\n106\u2192\\subsection{Closed-Loop Workflow}\n107\u2192Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108\u2192proceeds in four steps: (i)~a parameterized unit-cell design is\n109\u2192instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110\u2192is sliced and printed on a multi-material FDM system; (iii)~it is\n111\u2192tested under quasi-static compression and instrumented drop-weight\n112\u2192impact; and (iv)~the resulting performance metrics update a\n113\u2192Gaussian-process (GP) surrogate, which a batch acquisition function\n114\u2192queries to recommend the next set of candidates. Surrogate updating\n115\u2192and experiment selection are automated; specimen handling, slicing,\n116\u2192and test setup remain manual.\n117\u2192\n118\u2192\\subsection{Design Space}\n119\u2192We parameterize a family of unit cells using a core-wrapping\n120\u2192architecture in which rigid PLA struts are enclosed by continuous TPU\n121\u2192skins; the wrapping rationale follows multi-material FDM strategies in\n122\u2192which rigid panels are wrapped by stretchable soft material to prevent\n123\u2192delamination and improve interfacial bonding at the rigid--flexible\n124\u2192interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125\u2192comprises continuous geometric variables (strut diameter and length,\n126\u2192tension-element width and thickness), an integer strut count per unit\n127\u2192cell, and two categorical variables (connectivity topology, unit-cell\n128\u2192tiling pattern). Categorical choices are encoded explicitly rather\n129\u2192than embedded in a continuous metric, and bounds on the continuous\n130\u2192variables are chosen to remain within the printer's resolution and\n131\u2192overhang limits and to keep specimens in a common form factor for the\n132\u2192test fixtures.\n133\u2192\n134\u2192\\subsection{Surrogate and Acquisition}\n135\u2192The GP surrogate is trained on three measured performance metrics:\n136\u2192peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137\u2192(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138\u2192objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139\u2192likelihood with priors to avoid pathological length scales at small $n$.\n140\u2192Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141\u2192interfacial defects motivates our use of q-noisy expected hypervolume\n142\u2192improvement (qNEHVI), which explicitly accounts for observation noise\n143\u2192in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144\u2192expected-improvement variants. Print failures are treated as a\n145\u2192probabilistic feasibility constraint: the acquisition score for each\n146\u2192candidate is its qNEHVI improvement weighted by the GP-estimated\n147\u2192probability of successful fabrication, following recent constrained\n148\u2192multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149\u2192Where applicable, we use numerically stable log-space variants of\n150\u2192improvement-based acquisitions to mitigate vanishing-gradient\n151\u2192pathologies as data and constraints\n152\u2192accumulate~\\citep{ament2023logei}.\n153\u2192\n154\u2192\\section{Methods}\n155\u2192\\subsection{Fabrication}\n156\u2192Specimens are fabricated on a multi-material FDM system capable of\n157\u2192co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158\u2192Print parameters (temperature, line width, infill, retraction) are\n159\u2192held fixed within a batch to isolate the effect of the design\n160\u2192variables. Each specimen is weighed and dimensionally inspected prior\n161\u2192to testing so that mass and geometric deviations are available as\n162\u2192covariates.\n163\u2192\n164\u2192\\subsection{Mechanical Testing}\n165\u2192Quasi-static compression is performed on a screw-driven load frame at\n166\u2192a fixed nominal strain rate, with force--displacement recorded to\n167\u2192densification. Drop-weight impact testing uses an instrumented tup at\n168\u2192a fixed impact energy; transmitted force is captured at high sample\n169\u2192rate, and specimens are imaged before and after impact to document\n170\u2192failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171\u2192unit mass, and compaction efficiency $\\eta$, defined consistently\n172\u2192across the campaign. A control specimen from an earlier batch is\n173\u2192retested periodically to monitor TPU batch-to-batch and printer drift.\n174\u2192\n175\u2192\\section{Expected Outcomes and Discussion}\n176\u2192We will report how BO-selected designs evolve across iterations, how\n177\u2192GP surrogate predictions and uncertainties change as data accumulate,\n178\u2192and which Pareto-efficient trade-offs emerge between SEA and peak\n179\u2192transmitted force within the explored design space. We will also\n180\u2192discuss practical lessons for operating the closed-loop workflow,\n181\u2192including handling of print failures, batch-to-batch variability in\n182\u2192TPU response, the influence of the rigid--flexible interface on\n183\u2192energy absorption, and the exploration--exploitation balance of the\n184\u2192acquisition function under realistic experimental noise. We expect\n185\u2192the campaign to clarify where physical experimentation is genuinely\n186\u2192required versus where simpler analytical or multi-fidelity surrogate\n187\u2192shortcuts~\\citep{mo2023accelerated} could be\n188\u2192substituted in future work.\n189\u2192\n190\u2192\\section{Conclusion}\n191\u2192This work demonstrates a partially automated, experiment-driven\n192\u2192closed-loop pipeline for designing multi-material 3D-printed\n193\u2192tensegrity-inspired energy absorbers, with surrogate updating and\n194\u2192candidate selection automated and fabrication and testing kept manual.\n195\u2192By updating a GP surrogate directly from physical measurements, the\n196\u2192workflow avoids direct dependence on calibrated finite-element\n197\u2192simulation for objective evaluation while still providing principled,\n198\u2192multi-objective design recommendations. The same closed-loop pattern is\n199\u2192expected to transfer to other multi-material additively manufactured\n200\u2192architectures whose performance is dominated by hard-to-simulate\n201\u2192effects.\n202\u2192\n203\u2192{\\tiny\n204\u2192\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205\u2192\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206\u2192% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207\u2192% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208\u2192\\providecommand{\\doi}[1]{}\n209\u2192\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210\u2192\\bibliography{references}\n211\u2192}\n212\u2192\n213\u2192\\end{document}", + "name": "read", + "tool_call_id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "info": { + "start_ts": 8147.332633466, + "end_ts": 8147.339043095 + } + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\", \"offset\": 174, \"limit\": 100}", + "name": "read" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "tool", + "content": " 1\u2192# IDETC Tensegrity Slides \u2014 Draft 1 (extracted content)\n 2\u2192\n 3\u2192Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4\u2192\n 5\u2192\n 6\u2192## Slide 1 \u2014 layout: Title Only\n 7\u2192\n 8\u2192**On-slide text:**\n 9\u2192- **Title 1**: Let\u2019s build better tensegrity structures faster\n 10\u2192- **TextBox 4**: Marcus Madsen\n 11\u2192Undergraduate researcher under Sterling Baird, Ph.D.\n 12\u2192- **TextBox 6**: IDETC 2026\n 13\u2192\n 14\u2192**Non-text shapes / media / placeholders:**\n 15\u2192- [PICTURE (13)] Picture 3\n 16\u2192- [PICTURE (13)] Picture 8\n 17\u2192\n 18\u2192**Presenter notes:**\n 19\u2192> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20\u2192> \n 21\u2192> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22\u2192\n 23\u2192\n 24\u2192## Slide 2 \u2014 layout: Title Only\n 25\u2192\n 26\u2192**On-slide text:**\n 27\u2192- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings\u2014and to problems closer to home.\n 28\u2192\n 29\u2192**Non-text shapes / media / placeholders:**\n 30\u2192- [MEDIA (16)] Online Media 5\n 31\u2192\n 32\u2192**Presenter notes:**\n 33\u2192> Replace this video with a downloaded and trimmed version of just the fall. \n 34\u2192> \n 35\u2192> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle \u2014 thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander\u00a0is\u00a0the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit\u00a0and get back up\u00a0\u2014 it survives multiple drops and keeps working.\n 36\u2192\n 37\u2192\n 38\u2192## Slide 3 (HIDDEN) \u2014 layout: Title Only\n 39\u2192\n 40\u2192**On-slide text:**\n 41\u2192- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42\u2192- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43\u2192- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44\u2192\n 45\u2192**Presenter notes:**\n 46\u2192> This might be better explained purely vocally while on the previous slide.\n 47\u2192\n 48\u2192\n 49\u2192## Slide 4 \u2014 layout: Title Only\n 50\u2192\n 51\u2192**On-slide text:**\n 52\u2192- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53\u2192- **TextBox 3**: Iteration\n 54\u2192- **TextBox 5**: Building\n 55\u2192- **TextBox 7**: Testing\n 56\u2192\n 57\u2192**Non-text shapes / media / placeholders:**\n 58\u2192- [MEDIA (16)] AdobeStock_207717222\n 59\u2192- [MEDIA (16)] Online Media 2\n 60\u2192- [PICTURE (13)] Picture 14\n 61\u2192- [PICTURE (13)] Picture 16\n 62\u2192- [PICTURE (13)] Picture 18\n 63\u2192\n 64\u2192**Presenter notes:**\n 65\u2192> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn\u2019t become distracting because it\u2019s fun to watch it spin.\n 66\u2192> \n 67\u2192> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68\u2192\n 69\u2192\n 70\u2192## Slide 5 (HIDDEN) \u2014 layout: Title Only\n 71\u2192\n 72\u2192**On-slide text:**\n 73\u2192- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74\u2192- **TextBox 14**: Baird et al. Matter (2022)\n 75\u2192\n 76\u2192**Non-text shapes / media / placeholders:**\n 77\u2192- [GROUP (6)] Group 2\n 78\u2192\n 79\u2192**Presenter notes:**\n 80\u2192> Consult Sterling on this slide. I don\u2019t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it\u2019s well enough known that it\u2019d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81\u2192\n 82\u2192\n 83\u2192## Slide 6 (HIDDEN) \u2014 layout: Title Only\n 84\u2192\n 85\u2192**On-slide text:**\n 86\u2192- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87\u2192- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88\u2192\n 89\u2192**Presenter notes:**\n 90\u2192> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91\u2192\n 92\u2192\n 93\u2192## Slide 7 \u2014 layout: Title Only\n 94\u2192\n 95\u2192**On-slide text:**\n 96\u2192- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97\u2192- **TextBox 5**: 6 variables\n 98\u2192- **TextBox 7**: 2 types of input data\n 99\u2192- **TextBox 9**: 2 objectives\n100\u2192- **TextBox 17**: *image of blackbox*\n101\u2192\n102\u2192**Non-text shapes / media / placeholders:**\n103\u2192- [LINE (9)] Connector: Curved 11\n104\u2192- [LINE (9)] Connector: Curved 13\n105\u2192- [LINE (9)] Connector: Curved 15\n106\u2192\n107\u2192**Presenter notes:**\n108\u2192> Very rough slide as I\u2019m not entirely sure what graphic or text to put on this, partially because I\u2019m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109\u2192\n110\u2192\n111\u2192## Slide 8 \u2014 layout: Title Only\n112\u2192\n113\u2192**On-slide text:**\n114\u2192- **Title 1**: Multi-material additive manufacturing supplies that data fast \u2014 single-build co-fabrication, with no joining of parts.\n115\u2192\n116\u2192**Non-text shapes / media / placeholders:**\n117\u2192- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118\u2192- [PICTURE (13)] Picture 4\n119\u2192\n120\u2192**Presenter notes:**\n121\u2192> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122\u2192\n123\u2192\n124\u2192## Slide 9 \u2014 layout: Title Only\n125\u2192\n126\u2192**On-slide text:**\n127\u2192- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128\u2192- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129\u2192- **TextBox 5**: *placeholder for slo motion of drop test*\n130\u2192\n131\u2192\n132\u2192## Slide 10 \u2014 layout: Title Only\n133\u2192\n134\u2192**On-slide text:**\n135\u2192- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136\u2192- **TextBox 3**: Printing\n137\u2192- **TextBox 5**: Testing\n138\u2192- **TextBox 7**: BO feed\n139\u2192\n140\u2192**Non-text shapes / media / placeholders:**\n141\u2192- [PICTURE (13)] Picture 14\n142\u2192- [PICTURE (13)] Picture 16\n143\u2192- [PICTURE (13)] Picture 18\n144\u2192- [PICTURE (13)] Graphic 8\n145\u2192\n146\u2192**Presenter notes:**\n147\u2192> I think it might be better if we replaced \u201cprinting\u201d with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148\u2192\n149\u2192\n150\u2192## Slide 11 (HIDDEN) \u2014 layout: Title Only\n151\u2192\n152\u2192**On-slide text:**\n153\u2192- **Title 1**: Reserved for results\n154\u2192\n155\u2192**Presenter notes:**\n156\u2192> Would be helpful if we could show that this process works in this case.\n157\u2192\n158\u2192\n159\u2192## Slide 12 \u2014 layout: Title Only\n160\u2192\n161\u2192**On-slide text:**\n162\u2192- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163\u2192- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164\u2192- **TextBox 3**: *image to show lattice structure\n165\u2192\n166\u2192\n167\u2192## Slide 13 \u2014 layout: Title Only\n168\u2192\n169\u2192**Non-text shapes / media / placeholders:**\n170\u2192- [PICTURE (13)] Picture 3\n171\u2192\n172\u2192**Presenter notes:**\n173\u2192> Slide for questions at the end\n174\u2192\n175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "info": { + "start_ts": 8147.330900226, + "end_ts": 8147.337548875 + } + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Presentation \u2014 Doumont Presentation Structure Template\n 2\u2192\n 3\u2192Transcribed from Jean-luc Doumont's presentation-structure template\n 4\u2192([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5\u2192adapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\n 6\u2192All rights reserved. Can be downloaded from .\n 7\u2192\n 8\u2192> To design your presentation, write down your ideas for each component below.\n 9\u2192> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10\u2192\n 11\u2192**Draft 3** \u2014 applies the inline review comments from @sgbaird on PR #84 and the\n 12\u2192priority revisions from the\n 13\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14\u2192Summaries of what changed are at the bottom of this file\n 15\u2192([Draft 2 \u2192 3](#changes-from-draft-2), [Draft 1 \u2192 2](#changes-from-draft-1)).\n 16\u2192\n 17\u2192**Scope note (frame everything below with this):** the study presented is the\n 18\u2192optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19\u2192processing parameters** (e.g., nozzle temperature, print speed). PLA\u2013TPU is a\n 20\u2192**proxy system** \u2014 not flight material \u2014 used to prototype a self-driving-lab\n 21\u2192style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22\u2192The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23\u2192beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24\u2192\n 25\u2192## Opening\n 26\u2192\n 27\u2192### Attention getter\n 28\u2192\n 29\u2192*A way to lead the audience to the need efficiently*\n 30\u2192\n 31\u2192Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32\u2192landings \u2014 and to problems closer to home.\n 33\u2192\n 34\u2192One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35\u2192clip) do the work:\n 36\u2192\n 37\u2192> Landing a payload on another planet means surviving an impact in conditions\n 38\u2192> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n 39\u2192> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40\u2192> structure: the lander *is* the energy absorber. And unlike crushable\n 41\u2192> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42\u2192> hit **and get back up** \u2014 it survives multiple drops and keeps working.\n 43\u2192\n 44\u2192Candidate Super Ball Bot videos to show (verified links):\n 45\u2192\n 46\u2192- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n 47\u2192 (official channel); the canonical overview.\n 48\u2192- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49\u2192 \u2014 Wall Street Journal; prototype drop footage.\n 50\u2192- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51\u2192 \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n 52\u2192\n 53\u2192Immediately after the hook, state the present study scope in one sentence\n 54\u2192(per the scope note above): *we study a printed PLA\u2013TPU T3 prism as a proxy\n 55\u2192system for prototyping an autonomous tensegrity-optimization workflow* \u2014 so\n 56\u2192the planetary framing never outruns the demonstrated scope.\n 57\u2192\n 58\u2192\n 60\u2192\n 61\u2192### Need\n 62\u2192\n 63\u2192*A difference between actual and desired situations*\n 64\u2192\n 65\u2192**Actual situation** (a two-sided pincer \u2014 stated as a scoped engineering\n 66\u2192judgment, not an absolute):\n 67\u2192\n 68\u2192- For these structures, obtaining trustworthy objective values from a\n 69\u2192 validated high-fidelity model would require substantial calibration of\n 70\u2192 interface, rate, contact, and defect behavior \u2014 and even heavy-duty\n 71\u2192 simulation falls short of the experiments we are running: peak transmitted\n 72\u2192 shock is a millisecond-scale transient that is notoriously difficult to\n 73\u2192 resolve numerically. This holds even for idealized cable\u2013strut tensegrities\n 74\u2192 in their actual engineering materials (steel or similar), not just for\n 75\u2192 printed PLA\u2013TPU.\n 76\u2192 \n 79\u2192- Exhaustive physical search fails on cost: a traditional design of\n 80\u2192 experiments over this relatively high-dimensional space (geometry + FDM\n 81\u2192 process parameters) would demand hundreds of specimens, each costing print\n 82\u2192 and test time.\n 83\u2192- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84\u2192 more complex structures \u2014 especially tensegrity *lattices* \u2014 become\n 85\u2192 extremely difficult and ultimately infeasible to hand-build.\n 86\u2192\n 87\u2192**Desired situation:**\n 88\u2192\n 89\u2192- Trustworthy performance data without hand assembly (easy creation of\n 90\u2192 tensegrity structures, scalable beyond a single prism).\n 91\u2192- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n 92\u2192 not the hundreds a traditional design of experiments would require.\n 93\u2192\n 94\u2192### Task\n 95\u2192\n 96\u2192*What I decided/was asked to do to address the need*\n 97\u2192\n 98\u2192Build a closed-loop design\u2013print\u2013test workflow that optimizes a **T3-prism\n 99\u2192tensegrity-inspired energy absorber together with its FDM processing\n100\u2192parameters** (e.g., temperature, print speed) directly from real, measured\n101\u2192impact data \u2014 maximizing energy absorbed per gram (specific energy\n102\u2192absorption) and compaction efficiency, subject to a cap on the peak force\n103\u2192transmitted to the payload. Extensions beyond the T3 prism are future work.\n104\u2192\n105\u2192### Main message\n106\u2192\n107\u2192*The one sentence I want my audience to remember*\n108\u2192\n109\u2192By closing the loop between multi-material 3D printing and Bayesian\n110\u2192optimization, we can optimize tensegrity-inspired energy absorbers directly\n111\u2192from real impact data \u2014 in dozens of prints, not the hundreds a traditional\n112\u2192design of experiments would demand.\n113\u2192\n114\u2192\n118\u2192\n119\u2192### Preview\n120\u2192\n121\u2192*A map of the body (ideally three points, max. five)*\n122\u2192\n123\u21921. When model calibration and exhaustive testing are both costly, each\n124\u2192 physical specimen must be chosen for its information value \u2014 so the\n125\u2192 measured experiment is our source of truth.\n126\u21922. Bayesian optimization makes physical experimentation affordable: it handles\n127\u2192 noisy measurements, small batches, and the constrained multi-objective\n128\u2192 trade-off \u2014 and Honegumi let us scaffold it in minutes.\n129\u21923. Multi-material additive manufacturing closes the loop: single-build PLA\u2013TPU\n130\u2192 co-fabrication turns each proposed design into test data in hours, with no\n131\u2192 joining of parts.\n132\u21924. *(Optional fourth point, if time allows)* The same closed loop points at\n133\u2192 what comes next: tensegrity lattices, flight-relevant materials, and\n134\u2192 safety hardware on Earth.\n135\u2192\n136\u2192## Body\n137\u2192\n138\u2192### Point 1\n139\u2192\n140\u2192When model calibration and exhaustive testing are both costly, each physical\n141\u2192specimen must be chosen for its information value.\n142\u2192\n143\u2192- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144\u2192 these structures, a sufficiently calibrated multiphysics model is expensive\n145\u2192 to build relative to the design campaign itself \u2014 and the hardest quantity,\n146\u2192 peak transmitted shock, is a millisecond-scale transient that even\n147\u2192 heavy-duty simulation struggles to resolve. The gap persists in the actual\n148\u2192 engineering materials a real lander would use (steel struts and cables),\n149\u2192 so this is not merely an artifact of printed PLA\u2013TPU.\n150\u2192- The alternative \u2014 sweeping the design space physically \u2014 fails on cost: a\n151\u2192 traditional design of experiments over geometry plus FDM process parameters\n152\u2192 would take hundreds of specimens, each needing print + test time.\n153\u2192- Therefore: treat the physical experiment as the authoritative source of\n154\u2192 objective values, and be ruthless about *which* experiments to run.\n155\u2192- Anticipate the FEA-veteran pushback (\"inconvenient \u2260 untrustworthy\"): frame\n156\u2192 it as a cost-and-dependence decision, and keep a backup slide with the\n157\u2192 literature (or our own validation) on simulation\u2013experiment discrepancy for\n158\u2192 impact transients.\n159\u2192\n160\u2192### Transition\n161\u2192\n162\u2192If every data point must be a real experiment, we need a method that extracts\n163\u2192the most from every specimen \u2014 we need to make the process radically more\n164\u2192sample-efficient.\n165\u2192\n166\u2192### Point 2\n167\u2192\n168\u2192Bayesian optimization makes physical experimentation affordable, even with\n169\u2192limited, noisy data.\n170\u2192\n171\u2192- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172\u2192 visual in the main talk; implementation detail goes to backup slides.\n173\u2192 Spoken version:\n174\u2192 > \"After each batch, the model estimates both expected performance and\n175\u2192 > uncertainty across the design space. The acquisition rule selects a small\n176\u2192 > next batch that is most likely to expand the feasible trade-off frontier,\n177\u2192 > while accounting for noisy tests and print failures.\"\n178\u2192- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179\u2192 motivate a noise-aware acquisition function (qNEHVI) rather than\n180\u2192 non-noise-aware alternatives. (Backup slide: how observation noise is\n181\u2192 actually modeled \u2014 replicates, noise identification \u2014 and budget-matched\n182\u2192 baselines such as Sobol/random sampling.)\n183\u2192- Small parallel batches: the surrogate proposes the next batch of specimens\n184\u2192 to print, balancing exploration and exploitation.\n185\u2192- Constrained multi-objective by construction: **objectives** = maximize\n186\u2192 specific energy absorption and compaction efficiency; **constraint** = peak\n187\u2192 transmitted force must stay under a cap; the deliverable is the *feasible*\n188\u2192 Pareto front between the two objectives among force-cap-satisfying designs.\n189\u2192 (Keep this formulation consistent everywhere \u2014 do not present \"SEA vs. peak\n190\u2192 force\" as the trade-off.)\n191\u2192- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192\u2192 this advanced task (noisy, batched, constrained, multi-objective) in\n193\u2192 minutes using [Honegumi](https://honegumi.readthedocs.io/) \u2014 the audience\n194\u2192 can do the same for their own problems; show the code-template picker\n195\u2192 briefly so they see how low the barrier is.\n196\u2192\n197\u2192### Transition\n198\u2192\n199\u2192The optimizer is no longer the bottleneck \u2014 the limit is now providing real\n200\u2192data quickly enough.\n201\u2192\n202\u2192### Point 3\n203\u2192\n204\u2192Multi-material additive manufacturing supplies that data fast \u2014 single-build\n205\u2192co-fabrication, with no joining of parts.\n206\u2192\n207\u2192- Single-build PLA\u2013TPU co-fabrication: rigid struts and flexible tension\n208\u2192 network in one print \u2014 no joining or hand assembly of members, which closes\n209\u2192 the \"easy creation\" gap from the Need and is what makes future lattices\n210\u2192 feasible at all. (Be precise: if support removal, trimming, or conditioning\n211\u2192 remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212\u2192 assembly.\")\n213\u2192- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214\u2192 as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215\u2192 pre-tension status) plus a short deformation sequence \u2014 so the EDL engineer\n216\u2192 doesn't file it as a \"crush structure\" and the grad student can tell it from\n217\u2192 an ordinary lattice.\n218\u2192- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219\u2192 tested specimen (quasi-static compression + instrumented drop-weight impact)\n220\u2192 in hours. (Backup slide: representative print-to-result time breakdown and\n221\u2192 which steps remain manual \u2014 candidate selection is automated; slicing,\n222\u2192 handling, and testing are not yet.)\n223\u2192- Now we have the data we need to complete the loop.\n224\u2192\n225\u2192### Point 4 (optional \u2014 future applications)\n226\u2192\n227\u2192*Include if timing allows; otherwise fold into the Conclusion.*\n228\u2192\n229\u2192The same closed loop transfers wherever tests are authoritative, each test is\n230\u2192costly, the design space is mixed, and several performance goals conflict:\n231\u2192\n232\u2192- Tensegrity *lattices* and more complex architectures \u2014 exactly where hand\n233\u2192 assembly becomes infeasible and single-build co-fabrication pays off.\n234\u2192- Migration from the PLA\u2013TPU proxy toward flight-relevant materials and\n235\u2192 impact regimes.\n236\u2192- Safety hardware on Earth (e.g., the tensegrity crutch tip \u2014 future work).\n237\u2192- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238\u2192 handling, testing) in the loop.\n239\u2192\n240\u2192### Evidence / results\n241\u2192\n242\u2192*What we found \u2014 ground the conclusion in shown data*\n243\u2192\n244\u2192Build the talk around a quantitative three-slide sequence (per the\n245\u2192mock-audience report; insert observed values once the campaign completes):\n246\u2192\n247\u21921. **Campaign ledger** \u2014 \"The campaign used N specimens across B adaptive\n248\u2192 batches under a fixed physical-test budget\": initialization vs. adaptive\n249\u2192 batches, replicates, print failures, cycle time, and the numerical value\n250\u2192 (and rationale) of the force cap.\n251\u21922. **Budget-matched baseline** \u2014 \"Adaptive selection improved the feasible\n252\u2192 design set faster than [predeclared baseline]\": performance vs. number of\n253\u2192 physical tests against Sobol/random sampling at the same budget, with\n254\u2192 uncertainty where available.\n255\u21923. **Measured feasible Pareto front** \u2014 specific energy absorption vs.\n256\u2192 compaction efficiency among designs satisfying the force cap, with\n257\u2192 replicate spread, marked infeasible designs, and specimen photos or\n258\u2192 force\u2013time traces for representative Pareto designs.\n259\u2192\n260\u2192Supporting raw-data candidates: drop-test force\u2013time traces, Sobol /\n261\u2192first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262\u2192\n263\u2192### Suggested 15-minute allocation\n264\u2192\n265\u2192*(From the mock-audience report \u2014 results get the largest block.)*\n266\u2192\n267\u2192| Time | Content |\n268\u2192|---|---|\n269\u2192| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270\u2192| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271\u2192| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272\u2192| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273\u2192| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274\u2192| 1 min | Limitations and transfer conditions |\n275\u2192| 1 min | Measured conclusion + bookend |\n276\u2192\n277\u2192Move kernel choices, encodings, and other implementation detail to backup\n278\u2192slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279\u2192[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280\u2192\n281\u2192## Closing\n282\u2192\n283\u2192### Review\n284\u2192\n285\u2192*A recap of the body, leading into the conclusion*\n286\u2192\n287\u2192Using real, measured impact data from additively manufactured\n288\u2192tensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\n289\u2192optimization routine \u2014 we rapidly iterate toward better energy absorbers.\n290\u2192This feedback loop makes tensegrity optimization dramatically faster: dozens\n291\u2192of prints instead of the hundreds a traditional design of experiments over\n292\u2192this design space would require.\n293\u2192\n294\u2192### Conclusion\n295\u2192\n296\u2192*What the above means to the audience in the end*\n297\u2192\n298\u2192Because the loop runs on physical measurements, it is useful precisely where\n299\u2192model calibration is not worth its cost \u2014 and the same closed-loop pattern\n300\u2192should transfer (a conditional claim, pending evidence) to other additively\n301\u2192manufactured architectures whose performance is dominated by\n302\u2192hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303\u2192*use this loop when tests are authoritative, each test is costly, the design\n304\u2192space is mixed, and several performance goals conflict.* For tensegrity\n305\u2192specifically, it opens a path from this T3 proxy study toward lattices,\n306\u2192flight-relevant materials, payload protection on other planets, and safety\n307\u2192hardware here on Earth (e.g., the crutch tip, as future work).\n308\u2192\n309\u2192### Close\n310\u2192\n311\u2192*A way to end the presentation clearly and elegantly*\n312\u2192\n313\u2192Return to the opening image: the next planetary lander's energy absorber \u2014 a\n314\u2192structure that has to survive not one impact but many \u2014 doesn't have to take\n315\u2192years of hand-built prototypes; with this loop, candidate designs can be\n316\u2192designed, printed, and tested against real impacts in weeks instead of months\n317\u2192or longer. End on the measured engineering result and its transfer condition,\n318\u2192then the bookend: every method that compresses design time moves our ventures\n319\u2192\u2014 on this planet and beyond \u2014 forward.\n320\u2192\n321\u2192---\n322\u2192\n323\u2192## Changes from Draft 2\n324\u2192\n325\u2192Applied from @sgbaird's inline review comments on PR #84 and the\n326\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327\u2192\n328\u2192**From the inline review comments:**\n329\u2192\n330\u21921. **Reusability in the hook** (line 22): the tensegrity lander survives\n331\u2192 multiple drops, unlike crushable/sacrificial alternatives.\n332\u21922. **Proxy-system framing** (lines 49, 87): PLA\u2013TPU would never fly; it is a\n333\u2192 proxy for prototyping a self-driving-lab-style workflow \u2014 stated in a\n334\u2192 scope note up front and re-stated right after the hook.\n335\u21923. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336\u2192 heavy-duty simulation falls short of the experiments (millisecond-scale\n337\u2192 peak shock), not that it merely costs too much.\n338\u21924. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339\u2192 complex structures and especially lattices are not.\n340\u21925. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341\u2192 (temperature, print speed, \u2026) only; the rest is explicitly future work.\n342\u21926. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343\u2192 literature search (or validation of experiments against simulation), plus\n344\u2192 the millisecond-resolution peak-shock argument.\n345\u21927. **Optional Point 4 added** (line 97): future applications, with a note it\n346\u2192 can fold into the Conclusion if time is short.\n347\u21928. **Steel-materials point** (line 104): simulations fall short for the\n348\u2192 actual engineering materials (steel struts/cables), not just printed\n349\u2192 PLA\u2013TPU.\n350\u21929. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351\u2192 in minutes, pitched so the audience sees they can use it too.\n352\u219210. **\"Thousands\" \u2192 \"hundreds\"** (line 173): baseline is now a traditional\n353\u2192 design of experiments over a high-dimensional space (hundreds), in the\n354\u2192 main message, Need, Point 1, and Review.\n355\u219211. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356\u2192 or longer\" for candidate designs.\n357\u2192\n358\u2192**From the mock-audience report's priority revisions:**\n359\u2192\n360\u219212. **Quantitative three-slide results sequence** (Priority 1): campaign\n361\u2192 ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front, with a\n362\u2192 fallback main-message wording if the campaign can't yet support the\n363\u2192 efficiency claim.\n364\u219213. **Anti-simulation premise replaced with a scoped cost decision**\n365\u2192 (Priority 2): Point 1 retitled to the information-value message;\n366\u2192 \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367\u2192 anticipated with a backup slide.\n368\u219214. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369\u2192 implementation detail moved to backup slides; Q&A prep pointed at the\n370\u2192 report's claims-vs-evidence table.\n371\u219215. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372\u2192 the main talk, machinery in backup slides; the misleading\n373\u2192 \"qNEHVI over standard expected improvement\" comparison replaced with\n374\u2192 \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375\u219216. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376\u2192 sequence in Point 3, so the structure isn't remembered as a generic crush\n377\u2192 structure or lattice.\n378\u219217. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379\u2192 co-fabrication / no joining of parts,\" with a note to list remaining\n380\u2192 manual steps and an automation-status caveat.\n381\u219218. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382\u2192 compaction efficiency, constraint = force cap, deliverable = *feasible*\n383\u2192 Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384\u219219. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385\u2192 transfer claim marked conditional, and the close ends on the measured\n386\u2192 result before the bookend.\n387\u2192\n388\u2192## Changes from Draft 1\n389\u2192\n390\u2192Feedback from the PR #84 review, applied in Draft 2:\n391\u2192\n392\u21921. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n393\u2192 dozens-not-thousands claim); body and message now carry the same story.\n394\u21922. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n395\u2192 manuscript's positioning (#75/#76) is that the physical experiment is the\n396\u2192 source of truth, explicitly avoiding calibrated FE simulation.\n397\u21923. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n398\u2192 parametric design space and avoid an over-claim to a design-automation\n399\u2192 audience.\n400\u21924. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401\u2192 energy absorption and compaction efficiency, cap peak transmitted force.\n402\u21925. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403\u2192 *and* the space is too big to brute-force \u2014 which makes BO + AM the\n404\u2192 inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405\u2192 above.)*\n406\u21926. **Point 1 given its own content** (the *why* behind the inefficiency)\n407\u2192 instead of restating the Need.\n408\u21927. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409\u2192 the specifics (noise-aware acquisition, small batches, multi-objective).\n410\u21928. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411\u2192 (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412\u2192 above.)*\n413\u21929. **Evidence/results slot added** before the Closing, so the Conclusion is\n414\u2192 grounded in shown data rather than three hedged \"may\"s.\n415\u219210. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n416\u2192 Ball Bot visual; verified video links included.\n417\u219211. **Close bookends the opening** (the next lander's absorber, designed in\n418\u2192 weeks) before the broader humankind line.\n419\u219212. **Spelling**: Doumont (was \"Duomont\").", + "name": "read", + "tool_call_id": "call_93haurOMDGiH2mQFFbaMaYKn", + "info": { + "start_ts": 8147.331065886, + "end_ts": 8147.336467845 + } + }, + { + "role": "tool", + "content": " 1\u2192# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n 2\u2192\n 3\u2192Video: (assigned in #83)\n 4\u2192\n 5\u2192Transcribed from @me-madsen's handwritten notes\n 6\u2192([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7\u2192dated 2026-07-15).\n 8\u2192\n 9\u2192## Overview\n10\u2192\n11\u2192A presentation is all about the right content, displayed the right way, at the\n12\u2192right time.\n13\u2192\n14\u2192- Verbal communication is sequential.\n15\u2192- Message is *interpretation* of the information.\n16\u2192 - Ensure the audience can get the most out of what you're doing.\n17\u2192 - Don't just give them information \u2014 help them understand it, interpret it,\n18\u2192 and internalize it.\n19\u2192\n20\u2192## Doumont's suggested rules for a presentation\n21\u2192\n22\u21920. Ensure you have **messages** for your audience, not just words.\n23\u21921. **Adapt to your audience.**\n24\u2192 - Think like the audience, not the speaker.\n25\u21922. **Maximize signal-to-noise ratio.**\n26\u2192 - Reduce noise (animations, laser pointers, anything that pulls attention\n27\u2192 away from your message).\n28\u21923. **Use effective redundancy.**\n29\u2192 - Tell your message in word *and* on slide: someone deaf should understand\n30\u2192 your messages just as well as someone blind.\n31\u2192 - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32\u2192 date).\n33\u2192\n34\u2192## When creating slides\n35\u2192\n36\u2192- If the audience doesn't need it, why put it on the slides?\n37\u2192- People shouldn't have to count the items on a slide to know how many are\n38\u2192 there.\n39\u2192- Don't use your slides as your \"presentation notes.\"\n40\u2192 - Don't show your notes to the audience.\n41\u2192 - **A bad slide is worse than no slide at all.**\n42\u2192- **One message per slide** is a good rule of thumb.\n43\u2192 - Prioritize the *so what* (or *therefore what*) of your message.\n44\u2192- Make it easy to interpret the information on the slide:\n45\u2192 - 3\u20135 items at most.\n46\u2192 - Align the title left.\n47\u2192 - Do not clutter slides with repetitive or unnecessary information: logos,\n48\u2192 affiliation, date, slide numbers.\n49\u2192- Doumont's suggested workflow:\n50\u2192 1. **Start with the title.**\n51\u2192 - Use a full sentence in the title.\n52\u2192 - The title is the main message of the slide.\n53\u2192 - Make it easy to read: left-aligned, with intentional line breaks.\n54\u2192 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55\u2192- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56\u2192\n57\u2192## General preparation\n58\u2192\n59\u2192- Planning for audience, content, and structure matters more than materials\n60\u2192 (i.e., slides).\n61\u2192- Try using one sheet of paper for presentation preparation.\n62\u2192- It's most important that your presentation is:\n63\u2192 - well planned, structured, and delivered;\n64\u2192 - **if you don't have a lot of time to prepare, don't focus on making\n65\u2192 slides.**\n66\u2192- Practice the presentation a few times. Practice at least once *without* your\n67\u2192 slideshow!\n68\u2192- If doing a handout, keep it separate from the slides you're preparing.\n69\u2192- Impress the audience with your body.\n70\u2192 - Use gestures, not laser pointers.\n71\u2192 - \"Control\" the audience with your body \u2014 keep their attention on your\n72\u2192 presentation.\n73\u2192\n74\u2192## Impressions / what to apply to the IDETC presentation\n75\u2192\n76\u2192\n77\u2192\n78\u2192## Questions / things to discuss\n79\u2192\n80\u2192", + "name": "read", + "tool_call_id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "info": { + "start_ts": 8147.331133416, + "end_ts": 8147.338304015 + } + }, + { + "role": "tool", + "content": " 1\u2192\\documentclass[10pt]{article}\n 2\u2192\n 3\u2192% --- Packages ---\n 4\u2192\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5\u2192\\usepackage[hidelinks]{hyperref}\n 6\u2192\\usepackage{parskip}\n 7\u2192\\usepackage{graphicx}\n 8\u2192\\usepackage{caption}\n 9\u2192\\usepackage{titlesec}\n 10\u2192\\usepackage{wrapfig}\n 11\u2192\\usepackage[numbers,sort&compress]{natbib}\n 12\u2192\n 13\u2192% --- Formatting ---\n 14\u2192\\setlength{\\parskip}{0.2em}\n 15\u2192\\setlength{\\parindent}{0em}\n 16\u2192\\titleformat*{\\section}{\\large\\bfseries}\n 17\u2192\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18\u2192\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19\u2192\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20\u2192\\setlength{\\emergencystretch}{2em}\n 21\u2192\\setlength{\\intextsep}{0.2em}\n 22\u2192\\bibliographystyle{abbrvnat}\n 23\u2192\n 24\u2192\\begin{document}\n 25\u2192\n 26\u2192\\begin{center}\n 27\u2192{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28\u21923D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29\u2192\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30\u2192\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31\u2192\\end{center}\n 32\u2192\n 33\u2192\\vspace{0.4em}\n 34\u2192\n 35\u2192\\section*{Abstract}\n 36\u2192Tensegrity-inspired architectures---rigid struts suspended within a\n 37\u2192continuous flexible network---can exhibit tunable nonlinear\n 38\u2192force--displacement responses and favorable energy absorption per unit\n 39\u2192mass, motivating their use in lightweight energy-absorbing and protective\n 40\u2192structures\n 41\u2192\\citep{skelton2009tensegrity,pajunen2019design}.\n 42\u2192Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43\u2192struts and flexible (TPU) elements in a single build\n 44\u2192\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45\u2192space---strut geometry, tension-element cross-section, connectivity\n 46\u2192topology, and unit-cell tiling---is too large to explore by trial and\n 47\u2192error. We present a closed-loop experimental campaign that uses\n 48\u2192Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49\u2192to drive a design--print--test workflow operating directly on physical\n 50\u2192measurements, without relying on calibrated finite-element simulation\n 51\u2192for objective evaluation.\n 52\u2192\n 53\u2192\\section{Introduction and Motivation}\n 54\u2192\n 55\u2192\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56\u2192\\centering\n 57\u2192\\vspace{-0.4em}\n 58\u2192\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59\u2192\\captionsetup{font=small}\n 60\u2192\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61\u2192Candidate tensegrity-inspired unit cells are instantiated from a\n 62\u2192parameter vector, fabricated by multi-material FDM, and tested under\n 63\u2192quasi-static compression and drop-weight impact. Measured peak force,\n 64\u2192specific energy absorption (SEA), and compaction efficiency update a\n 65\u2192Gaussian-process surrogate that proposes the next batch of designs.}\n 66\u2192\\label{fig:overview}\n 67\u2192\\vspace{-0.4em}\n 68\u2192\\end{wrapfigure}\n 69\u2192\n 70\u2192Tensegrity structures combine compression-only members with a\n 71\u2192pre-tensioned tension network to achieve stiffness without rigid\n 72\u2192joinery~\\citep{skelton2009tensegrity}. Their\n 73\u2192nonlinear, often load-limiting force--displacement response makes them\n 74\u2192attractive for impact mitigation, packaging, and wearable protective\n 75\u2192gear~\\citep{pajunen2019design}. Idealized\n 76\u2192cable--strut tensegrities are difficult to manufacture at scale, but\n 77\u2192\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78\u21923D printing reproduce key tensegrity behaviors---including\n 79\u2192post-buckling stability and load-limiting force--displacement\n 80\u2192response~\\citep{pajunen2019design}---motivating our extension to\n 81\u2192multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82\u2192elements. PLA provides rigid struts; TPU provides flexible,\n 83\u2192rate-dependent tension elements whose viscoelasticity is expected to\n 84\u2192contribute hysteretic energy dissipation under impact loading,\n 85\u2192consistent with the energy-absorbing behavior reported for rigid--TPU\n 86\u2192multi-material prints~\\citep{khatri2024energy}.\n 87\u2192\n 88\u2192The challenge is that small geometric and topological changes can\n 89\u2192produce large changes in the measured response, and high-fidelity\n 90\u2192simulation of FDM specimens is computationally expensive and often\n 91\u2192struggles to capture physical defects such as interfacial slip and\n 92\u2192TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93\u2192trial-and-error sweeps over the joint design space are also infeasible:\n 94\u2192even modest parameter resolutions yield thousands of candidates, each\n 95\u2192requiring print and test time. We instead treat the physical\n 96\u2192experiment as the source of truth and use BO to select which specimens\n 97\u2192are most informative to fabricate next---an approach used in related\n 98\u2192closed-loop materials and structures\n 99\u2192campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100\u2192Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101\u2192set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102\u2192designs using parallel, noisy multi-objective BO driven by measured\n103\u2192$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104\u2192\n105\u2192\\section{Approach}\n106\u2192\\subsection{Closed-Loop Workflow}\n107\u2192Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108\u2192proceeds in four steps: (i)~a parameterized unit-cell design is\n109\u2192instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110\u2192is sliced and printed on a multi-material FDM system; (iii)~it is\n111\u2192tested under quasi-static compression and instrumented drop-weight\n112\u2192impact; and (iv)~the resulting performance metrics update a\n113\u2192Gaussian-process (GP) surrogate, which a batch acquisition function\n114\u2192queries to recommend the next set of candidates. Surrogate updating\n115\u2192and experiment selection are automated; specimen handling, slicing,\n116\u2192and test setup remain manual.\n117\u2192\n118\u2192\\subsection{Design Space}\n119\u2192We parameterize a family of unit cells using a core-wrapping\n120\u2192architecture in which rigid PLA struts are enclosed by continuous TPU\n121\u2192skins; the wrapping rationale follows multi-material FDM strategies in\n122\u2192which rigid panels are wrapped by stretchable soft material to prevent\n123\u2192delamination and improve interfacial bonding at the rigid--flexible\n124\u2192interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125\u2192comprises continuous geometric variables (strut diameter and length,\n126\u2192tension-element width and thickness), an integer strut count per unit\n127\u2192cell, and two categorical variables (connectivity topology, unit-cell\n128\u2192tiling pattern). Categorical choices are encoded explicitly rather\n129\u2192than embedded in a continuous metric, and bounds on the continuous\n130\u2192variables are chosen to remain within the printer's resolution and\n131\u2192overhang limits and to keep specimens in a common form factor for the\n132\u2192test fixtures.\n133\u2192\n134\u2192\\subsection{Surrogate and Acquisition}\n135\u2192The GP surrogate is trained on three measured performance metrics:\n136\u2192peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137\u2192(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138\u2192objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139\u2192likelihood with priors to avoid pathological length scales at small $n$.\n140\u2192Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141\u2192interfacial defects motivates our use of q-noisy expected hypervolume\n142\u2192improvement (qNEHVI), which explicitly accounts for observation noise\n143\u2192in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144\u2192expected-improvement variants. Print failures are treated as a\n145\u2192probabilistic feasibility constraint: the acquisition score for each\n146\u2192candidate is its qNEHVI improvement weighted by the GP-estimated\n147\u2192probability of successful fabrication, following recent constrained\n148\u2192multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149\u2192Where applicable, we use numerically stable log-space variants of\n150\u2192improvement-based acquisitions to mitigate vanishing-gradient\n151\u2192pathologies as data and constraints\n152\u2192accumulate~\\citep{ament2023logei}.\n153\u2192\n154\u2192\\section{Methods}\n155\u2192\\subsection{Fabrication}\n156\u2192Specimens are fabricated on a multi-material FDM system capable of\n157\u2192co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158\u2192Print parameters (temperature, line width, infill, retraction) are\n159\u2192held fixed within a batch to isolate the effect of the design\n160\u2192variables. Each specimen is weighed and dimensionally inspected prior\n161\u2192to testing so that mass and geometric deviations are available as\n162\u2192covariates.\n163\u2192\n164\u2192\\subsection{Mechanical Testing}\n165\u2192Quasi-static compression is performed on a screw-driven load frame at\n166\u2192a fixed nominal strain rate, with force--displacement recorded to\n167\u2192densification. Drop-weight impact testing uses an instrumented tup at\n168\u2192a fixed impact energy; transmitted force is captured at high sample\n169\u2192rate, and specimens are imaged before and after impact to document\n170\u2192failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171\u2192unit mass, and compaction efficiency $\\eta$, defined consistently\n172\u2192across the campaign. A control specimen from an earlier batch is\n173\u2192retested periodically to monitor TPU batch-to-batch and printer drift.\n174\u2192\n175\u2192\\section{Expected Outcomes and Discussion}\n176\u2192We will report how BO-selected designs evolve across iterations, how\n177\u2192GP surrogate predictions and uncertainties change as data accumulate,\n178\u2192and which Pareto-efficient trade-offs emerge between SEA and peak\n179\u2192transmitted force within the explored design space. We will also\n180\u2192discuss practical lessons for operating the closed-loop workflow,\n181\u2192including handling of print failures, batch-to-batch variability in\n182\u2192TPU response, the influence of the rigid--flexible interface on\n183\u2192energy absorption, and the exploration--exploitation balance of the\n184\u2192acquisition function under realistic experimental noise. We expect\n185\u2192the campaign to clarify where physical experimentation is genuinely\n186\u2192required versus where simpler analytical or multi-fidelity surrogate\n187\u2192shortcuts~\\citep{mo2023accelerated} could be\n188\u2192substituted in future work.\n189\u2192\n190\u2192\\section{Conclusion}\n191\u2192This work demonstrates a partially automated, experiment-driven\n192\u2192closed-loop pipeline for designing multi-material 3D-printed\n193\u2192tensegrity-inspired energy absorbers, with surrogate updating and\n194\u2192candidate selection automated and fabrication and testing kept manual.\n195\u2192By updating a GP surrogate directly from physical measurements, the\n196\u2192workflow avoids direct dependence on calibrated finite-element\n197\u2192simulation for objective evaluation while still providing principled,\n198\u2192multi-objective design recommendations. The same closed-loop pattern is\n199\u2192expected to transfer to other multi-material additively manufactured\n200\u2192architectures whose performance is dominated by hard-to-simulate\n201\u2192effects.\n202\u2192\n203\u2192{\\tiny\n204\u2192\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205\u2192\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206\u2192% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207\u2192% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208\u2192\\providecommand{\\doi}[1]{}\n209\u2192\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210\u2192\\bibliography{references}\n211\u2192}\n212\u2192\n213\u2192\\end{document}", + "name": "read", + "tool_call_id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "info": { + "start_ts": 8147.332633466, + "end_ts": 8147.339043095 + } + } + ], + "next_observation": [ + { + "role": "tool", + "content": "175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "info": { + "start_ts": 8153.825818486, + "end_ts": 8153.828437556 + } + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "callback_type": "after_env_step" + }, + "action": { + "call_id": { + "run_id": "f8b11daf-a506-414f-887c-a22c881e34cd", + "fwd_id": "38d5e2c5-ee05-46de-9864-cfd697621798" + }, + "op_name": "IdentityOp-10b149563727", + "op_class_name": "ldp.graph.common_ops.IdentityOp", + "value": { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\", \"offset\": 174, \"limit\": 100}", + "name": "read" + } + } + ] + }, + "logprob": null + } + } + }, + "created_at": "2026-07-26T02:29:26.688619Z" + }, + { + "id": "31b3d363-d46c-42ee-95b7-e94ebd0b6469", + "trajectory_id": "afade0b1-f49e-4031-a86d-cb5ec4652536", + "trajectory_timestep": 0, + "step": "after_agent_get_asv", + "state": { + "transition": { + "timestep": 0, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ], + "info": null + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Tensegrity Slides \u2014 Draft 1 (extracted content)\n 2\u2192\n 3\u2192Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4\u2192\n 5\u2192\n 6\u2192## Slide 1 \u2014 layout: Title Only\n 7\u2192\n 8\u2192**On-slide text:**\n 9\u2192- **Title 1**: Let\u2019s build better tensegrity structures faster\n 10\u2192- **TextBox 4**: Marcus Madsen\n 11\u2192Undergraduate researcher under Sterling Baird, Ph.D.\n 12\u2192- **TextBox 6**: IDETC 2026\n 13\u2192\n 14\u2192**Non-text shapes / media / placeholders:**\n 15\u2192- [PICTURE (13)] Picture 3\n 16\u2192- [PICTURE (13)] Picture 8\n 17\u2192\n 18\u2192**Presenter notes:**\n 19\u2192> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20\u2192> \n 21\u2192> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22\u2192\n 23\u2192\n 24\u2192## Slide 2 \u2014 layout: Title Only\n 25\u2192\n 26\u2192**On-slide text:**\n 27\u2192- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings\u2014and to problems closer to home.\n 28\u2192\n 29\u2192**Non-text shapes / media / placeholders:**\n 30\u2192- [MEDIA (16)] Online Media 5\n 31\u2192\n 32\u2192**Presenter notes:**\n 33\u2192> Replace this video with a downloaded and trimmed version of just the fall. \n 34\u2192> \n 35\u2192> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle \u2014 thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander\u00a0is\u00a0the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit\u00a0and get back up\u00a0\u2014 it survives multiple drops and keeps working.\n 36\u2192\n 37\u2192\n 38\u2192## Slide 3 (HIDDEN) \u2014 layout: Title Only\n 39\u2192\n 40\u2192**On-slide text:**\n 41\u2192- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42\u2192- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43\u2192- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44\u2192\n 45\u2192**Presenter notes:**\n 46\u2192> This might be better explained purely vocally while on the previous slide.\n 47\u2192\n 48\u2192\n 49\u2192## Slide 4 \u2014 layout: Title Only\n 50\u2192\n 51\u2192**On-slide text:**\n 52\u2192- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53\u2192- **TextBox 3**: Iteration\n 54\u2192- **TextBox 5**: Building\n 55\u2192- **TextBox 7**: Testing\n 56\u2192\n 57\u2192**Non-text shapes / media / placeholders:**\n 58\u2192- [MEDIA (16)] AdobeStock_207717222\n 59\u2192- [MEDIA (16)] Online Media 2\n 60\u2192- [PICTURE (13)] Picture 14\n 61\u2192- [PICTURE (13)] Picture 16\n 62\u2192- [PICTURE (13)] Picture 18\n 63\u2192\n 64\u2192**Presenter notes:**\n 65\u2192> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn\u2019t become distracting because it\u2019s fun to watch it spin.\n 66\u2192> \n 67\u2192> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68\u2192\n 69\u2192\n 70\u2192## Slide 5 (HIDDEN) \u2014 layout: Title Only\n 71\u2192\n 72\u2192**On-slide text:**\n 73\u2192- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74\u2192- **TextBox 14**: Baird et al. Matter (2022)\n 75\u2192\n 76\u2192**Non-text shapes / media / placeholders:**\n 77\u2192- [GROUP (6)] Group 2\n 78\u2192\n 79\u2192**Presenter notes:**\n 80\u2192> Consult Sterling on this slide. I don\u2019t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it\u2019s well enough known that it\u2019d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81\u2192\n 82\u2192\n 83\u2192## Slide 6 (HIDDEN) \u2014 layout: Title Only\n 84\u2192\n 85\u2192**On-slide text:**\n 86\u2192- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87\u2192- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88\u2192\n 89\u2192**Presenter notes:**\n 90\u2192> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91\u2192\n 92\u2192\n 93\u2192## Slide 7 \u2014 layout: Title Only\n 94\u2192\n 95\u2192**On-slide text:**\n 96\u2192- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97\u2192- **TextBox 5**: 6 variables\n 98\u2192- **TextBox 7**: 2 types of input data\n 99\u2192- **TextBox 9**: 2 objectives\n100\u2192- **TextBox 17**: *image of blackbox*\n101\u2192\n102\u2192**Non-text shapes / media / placeholders:**\n103\u2192- [LINE (9)] Connector: Curved 11\n104\u2192- [LINE (9)] Connector: Curved 13\n105\u2192- [LINE (9)] Connector: Curved 15\n106\u2192\n107\u2192**Presenter notes:**\n108\u2192> Very rough slide as I\u2019m not entirely sure what graphic or text to put on this, partially because I\u2019m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109\u2192\n110\u2192\n111\u2192## Slide 8 \u2014 layout: Title Only\n112\u2192\n113\u2192**On-slide text:**\n114\u2192- **Title 1**: Multi-material additive manufacturing supplies that data fast \u2014 single-build co-fabrication, with no joining of parts.\n115\u2192\n116\u2192**Non-text shapes / media / placeholders:**\n117\u2192- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118\u2192- [PICTURE (13)] Picture 4\n119\u2192\n120\u2192**Presenter notes:**\n121\u2192> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122\u2192\n123\u2192\n124\u2192## Slide 9 \u2014 layout: Title Only\n125\u2192\n126\u2192**On-slide text:**\n127\u2192- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128\u2192- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129\u2192- **TextBox 5**: *placeholder for slo motion of drop test*\n130\u2192\n131\u2192\n132\u2192## Slide 10 \u2014 layout: Title Only\n133\u2192\n134\u2192**On-slide text:**\n135\u2192- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136\u2192- **TextBox 3**: Printing\n137\u2192- **TextBox 5**: Testing\n138\u2192- **TextBox 7**: BO feed\n139\u2192\n140\u2192**Non-text shapes / media / placeholders:**\n141\u2192- [PICTURE (13)] Picture 14\n142\u2192- [PICTURE (13)] Picture 16\n143\u2192- [PICTURE (13)] Picture 18\n144\u2192- [PICTURE (13)] Graphic 8\n145\u2192\n146\u2192**Presenter notes:**\n147\u2192> I think it might be better if we replaced \u201cprinting\u201d with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148\u2192\n149\u2192\n150\u2192## Slide 11 (HIDDEN) \u2014 layout: Title Only\n151\u2192\n152\u2192**On-slide text:**\n153\u2192- **Title 1**: Reserved for results\n154\u2192\n155\u2192**Presenter notes:**\n156\u2192> Would be helpful if we could show that this process works in this case.\n157\u2192\n158\u2192\n159\u2192## Slide 12 \u2014 layout: Title Only\n160\u2192\n161\u2192**On-slide text:**\n162\u2192- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163\u2192- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164\u2192- **TextBox 3**: *image to show lattice structure\n165\u2192\n166\u2192\n167\u2192## Slide 13 \u2014 layout: Title Only\n168\u2192\n169\u2192**Non-text shapes / media / placeholders:**\n170\u2192- [PICTURE (13)] Picture 3\n171\u2192\n172\u2192**Presenter notes:**\n173\u2192> Slide for questions at the end\n174\u2192\n175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "info": { + "start_ts": 8147.330900226, + "end_ts": 8147.337548875 + } + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Presentation \u2014 Doumont Presentation Structure Template\n 2\u2192\n 3\u2192Transcribed from Jean-luc Doumont's presentation-structure template\n 4\u2192([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5\u2192adapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\n 6\u2192All rights reserved. Can be downloaded from .\n 7\u2192\n 8\u2192> To design your presentation, write down your ideas for each component below.\n 9\u2192> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10\u2192\n 11\u2192**Draft 3** \u2014 applies the inline review comments from @sgbaird on PR #84 and the\n 12\u2192priority revisions from the\n 13\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14\u2192Summaries of what changed are at the bottom of this file\n 15\u2192([Draft 2 \u2192 3](#changes-from-draft-2), [Draft 1 \u2192 2](#changes-from-draft-1)).\n 16\u2192\n 17\u2192**Scope note (frame everything below with this):** the study presented is the\n 18\u2192optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19\u2192processing parameters** (e.g., nozzle temperature, print speed). PLA\u2013TPU is a\n 20\u2192**proxy system** \u2014 not flight material \u2014 used to prototype a self-driving-lab\n 21\u2192style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22\u2192The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23\u2192beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24\u2192\n 25\u2192## Opening\n 26\u2192\n 27\u2192### Attention getter\n 28\u2192\n 29\u2192*A way to lead the audience to the need efficiently*\n 30\u2192\n 31\u2192Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32\u2192landings \u2014 and to problems closer to home.\n 33\u2192\n 34\u2192One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35\u2192clip) do the work:\n 36\u2192\n 37\u2192> Landing a payload on another planet means surviving an impact in conditions\n 38\u2192> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n 39\u2192> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40\u2192> structure: the lander *is* the energy absorber. And unlike crushable\n 41\u2192> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42\u2192> hit **and get back up** \u2014 it survives multiple drops and keeps working.\n 43\u2192\n 44\u2192Candidate Super Ball Bot videos to show (verified links):\n 45\u2192\n 46\u2192- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n 47\u2192 (official channel); the canonical overview.\n 48\u2192- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49\u2192 \u2014 Wall Street Journal; prototype drop footage.\n 50\u2192- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51\u2192 \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n 52\u2192\n 53\u2192Immediately after the hook, state the present study scope in one sentence\n 54\u2192(per the scope note above): *we study a printed PLA\u2013TPU T3 prism as a proxy\n 55\u2192system for prototyping an autonomous tensegrity-optimization workflow* \u2014 so\n 56\u2192the planetary framing never outruns the demonstrated scope.\n 57\u2192\n 58\u2192\n 60\u2192\n 61\u2192### Need\n 62\u2192\n 63\u2192*A difference between actual and desired situations*\n 64\u2192\n 65\u2192**Actual situation** (a two-sided pincer \u2014 stated as a scoped engineering\n 66\u2192judgment, not an absolute):\n 67\u2192\n 68\u2192- For these structures, obtaining trustworthy objective values from a\n 69\u2192 validated high-fidelity model would require substantial calibration of\n 70\u2192 interface, rate, contact, and defect behavior \u2014 and even heavy-duty\n 71\u2192 simulation falls short of the experiments we are running: peak transmitted\n 72\u2192 shock is a millisecond-scale transient that is notoriously difficult to\n 73\u2192 resolve numerically. This holds even for idealized cable\u2013strut tensegrities\n 74\u2192 in their actual engineering materials (steel or similar), not just for\n 75\u2192 printed PLA\u2013TPU.\n 76\u2192 \n 79\u2192- Exhaustive physical search fails on cost: a traditional design of\n 80\u2192 experiments over this relatively high-dimensional space (geometry + FDM\n 81\u2192 process parameters) would demand hundreds of specimens, each costing print\n 82\u2192 and test time.\n 83\u2192- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84\u2192 more complex structures \u2014 especially tensegrity *lattices* \u2014 become\n 85\u2192 extremely difficult and ultimately infeasible to hand-build.\n 86\u2192\n 87\u2192**Desired situation:**\n 88\u2192\n 89\u2192- Trustworthy performance data without hand assembly (easy creation of\n 90\u2192 tensegrity structures, scalable beyond a single prism).\n 91\u2192- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n 92\u2192 not the hundreds a traditional design of experiments would require.\n 93\u2192\n 94\u2192### Task\n 95\u2192\n 96\u2192*What I decided/was asked to do to address the need*\n 97\u2192\n 98\u2192Build a closed-loop design\u2013print\u2013test workflow that optimizes a **T3-prism\n 99\u2192tensegrity-inspired energy absorber together with its FDM processing\n100\u2192parameters** (e.g., temperature, print speed) directly from real, measured\n101\u2192impact data \u2014 maximizing energy absorbed per gram (specific energy\n102\u2192absorption) and compaction efficiency, subject to a cap on the peak force\n103\u2192transmitted to the payload. Extensions beyond the T3 prism are future work.\n104\u2192\n105\u2192### Main message\n106\u2192\n107\u2192*The one sentence I want my audience to remember*\n108\u2192\n109\u2192By closing the loop between multi-material 3D printing and Bayesian\n110\u2192optimization, we can optimize tensegrity-inspired energy absorbers directly\n111\u2192from real impact data \u2014 in dozens of prints, not the hundreds a traditional\n112\u2192design of experiments would demand.\n113\u2192\n114\u2192\n118\u2192\n119\u2192### Preview\n120\u2192\n121\u2192*A map of the body (ideally three points, max. five)*\n122\u2192\n123\u21921. When model calibration and exhaustive testing are both costly, each\n124\u2192 physical specimen must be chosen for its information value \u2014 so the\n125\u2192 measured experiment is our source of truth.\n126\u21922. Bayesian optimization makes physical experimentation affordable: it handles\n127\u2192 noisy measurements, small batches, and the constrained multi-objective\n128\u2192 trade-off \u2014 and Honegumi let us scaffold it in minutes.\n129\u21923. Multi-material additive manufacturing closes the loop: single-build PLA\u2013TPU\n130\u2192 co-fabrication turns each proposed design into test data in hours, with no\n131\u2192 joining of parts.\n132\u21924. *(Optional fourth point, if time allows)* The same closed loop points at\n133\u2192 what comes next: tensegrity lattices, flight-relevant materials, and\n134\u2192 safety hardware on Earth.\n135\u2192\n136\u2192## Body\n137\u2192\n138\u2192### Point 1\n139\u2192\n140\u2192When model calibration and exhaustive testing are both costly, each physical\n141\u2192specimen must be chosen for its information value.\n142\u2192\n143\u2192- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144\u2192 these structures, a sufficiently calibrated multiphysics model is expensive\n145\u2192 to build relative to the design campaign itself \u2014 and the hardest quantity,\n146\u2192 peak transmitted shock, is a millisecond-scale transient that even\n147\u2192 heavy-duty simulation struggles to resolve. The gap persists in the actual\n148\u2192 engineering materials a real lander would use (steel struts and cables),\n149\u2192 so this is not merely an artifact of printed PLA\u2013TPU.\n150\u2192- The alternative \u2014 sweeping the design space physically \u2014 fails on cost: a\n151\u2192 traditional design of experiments over geometry plus FDM process parameters\n152\u2192 would take hundreds of specimens, each needing print + test time.\n153\u2192- Therefore: treat the physical experiment as the authoritative source of\n154\u2192 objective values, and be ruthless about *which* experiments to run.\n155\u2192- Anticipate the FEA-veteran pushback (\"inconvenient \u2260 untrustworthy\"): frame\n156\u2192 it as a cost-and-dependence decision, and keep a backup slide with the\n157\u2192 literature (or our own validation) on simulation\u2013experiment discrepancy for\n158\u2192 impact transients.\n159\u2192\n160\u2192### Transition\n161\u2192\n162\u2192If every data point must be a real experiment, we need a method that extracts\n163\u2192the most from every specimen \u2014 we need to make the process radically more\n164\u2192sample-efficient.\n165\u2192\n166\u2192### Point 2\n167\u2192\n168\u2192Bayesian optimization makes physical experimentation affordable, even with\n169\u2192limited, noisy data.\n170\u2192\n171\u2192- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172\u2192 visual in the main talk; implementation detail goes to backup slides.\n173\u2192 Spoken version:\n174\u2192 > \"After each batch, the model estimates both expected performance and\n175\u2192 > uncertainty across the design space. The acquisition rule selects a small\n176\u2192 > next batch that is most likely to expand the feasible trade-off frontier,\n177\u2192 > while accounting for noisy tests and print failures.\"\n178\u2192- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179\u2192 motivate a noise-aware acquisition function (qNEHVI) rather than\n180\u2192 non-noise-aware alternatives. (Backup slide: how observation noise is\n181\u2192 actually modeled \u2014 replicates, noise identification \u2014 and budget-matched\n182\u2192 baselines such as Sobol/random sampling.)\n183\u2192- Small parallel batches: the surrogate proposes the next batch of specimens\n184\u2192 to print, balancing exploration and exploitation.\n185\u2192- Constrained multi-objective by construction: **objectives** = maximize\n186\u2192 specific energy absorption and compaction efficiency; **constraint** = peak\n187\u2192 transmitted force must stay under a cap; the deliverable is the *feasible*\n188\u2192 Pareto front between the two objectives among force-cap-satisfying designs.\n189\u2192 (Keep this formulation consistent everywhere \u2014 do not present \"SEA vs. peak\n190\u2192 force\" as the trade-off.)\n191\u2192- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192\u2192 this advanced task (noisy, batched, constrained, multi-objective) in\n193\u2192 minutes using [Honegumi](https://honegumi.readthedocs.io/) \u2014 the audience\n194\u2192 can do the same for their own problems; show the code-template picker\n195\u2192 briefly so they see how low the barrier is.\n196\u2192\n197\u2192### Transition\n198\u2192\n199\u2192The optimizer is no longer the bottleneck \u2014 the limit is now providing real\n200\u2192data quickly enough.\n201\u2192\n202\u2192### Point 3\n203\u2192\n204\u2192Multi-material additive manufacturing supplies that data fast \u2014 single-build\n205\u2192co-fabrication, with no joining of parts.\n206\u2192\n207\u2192- Single-build PLA\u2013TPU co-fabrication: rigid struts and flexible tension\n208\u2192 network in one print \u2014 no joining or hand assembly of members, which closes\n209\u2192 the \"easy creation\" gap from the Need and is what makes future lattices\n210\u2192 feasible at all. (Be precise: if support removal, trimming, or conditioning\n211\u2192 remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212\u2192 assembly.\")\n213\u2192- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214\u2192 as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215\u2192 pre-tension status) plus a short deformation sequence \u2014 so the EDL engineer\n216\u2192 doesn't file it as a \"crush structure\" and the grad student can tell it from\n217\u2192 an ordinary lattice.\n218\u2192- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219\u2192 tested specimen (quasi-static compression + instrumented drop-weight impact)\n220\u2192 in hours. (Backup slide: representative print-to-result time breakdown and\n221\u2192 which steps remain manual \u2014 candidate selection is automated; slicing,\n222\u2192 handling, and testing are not yet.)\n223\u2192- Now we have the data we need to complete the loop.\n224\u2192\n225\u2192### Point 4 (optional \u2014 future applications)\n226\u2192\n227\u2192*Include if timing allows; otherwise fold into the Conclusion.*\n228\u2192\n229\u2192The same closed loop transfers wherever tests are authoritative, each test is\n230\u2192costly, the design space is mixed, and several performance goals conflict:\n231\u2192\n232\u2192- Tensegrity *lattices* and more complex architectures \u2014 exactly where hand\n233\u2192 assembly becomes infeasible and single-build co-fabrication pays off.\n234\u2192- Migration from the PLA\u2013TPU proxy toward flight-relevant materials and\n235\u2192 impact regimes.\n236\u2192- Safety hardware on Earth (e.g., the tensegrity crutch tip \u2014 future work).\n237\u2192- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238\u2192 handling, testing) in the loop.\n239\u2192\n240\u2192### Evidence / results\n241\u2192\n242\u2192*What we found \u2014 ground the conclusion in shown data*\n243\u2192\n244\u2192Build the talk around a quantitative three-slide sequence (per the\n245\u2192mock-audience report; insert observed values once the campaign completes):\n246\u2192\n247\u21921. **Campaign ledger** \u2014 \"The campaign used N specimens across B adaptive\n248\u2192 batches under a fixed physical-test budget\": initialization vs. adaptive\n249\u2192 batches, replicates, print failures, cycle time, and the numerical value\n250\u2192 (and rationale) of the force cap.\n251\u21922. **Budget-matched baseline** \u2014 \"Adaptive selection improved the feasible\n252\u2192 design set faster than [predeclared baseline]\": performance vs. number of\n253\u2192 physical tests against Sobol/random sampling at the same budget, with\n254\u2192 uncertainty where available.\n255\u21923. **Measured feasible Pareto front** \u2014 specific energy absorption vs.\n256\u2192 compaction efficiency among designs satisfying the force cap, with\n257\u2192 replicate spread, marked infeasible designs, and specimen photos or\n258\u2192 force\u2013time traces for representative Pareto designs.\n259\u2192\n260\u2192Supporting raw-data candidates: drop-test force\u2013time traces, Sobol /\n261\u2192first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262\u2192\n263\u2192### Suggested 15-minute allocation\n264\u2192\n265\u2192*(From the mock-audience report \u2014 results get the largest block.)*\n266\u2192\n267\u2192| Time | Content |\n268\u2192|---|---|\n269\u2192| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270\u2192| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271\u2192| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272\u2192| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273\u2192| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274\u2192| 1 min | Limitations and transfer conditions |\n275\u2192| 1 min | Measured conclusion + bookend |\n276\u2192\n277\u2192Move kernel choices, encodings, and other implementation detail to backup\n278\u2192slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279\u2192[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280\u2192\n281\u2192## Closing\n282\u2192\n283\u2192### Review\n284\u2192\n285\u2192*A recap of the body, leading into the conclusion*\n286\u2192\n287\u2192Using real, measured impact data from additively manufactured\n288\u2192tensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\n289\u2192optimization routine \u2014 we rapidly iterate toward better energy absorbers.\n290\u2192This feedback loop makes tensegrity optimization dramatically faster: dozens\n291\u2192of prints instead of the hundreds a traditional design of experiments over\n292\u2192this design space would require.\n293\u2192\n294\u2192### Conclusion\n295\u2192\n296\u2192*What the above means to the audience in the end*\n297\u2192\n298\u2192Because the loop runs on physical measurements, it is useful precisely where\n299\u2192model calibration is not worth its cost \u2014 and the same closed-loop pattern\n300\u2192should transfer (a conditional claim, pending evidence) to other additively\n301\u2192manufactured architectures whose performance is dominated by\n302\u2192hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303\u2192*use this loop when tests are authoritative, each test is costly, the design\n304\u2192space is mixed, and several performance goals conflict.* For tensegrity\n305\u2192specifically, it opens a path from this T3 proxy study toward lattices,\n306\u2192flight-relevant materials, payload protection on other planets, and safety\n307\u2192hardware here on Earth (e.g., the crutch tip, as future work).\n308\u2192\n309\u2192### Close\n310\u2192\n311\u2192*A way to end the presentation clearly and elegantly*\n312\u2192\n313\u2192Return to the opening image: the next planetary lander's energy absorber \u2014 a\n314\u2192structure that has to survive not one impact but many \u2014 doesn't have to take\n315\u2192years of hand-built prototypes; with this loop, candidate designs can be\n316\u2192designed, printed, and tested against real impacts in weeks instead of months\n317\u2192or longer. End on the measured engineering result and its transfer condition,\n318\u2192then the bookend: every method that compresses design time moves our ventures\n319\u2192\u2014 on this planet and beyond \u2014 forward.\n320\u2192\n321\u2192---\n322\u2192\n323\u2192## Changes from Draft 2\n324\u2192\n325\u2192Applied from @sgbaird's inline review comments on PR #84 and the\n326\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327\u2192\n328\u2192**From the inline review comments:**\n329\u2192\n330\u21921. **Reusability in the hook** (line 22): the tensegrity lander survives\n331\u2192 multiple drops, unlike crushable/sacrificial alternatives.\n332\u21922. **Proxy-system framing** (lines 49, 87): PLA\u2013TPU would never fly; it is a\n333\u2192 proxy for prototyping a self-driving-lab-style workflow \u2014 stated in a\n334\u2192 scope note up front and re-stated right after the hook.\n335\u21923. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336\u2192 heavy-duty simulation falls short of the experiments (millisecond-scale\n337\u2192 peak shock), not that it merely costs too much.\n338\u21924. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339\u2192 complex structures and especially lattices are not.\n340\u21925. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341\u2192 (temperature, print speed, \u2026) only; the rest is explicitly future work.\n342\u21926. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343\u2192 literature search (or validation of experiments against simulation), plus\n344\u2192 the millisecond-resolution peak-shock argument.\n345\u21927. **Optional Point 4 added** (line 97): future applications, with a note it\n346\u2192 can fold into the Conclusion if time is short.\n347\u21928. **Steel-materials point** (line 104): simulations fall short for the\n348\u2192 actual engineering materials (steel struts/cables), not just printed\n349\u2192 PLA\u2013TPU.\n350\u21929. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351\u2192 in minutes, pitched so the audience sees they can use it too.\n352\u219210. **\"Thousands\" \u2192 \"hundreds\"** (line 173): baseline is now a traditional\n353\u2192 design of experiments over a high-dimensional space (hundreds), in the\n354\u2192 main message, Need, Point 1, and Review.\n355\u219211. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356\u2192 or longer\" for candidate designs.\n357\u2192\n358\u2192**From the mock-audience report's priority revisions:**\n359\u2192\n360\u219212. **Quantitative three-slide results sequence** (Priority 1): campaign\n361\u2192 ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front, with a\n362\u2192 fallback main-message wording if the campaign can't yet support the\n363\u2192 efficiency claim.\n364\u219213. **Anti-simulation premise replaced with a scoped cost decision**\n365\u2192 (Priority 2): Point 1 retitled to the information-value message;\n366\u2192 \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367\u2192 anticipated with a backup slide.\n368\u219214. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369\u2192 implementation detail moved to backup slides; Q&A prep pointed at the\n370\u2192 report's claims-vs-evidence table.\n371\u219215. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372\u2192 the main talk, machinery in backup slides; the misleading\n373\u2192 \"qNEHVI over standard expected improvement\" comparison replaced with\n374\u2192 \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375\u219216. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376\u2192 sequence in Point 3, so the structure isn't remembered as a generic crush\n377\u2192 structure or lattice.\n378\u219217. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379\u2192 co-fabrication / no joining of parts,\" with a note to list remaining\n380\u2192 manual steps and an automation-status caveat.\n381\u219218. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382\u2192 compaction efficiency, constraint = force cap, deliverable = *feasible*\n383\u2192 Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384\u219219. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385\u2192 transfer claim marked conditional, and the close ends on the measured\n386\u2192 result before the bookend.\n387\u2192\n388\u2192## Changes from Draft 1\n389\u2192\n390\u2192Feedback from the PR #84 review, applied in Draft 2:\n391\u2192\n392\u21921. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n393\u2192 dozens-not-thousands claim); body and message now carry the same story.\n394\u21922. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n395\u2192 manuscript's positioning (#75/#76) is that the physical experiment is the\n396\u2192 source of truth, explicitly avoiding calibrated FE simulation.\n397\u21923. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n398\u2192 parametric design space and avoid an over-claim to a design-automation\n399\u2192 audience.\n400\u21924. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401\u2192 energy absorption and compaction efficiency, cap peak transmitted force.\n402\u21925. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403\u2192 *and* the space is too big to brute-force \u2014 which makes BO + AM the\n404\u2192 inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405\u2192 above.)*\n406\u21926. **Point 1 given its own content** (the *why* behind the inefficiency)\n407\u2192 instead of restating the Need.\n408\u21927. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409\u2192 the specifics (noise-aware acquisition, small batches, multi-objective).\n410\u21928. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411\u2192 (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412\u2192 above.)*\n413\u21929. **Evidence/results slot added** before the Closing, so the Conclusion is\n414\u2192 grounded in shown data rather than three hedged \"may\"s.\n415\u219210. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n416\u2192 Ball Bot visual; verified video links included.\n417\u219211. **Close bookends the opening** (the next lander's absorber, designed in\n418\u2192 weeks) before the broader humankind line.\n419\u219212. **Spelling**: Doumont (was \"Duomont\").", + "name": "read", + "tool_call_id": "call_93haurOMDGiH2mQFFbaMaYKn", + "info": { + "start_ts": 8147.331065886, + "end_ts": 8147.336467845 + } + }, + { + "role": "tool", + "content": " 1\u2192# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n 2\u2192\n 3\u2192Video: (assigned in #83)\n 4\u2192\n 5\u2192Transcribed from @me-madsen's handwritten notes\n 6\u2192([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7\u2192dated 2026-07-15).\n 8\u2192\n 9\u2192## Overview\n10\u2192\n11\u2192A presentation is all about the right content, displayed the right way, at the\n12\u2192right time.\n13\u2192\n14\u2192- Verbal communication is sequential.\n15\u2192- Message is *interpretation* of the information.\n16\u2192 - Ensure the audience can get the most out of what you're doing.\n17\u2192 - Don't just give them information \u2014 help them understand it, interpret it,\n18\u2192 and internalize it.\n19\u2192\n20\u2192## Doumont's suggested rules for a presentation\n21\u2192\n22\u21920. Ensure you have **messages** for your audience, not just words.\n23\u21921. **Adapt to your audience.**\n24\u2192 - Think like the audience, not the speaker.\n25\u21922. **Maximize signal-to-noise ratio.**\n26\u2192 - Reduce noise (animations, laser pointers, anything that pulls attention\n27\u2192 away from your message).\n28\u21923. **Use effective redundancy.**\n29\u2192 - Tell your message in word *and* on slide: someone deaf should understand\n30\u2192 your messages just as well as someone blind.\n31\u2192 - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32\u2192 date).\n33\u2192\n34\u2192## When creating slides\n35\u2192\n36\u2192- If the audience doesn't need it, why put it on the slides?\n37\u2192- People shouldn't have to count the items on a slide to know how many are\n38\u2192 there.\n39\u2192- Don't use your slides as your \"presentation notes.\"\n40\u2192 - Don't show your notes to the audience.\n41\u2192 - **A bad slide is worse than no slide at all.**\n42\u2192- **One message per slide** is a good rule of thumb.\n43\u2192 - Prioritize the *so what* (or *therefore what*) of your message.\n44\u2192- Make it easy to interpret the information on the slide:\n45\u2192 - 3\u20135 items at most.\n46\u2192 - Align the title left.\n47\u2192 - Do not clutter slides with repetitive or unnecessary information: logos,\n48\u2192 affiliation, date, slide numbers.\n49\u2192- Doumont's suggested workflow:\n50\u2192 1. **Start with the title.**\n51\u2192 - Use a full sentence in the title.\n52\u2192 - The title is the main message of the slide.\n53\u2192 - Make it easy to read: left-aligned, with intentional line breaks.\n54\u2192 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55\u2192- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56\u2192\n57\u2192## General preparation\n58\u2192\n59\u2192- Planning for audience, content, and structure matters more than materials\n60\u2192 (i.e., slides).\n61\u2192- Try using one sheet of paper for presentation preparation.\n62\u2192- It's most important that your presentation is:\n63\u2192 - well planned, structured, and delivered;\n64\u2192 - **if you don't have a lot of time to prepare, don't focus on making\n65\u2192 slides.**\n66\u2192- Practice the presentation a few times. Practice at least once *without* your\n67\u2192 slideshow!\n68\u2192- If doing a handout, keep it separate from the slides you're preparing.\n69\u2192- Impress the audience with your body.\n70\u2192 - Use gestures, not laser pointers.\n71\u2192 - \"Control\" the audience with your body \u2014 keep their attention on your\n72\u2192 presentation.\n73\u2192\n74\u2192## Impressions / what to apply to the IDETC presentation\n75\u2192\n76\u2192\n77\u2192\n78\u2192## Questions / things to discuss\n79\u2192\n80\u2192", + "name": "read", + "tool_call_id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "info": { + "start_ts": 8147.331133416, + "end_ts": 8147.338304015 + } + }, + { + "role": "tool", + "content": " 1\u2192\\documentclass[10pt]{article}\n 2\u2192\n 3\u2192% --- Packages ---\n 4\u2192\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5\u2192\\usepackage[hidelinks]{hyperref}\n 6\u2192\\usepackage{parskip}\n 7\u2192\\usepackage{graphicx}\n 8\u2192\\usepackage{caption}\n 9\u2192\\usepackage{titlesec}\n 10\u2192\\usepackage{wrapfig}\n 11\u2192\\usepackage[numbers,sort&compress]{natbib}\n 12\u2192\n 13\u2192% --- Formatting ---\n 14\u2192\\setlength{\\parskip}{0.2em}\n 15\u2192\\setlength{\\parindent}{0em}\n 16\u2192\\titleformat*{\\section}{\\large\\bfseries}\n 17\u2192\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18\u2192\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19\u2192\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20\u2192\\setlength{\\emergencystretch}{2em}\n 21\u2192\\setlength{\\intextsep}{0.2em}\n 22\u2192\\bibliographystyle{abbrvnat}\n 23\u2192\n 24\u2192\\begin{document}\n 25\u2192\n 26\u2192\\begin{center}\n 27\u2192{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28\u21923D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29\u2192\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30\u2192\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31\u2192\\end{center}\n 32\u2192\n 33\u2192\\vspace{0.4em}\n 34\u2192\n 35\u2192\\section*{Abstract}\n 36\u2192Tensegrity-inspired architectures---rigid struts suspended within a\n 37\u2192continuous flexible network---can exhibit tunable nonlinear\n 38\u2192force--displacement responses and favorable energy absorption per unit\n 39\u2192mass, motivating their use in lightweight energy-absorbing and protective\n 40\u2192structures\n 41\u2192\\citep{skelton2009tensegrity,pajunen2019design}.\n 42\u2192Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43\u2192struts and flexible (TPU) elements in a single build\n 44\u2192\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45\u2192space---strut geometry, tension-element cross-section, connectivity\n 46\u2192topology, and unit-cell tiling---is too large to explore by trial and\n 47\u2192error. We present a closed-loop experimental campaign that uses\n 48\u2192Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49\u2192to drive a design--print--test workflow operating directly on physical\n 50\u2192measurements, without relying on calibrated finite-element simulation\n 51\u2192for objective evaluation.\n 52\u2192\n 53\u2192\\section{Introduction and Motivation}\n 54\u2192\n 55\u2192\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56\u2192\\centering\n 57\u2192\\vspace{-0.4em}\n 58\u2192\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59\u2192\\captionsetup{font=small}\n 60\u2192\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61\u2192Candidate tensegrity-inspired unit cells are instantiated from a\n 62\u2192parameter vector, fabricated by multi-material FDM, and tested under\n 63\u2192quasi-static compression and drop-weight impact. Measured peak force,\n 64\u2192specific energy absorption (SEA), and compaction efficiency update a\n 65\u2192Gaussian-process surrogate that proposes the next batch of designs.}\n 66\u2192\\label{fig:overview}\n 67\u2192\\vspace{-0.4em}\n 68\u2192\\end{wrapfigure}\n 69\u2192\n 70\u2192Tensegrity structures combine compression-only members with a\n 71\u2192pre-tensioned tension network to achieve stiffness without rigid\n 72\u2192joinery~\\citep{skelton2009tensegrity}. Their\n 73\u2192nonlinear, often load-limiting force--displacement response makes them\n 74\u2192attractive for impact mitigation, packaging, and wearable protective\n 75\u2192gear~\\citep{pajunen2019design}. Idealized\n 76\u2192cable--strut tensegrities are difficult to manufacture at scale, but\n 77\u2192\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78\u21923D printing reproduce key tensegrity behaviors---including\n 79\u2192post-buckling stability and load-limiting force--displacement\n 80\u2192response~\\citep{pajunen2019design}---motivating our extension to\n 81\u2192multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82\u2192elements. PLA provides rigid struts; TPU provides flexible,\n 83\u2192rate-dependent tension elements whose viscoelasticity is expected to\n 84\u2192contribute hysteretic energy dissipation under impact loading,\n 85\u2192consistent with the energy-absorbing behavior reported for rigid--TPU\n 86\u2192multi-material prints~\\citep{khatri2024energy}.\n 87\u2192\n 88\u2192The challenge is that small geometric and topological changes can\n 89\u2192produce large changes in the measured response, and high-fidelity\n 90\u2192simulation of FDM specimens is computationally expensive and often\n 91\u2192struggles to capture physical defects such as interfacial slip and\n 92\u2192TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93\u2192trial-and-error sweeps over the joint design space are also infeasible:\n 94\u2192even modest parameter resolutions yield thousands of candidates, each\n 95\u2192requiring print and test time. We instead treat the physical\n 96\u2192experiment as the source of truth and use BO to select which specimens\n 97\u2192are most informative to fabricate next---an approach used in related\n 98\u2192closed-loop materials and structures\n 99\u2192campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100\u2192Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101\u2192set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102\u2192designs using parallel, noisy multi-objective BO driven by measured\n103\u2192$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104\u2192\n105\u2192\\section{Approach}\n106\u2192\\subsection{Closed-Loop Workflow}\n107\u2192Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108\u2192proceeds in four steps: (i)~a parameterized unit-cell design is\n109\u2192instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110\u2192is sliced and printed on a multi-material FDM system; (iii)~it is\n111\u2192tested under quasi-static compression and instrumented drop-weight\n112\u2192impact; and (iv)~the resulting performance metrics update a\n113\u2192Gaussian-process (GP) surrogate, which a batch acquisition function\n114\u2192queries to recommend the next set of candidates. Surrogate updating\n115\u2192and experiment selection are automated; specimen handling, slicing,\n116\u2192and test setup remain manual.\n117\u2192\n118\u2192\\subsection{Design Space}\n119\u2192We parameterize a family of unit cells using a core-wrapping\n120\u2192architecture in which rigid PLA struts are enclosed by continuous TPU\n121\u2192skins; the wrapping rationale follows multi-material FDM strategies in\n122\u2192which rigid panels are wrapped by stretchable soft material to prevent\n123\u2192delamination and improve interfacial bonding at the rigid--flexible\n124\u2192interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125\u2192comprises continuous geometric variables (strut diameter and length,\n126\u2192tension-element width and thickness), an integer strut count per unit\n127\u2192cell, and two categorical variables (connectivity topology, unit-cell\n128\u2192tiling pattern). Categorical choices are encoded explicitly rather\n129\u2192than embedded in a continuous metric, and bounds on the continuous\n130\u2192variables are chosen to remain within the printer's resolution and\n131\u2192overhang limits and to keep specimens in a common form factor for the\n132\u2192test fixtures.\n133\u2192\n134\u2192\\subsection{Surrogate and Acquisition}\n135\u2192The GP surrogate is trained on three measured performance metrics:\n136\u2192peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137\u2192(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138\u2192objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139\u2192likelihood with priors to avoid pathological length scales at small $n$.\n140\u2192Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141\u2192interfacial defects motivates our use of q-noisy expected hypervolume\n142\u2192improvement (qNEHVI), which explicitly accounts for observation noise\n143\u2192in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144\u2192expected-improvement variants. Print failures are treated as a\n145\u2192probabilistic feasibility constraint: the acquisition score for each\n146\u2192candidate is its qNEHVI improvement weighted by the GP-estimated\n147\u2192probability of successful fabrication, following recent constrained\n148\u2192multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149\u2192Where applicable, we use numerically stable log-space variants of\n150\u2192improvement-based acquisitions to mitigate vanishing-gradient\n151\u2192pathologies as data and constraints\n152\u2192accumulate~\\citep{ament2023logei}.\n153\u2192\n154\u2192\\section{Methods}\n155\u2192\\subsection{Fabrication}\n156\u2192Specimens are fabricated on a multi-material FDM system capable of\n157\u2192co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158\u2192Print parameters (temperature, line width, infill, retraction) are\n159\u2192held fixed within a batch to isolate the effect of the design\n160\u2192variables. Each specimen is weighed and dimensionally inspected prior\n161\u2192to testing so that mass and geometric deviations are available as\n162\u2192covariates.\n163\u2192\n164\u2192\\subsection{Mechanical Testing}\n165\u2192Quasi-static compression is performed on a screw-driven load frame at\n166\u2192a fixed nominal strain rate, with force--displacement recorded to\n167\u2192densification. Drop-weight impact testing uses an instrumented tup at\n168\u2192a fixed impact energy; transmitted force is captured at high sample\n169\u2192rate, and specimens are imaged before and after impact to document\n170\u2192failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171\u2192unit mass, and compaction efficiency $\\eta$, defined consistently\n172\u2192across the campaign. A control specimen from an earlier batch is\n173\u2192retested periodically to monitor TPU batch-to-batch and printer drift.\n174\u2192\n175\u2192\\section{Expected Outcomes and Discussion}\n176\u2192We will report how BO-selected designs evolve across iterations, how\n177\u2192GP surrogate predictions and uncertainties change as data accumulate,\n178\u2192and which Pareto-efficient trade-offs emerge between SEA and peak\n179\u2192transmitted force within the explored design space. We will also\n180\u2192discuss practical lessons for operating the closed-loop workflow,\n181\u2192including handling of print failures, batch-to-batch variability in\n182\u2192TPU response, the influence of the rigid--flexible interface on\n183\u2192energy absorption, and the exploration--exploitation balance of the\n184\u2192acquisition function under realistic experimental noise. We expect\n185\u2192the campaign to clarify where physical experimentation is genuinely\n186\u2192required versus where simpler analytical or multi-fidelity surrogate\n187\u2192shortcuts~\\citep{mo2023accelerated} could be\n188\u2192substituted in future work.\n189\u2192\n190\u2192\\section{Conclusion}\n191\u2192This work demonstrates a partially automated, experiment-driven\n192\u2192closed-loop pipeline for designing multi-material 3D-printed\n193\u2192tensegrity-inspired energy absorbers, with surrogate updating and\n194\u2192candidate selection automated and fabrication and testing kept manual.\n195\u2192By updating a GP surrogate directly from physical measurements, the\n196\u2192workflow avoids direct dependence on calibrated finite-element\n197\u2192simulation for objective evaluation while still providing principled,\n198\u2192multi-objective design recommendations. The same closed-loop pattern is\n199\u2192expected to transfer to other multi-material additively manufactured\n200\u2192architectures whose performance is dominated by hard-to-simulate\n201\u2192effects.\n202\u2192\n203\u2192{\\tiny\n204\u2192\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205\u2192\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206\u2192% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207\u2192% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208\u2192\\providecommand{\\doi}[1]{}\n209\u2192\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210\u2192\\bibliography{references}\n211\u2192}\n212\u2192\n213\u2192\\end{document}", + "name": "read", + "tool_call_id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "info": { + "start_ts": 8147.332633466, + "end_ts": 8147.339043095 + } + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\", \"offset\": 174, \"limit\": 100}", + "name": "read" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "tool", + "content": " 1\u2192# IDETC Tensegrity Slides \u2014 Draft 1 (extracted content)\n 2\u2192\n 3\u2192Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4\u2192\n 5\u2192\n 6\u2192## Slide 1 \u2014 layout: Title Only\n 7\u2192\n 8\u2192**On-slide text:**\n 9\u2192- **Title 1**: Let\u2019s build better tensegrity structures faster\n 10\u2192- **TextBox 4**: Marcus Madsen\n 11\u2192Undergraduate researcher under Sterling Baird, Ph.D.\n 12\u2192- **TextBox 6**: IDETC 2026\n 13\u2192\n 14\u2192**Non-text shapes / media / placeholders:**\n 15\u2192- [PICTURE (13)] Picture 3\n 16\u2192- [PICTURE (13)] Picture 8\n 17\u2192\n 18\u2192**Presenter notes:**\n 19\u2192> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20\u2192> \n 21\u2192> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22\u2192\n 23\u2192\n 24\u2192## Slide 2 \u2014 layout: Title Only\n 25\u2192\n 26\u2192**On-slide text:**\n 27\u2192- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings\u2014and to problems closer to home.\n 28\u2192\n 29\u2192**Non-text shapes / media / placeholders:**\n 30\u2192- [MEDIA (16)] Online Media 5\n 31\u2192\n 32\u2192**Presenter notes:**\n 33\u2192> Replace this video with a downloaded and trimmed version of just the fall. \n 34\u2192> \n 35\u2192> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle \u2014 thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander\u00a0is\u00a0the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit\u00a0and get back up\u00a0\u2014 it survives multiple drops and keeps working.\n 36\u2192\n 37\u2192\n 38\u2192## Slide 3 (HIDDEN) \u2014 layout: Title Only\n 39\u2192\n 40\u2192**On-slide text:**\n 41\u2192- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42\u2192- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43\u2192- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44\u2192\n 45\u2192**Presenter notes:**\n 46\u2192> This might be better explained purely vocally while on the previous slide.\n 47\u2192\n 48\u2192\n 49\u2192## Slide 4 \u2014 layout: Title Only\n 50\u2192\n 51\u2192**On-slide text:**\n 52\u2192- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53\u2192- **TextBox 3**: Iteration\n 54\u2192- **TextBox 5**: Building\n 55\u2192- **TextBox 7**: Testing\n 56\u2192\n 57\u2192**Non-text shapes / media / placeholders:**\n 58\u2192- [MEDIA (16)] AdobeStock_207717222\n 59\u2192- [MEDIA (16)] Online Media 2\n 60\u2192- [PICTURE (13)] Picture 14\n 61\u2192- [PICTURE (13)] Picture 16\n 62\u2192- [PICTURE (13)] Picture 18\n 63\u2192\n 64\u2192**Presenter notes:**\n 65\u2192> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn\u2019t become distracting because it\u2019s fun to watch it spin.\n 66\u2192> \n 67\u2192> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68\u2192\n 69\u2192\n 70\u2192## Slide 5 (HIDDEN) \u2014 layout: Title Only\n 71\u2192\n 72\u2192**On-slide text:**\n 73\u2192- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74\u2192- **TextBox 14**: Baird et al. Matter (2022)\n 75\u2192\n 76\u2192**Non-text shapes / media / placeholders:**\n 77\u2192- [GROUP (6)] Group 2\n 78\u2192\n 79\u2192**Presenter notes:**\n 80\u2192> Consult Sterling on this slide. I don\u2019t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it\u2019s well enough known that it\u2019d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81\u2192\n 82\u2192\n 83\u2192## Slide 6 (HIDDEN) \u2014 layout: Title Only\n 84\u2192\n 85\u2192**On-slide text:**\n 86\u2192- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87\u2192- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88\u2192\n 89\u2192**Presenter notes:**\n 90\u2192> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91\u2192\n 92\u2192\n 93\u2192## Slide 7 \u2014 layout: Title Only\n 94\u2192\n 95\u2192**On-slide text:**\n 96\u2192- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97\u2192- **TextBox 5**: 6 variables\n 98\u2192- **TextBox 7**: 2 types of input data\n 99\u2192- **TextBox 9**: 2 objectives\n100\u2192- **TextBox 17**: *image of blackbox*\n101\u2192\n102\u2192**Non-text shapes / media / placeholders:**\n103\u2192- [LINE (9)] Connector: Curved 11\n104\u2192- [LINE (9)] Connector: Curved 13\n105\u2192- [LINE (9)] Connector: Curved 15\n106\u2192\n107\u2192**Presenter notes:**\n108\u2192> Very rough slide as I\u2019m not entirely sure what graphic or text to put on this, partially because I\u2019m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109\u2192\n110\u2192\n111\u2192## Slide 8 \u2014 layout: Title Only\n112\u2192\n113\u2192**On-slide text:**\n114\u2192- **Title 1**: Multi-material additive manufacturing supplies that data fast \u2014 single-build co-fabrication, with no joining of parts.\n115\u2192\n116\u2192**Non-text shapes / media / placeholders:**\n117\u2192- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118\u2192- [PICTURE (13)] Picture 4\n119\u2192\n120\u2192**Presenter notes:**\n121\u2192> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122\u2192\n123\u2192\n124\u2192## Slide 9 \u2014 layout: Title Only\n125\u2192\n126\u2192**On-slide text:**\n127\u2192- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128\u2192- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129\u2192- **TextBox 5**: *placeholder for slo motion of drop test*\n130\u2192\n131\u2192\n132\u2192## Slide 10 \u2014 layout: Title Only\n133\u2192\n134\u2192**On-slide text:**\n135\u2192- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136\u2192- **TextBox 3**: Printing\n137\u2192- **TextBox 5**: Testing\n138\u2192- **TextBox 7**: BO feed\n139\u2192\n140\u2192**Non-text shapes / media / placeholders:**\n141\u2192- [PICTURE (13)] Picture 14\n142\u2192- [PICTURE (13)] Picture 16\n143\u2192- [PICTURE (13)] Picture 18\n144\u2192- [PICTURE (13)] Graphic 8\n145\u2192\n146\u2192**Presenter notes:**\n147\u2192> I think it might be better if we replaced \u201cprinting\u201d with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148\u2192\n149\u2192\n150\u2192## Slide 11 (HIDDEN) \u2014 layout: Title Only\n151\u2192\n152\u2192**On-slide text:**\n153\u2192- **Title 1**: Reserved for results\n154\u2192\n155\u2192**Presenter notes:**\n156\u2192> Would be helpful if we could show that this process works in this case.\n157\u2192\n158\u2192\n159\u2192## Slide 12 \u2014 layout: Title Only\n160\u2192\n161\u2192**On-slide text:**\n162\u2192- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163\u2192- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164\u2192- **TextBox 3**: *image to show lattice structure\n165\u2192\n166\u2192\n167\u2192## Slide 13 \u2014 layout: Title Only\n168\u2192\n169\u2192**Non-text shapes / media / placeholders:**\n170\u2192- [PICTURE (13)] Picture 3\n171\u2192\n172\u2192**Presenter notes:**\n173\u2192> Slide for questions at the end\n174\u2192\n175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "info": { + "start_ts": 8147.330900226, + "end_ts": 8147.337548875 + } + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Presentation \u2014 Doumont Presentation Structure Template\n 2\u2192\n 3\u2192Transcribed from Jean-luc Doumont's presentation-structure template\n 4\u2192([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5\u2192adapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\n 6\u2192All rights reserved. Can be downloaded from .\n 7\u2192\n 8\u2192> To design your presentation, write down your ideas for each component below.\n 9\u2192> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10\u2192\n 11\u2192**Draft 3** \u2014 applies the inline review comments from @sgbaird on PR #84 and the\n 12\u2192priority revisions from the\n 13\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14\u2192Summaries of what changed are at the bottom of this file\n 15\u2192([Draft 2 \u2192 3](#changes-from-draft-2), [Draft 1 \u2192 2](#changes-from-draft-1)).\n 16\u2192\n 17\u2192**Scope note (frame everything below with this):** the study presented is the\n 18\u2192optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19\u2192processing parameters** (e.g., nozzle temperature, print speed). PLA\u2013TPU is a\n 20\u2192**proxy system** \u2014 not flight material \u2014 used to prototype a self-driving-lab\n 21\u2192style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22\u2192The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23\u2192beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24\u2192\n 25\u2192## Opening\n 26\u2192\n 27\u2192### Attention getter\n 28\u2192\n 29\u2192*A way to lead the audience to the need efficiently*\n 30\u2192\n 31\u2192Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32\u2192landings \u2014 and to problems closer to home.\n 33\u2192\n 34\u2192One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35\u2192clip) do the work:\n 36\u2192\n 37\u2192> Landing a payload on another planet means surviving an impact in conditions\n 38\u2192> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n 39\u2192> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40\u2192> structure: the lander *is* the energy absorber. And unlike crushable\n 41\u2192> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42\u2192> hit **and get back up** \u2014 it survives multiple drops and keeps working.\n 43\u2192\n 44\u2192Candidate Super Ball Bot videos to show (verified links):\n 45\u2192\n 46\u2192- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n 47\u2192 (official channel); the canonical overview.\n 48\u2192- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49\u2192 \u2014 Wall Street Journal; prototype drop footage.\n 50\u2192- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51\u2192 \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n 52\u2192\n 53\u2192Immediately after the hook, state the present study scope in one sentence\n 54\u2192(per the scope note above): *we study a printed PLA\u2013TPU T3 prism as a proxy\n 55\u2192system for prototyping an autonomous tensegrity-optimization workflow* \u2014 so\n 56\u2192the planetary framing never outruns the demonstrated scope.\n 57\u2192\n 58\u2192\n 60\u2192\n 61\u2192### Need\n 62\u2192\n 63\u2192*A difference between actual and desired situations*\n 64\u2192\n 65\u2192**Actual situation** (a two-sided pincer \u2014 stated as a scoped engineering\n 66\u2192judgment, not an absolute):\n 67\u2192\n 68\u2192- For these structures, obtaining trustworthy objective values from a\n 69\u2192 validated high-fidelity model would require substantial calibration of\n 70\u2192 interface, rate, contact, and defect behavior \u2014 and even heavy-duty\n 71\u2192 simulation falls short of the experiments we are running: peak transmitted\n 72\u2192 shock is a millisecond-scale transient that is notoriously difficult to\n 73\u2192 resolve numerically. This holds even for idealized cable\u2013strut tensegrities\n 74\u2192 in their actual engineering materials (steel or similar), not just for\n 75\u2192 printed PLA\u2013TPU.\n 76\u2192 \n 79\u2192- Exhaustive physical search fails on cost: a traditional design of\n 80\u2192 experiments over this relatively high-dimensional space (geometry + FDM\n 81\u2192 process parameters) would demand hundreds of specimens, each costing print\n 82\u2192 and test time.\n 83\u2192- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84\u2192 more complex structures \u2014 especially tensegrity *lattices* \u2014 become\n 85\u2192 extremely difficult and ultimately infeasible to hand-build.\n 86\u2192\n 87\u2192**Desired situation:**\n 88\u2192\n 89\u2192- Trustworthy performance data without hand assembly (easy creation of\n 90\u2192 tensegrity structures, scalable beyond a single prism).\n 91\u2192- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n 92\u2192 not the hundreds a traditional design of experiments would require.\n 93\u2192\n 94\u2192### Task\n 95\u2192\n 96\u2192*What I decided/was asked to do to address the need*\n 97\u2192\n 98\u2192Build a closed-loop design\u2013print\u2013test workflow that optimizes a **T3-prism\n 99\u2192tensegrity-inspired energy absorber together with its FDM processing\n100\u2192parameters** (e.g., temperature, print speed) directly from real, measured\n101\u2192impact data \u2014 maximizing energy absorbed per gram (specific energy\n102\u2192absorption) and compaction efficiency, subject to a cap on the peak force\n103\u2192transmitted to the payload. Extensions beyond the T3 prism are future work.\n104\u2192\n105\u2192### Main message\n106\u2192\n107\u2192*The one sentence I want my audience to remember*\n108\u2192\n109\u2192By closing the loop between multi-material 3D printing and Bayesian\n110\u2192optimization, we can optimize tensegrity-inspired energy absorbers directly\n111\u2192from real impact data \u2014 in dozens of prints, not the hundreds a traditional\n112\u2192design of experiments would demand.\n113\u2192\n114\u2192\n118\u2192\n119\u2192### Preview\n120\u2192\n121\u2192*A map of the body (ideally three points, max. five)*\n122\u2192\n123\u21921. When model calibration and exhaustive testing are both costly, each\n124\u2192 physical specimen must be chosen for its information value \u2014 so the\n125\u2192 measured experiment is our source of truth.\n126\u21922. Bayesian optimization makes physical experimentation affordable: it handles\n127\u2192 noisy measurements, small batches, and the constrained multi-objective\n128\u2192 trade-off \u2014 and Honegumi let us scaffold it in minutes.\n129\u21923. Multi-material additive manufacturing closes the loop: single-build PLA\u2013TPU\n130\u2192 co-fabrication turns each proposed design into test data in hours, with no\n131\u2192 joining of parts.\n132\u21924. *(Optional fourth point, if time allows)* The same closed loop points at\n133\u2192 what comes next: tensegrity lattices, flight-relevant materials, and\n134\u2192 safety hardware on Earth.\n135\u2192\n136\u2192## Body\n137\u2192\n138\u2192### Point 1\n139\u2192\n140\u2192When model calibration and exhaustive testing are both costly, each physical\n141\u2192specimen must be chosen for its information value.\n142\u2192\n143\u2192- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144\u2192 these structures, a sufficiently calibrated multiphysics model is expensive\n145\u2192 to build relative to the design campaign itself \u2014 and the hardest quantity,\n146\u2192 peak transmitted shock, is a millisecond-scale transient that even\n147\u2192 heavy-duty simulation struggles to resolve. The gap persists in the actual\n148\u2192 engineering materials a real lander would use (steel struts and cables),\n149\u2192 so this is not merely an artifact of printed PLA\u2013TPU.\n150\u2192- The alternative \u2014 sweeping the design space physically \u2014 fails on cost: a\n151\u2192 traditional design of experiments over geometry plus FDM process parameters\n152\u2192 would take hundreds of specimens, each needing print + test time.\n153\u2192- Therefore: treat the physical experiment as the authoritative source of\n154\u2192 objective values, and be ruthless about *which* experiments to run.\n155\u2192- Anticipate the FEA-veteran pushback (\"inconvenient \u2260 untrustworthy\"): frame\n156\u2192 it as a cost-and-dependence decision, and keep a backup slide with the\n157\u2192 literature (or our own validation) on simulation\u2013experiment discrepancy for\n158\u2192 impact transients.\n159\u2192\n160\u2192### Transition\n161\u2192\n162\u2192If every data point must be a real experiment, we need a method that extracts\n163\u2192the most from every specimen \u2014 we need to make the process radically more\n164\u2192sample-efficient.\n165\u2192\n166\u2192### Point 2\n167\u2192\n168\u2192Bayesian optimization makes physical experimentation affordable, even with\n169\u2192limited, noisy data.\n170\u2192\n171\u2192- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172\u2192 visual in the main talk; implementation detail goes to backup slides.\n173\u2192 Spoken version:\n174\u2192 > \"After each batch, the model estimates both expected performance and\n175\u2192 > uncertainty across the design space. The acquisition rule selects a small\n176\u2192 > next batch that is most likely to expand the feasible trade-off frontier,\n177\u2192 > while accounting for noisy tests and print failures.\"\n178\u2192- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179\u2192 motivate a noise-aware acquisition function (qNEHVI) rather than\n180\u2192 non-noise-aware alternatives. (Backup slide: how observation noise is\n181\u2192 actually modeled \u2014 replicates, noise identification \u2014 and budget-matched\n182\u2192 baselines such as Sobol/random sampling.)\n183\u2192- Small parallel batches: the surrogate proposes the next batch of specimens\n184\u2192 to print, balancing exploration and exploitation.\n185\u2192- Constrained multi-objective by construction: **objectives** = maximize\n186\u2192 specific energy absorption and compaction efficiency; **constraint** = peak\n187\u2192 transmitted force must stay under a cap; the deliverable is the *feasible*\n188\u2192 Pareto front between the two objectives among force-cap-satisfying designs.\n189\u2192 (Keep this formulation consistent everywhere \u2014 do not present \"SEA vs. peak\n190\u2192 force\" as the trade-off.)\n191\u2192- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192\u2192 this advanced task (noisy, batched, constrained, multi-objective) in\n193\u2192 minutes using [Honegumi](https://honegumi.readthedocs.io/) \u2014 the audience\n194\u2192 can do the same for their own problems; show the code-template picker\n195\u2192 briefly so they see how low the barrier is.\n196\u2192\n197\u2192### Transition\n198\u2192\n199\u2192The optimizer is no longer the bottleneck \u2014 the limit is now providing real\n200\u2192data quickly enough.\n201\u2192\n202\u2192### Point 3\n203\u2192\n204\u2192Multi-material additive manufacturing supplies that data fast \u2014 single-build\n205\u2192co-fabrication, with no joining of parts.\n206\u2192\n207\u2192- Single-build PLA\u2013TPU co-fabrication: rigid struts and flexible tension\n208\u2192 network in one print \u2014 no joining or hand assembly of members, which closes\n209\u2192 the \"easy creation\" gap from the Need and is what makes future lattices\n210\u2192 feasible at all. (Be precise: if support removal, trimming, or conditioning\n211\u2192 remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212\u2192 assembly.\")\n213\u2192- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214\u2192 as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215\u2192 pre-tension status) plus a short deformation sequence \u2014 so the EDL engineer\n216\u2192 doesn't file it as a \"crush structure\" and the grad student can tell it from\n217\u2192 an ordinary lattice.\n218\u2192- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219\u2192 tested specimen (quasi-static compression + instrumented drop-weight impact)\n220\u2192 in hours. (Backup slide: representative print-to-result time breakdown and\n221\u2192 which steps remain manual \u2014 candidate selection is automated; slicing,\n222\u2192 handling, and testing are not yet.)\n223\u2192- Now we have the data we need to complete the loop.\n224\u2192\n225\u2192### Point 4 (optional \u2014 future applications)\n226\u2192\n227\u2192*Include if timing allows; otherwise fold into the Conclusion.*\n228\u2192\n229\u2192The same closed loop transfers wherever tests are authoritative, each test is\n230\u2192costly, the design space is mixed, and several performance goals conflict:\n231\u2192\n232\u2192- Tensegrity *lattices* and more complex architectures \u2014 exactly where hand\n233\u2192 assembly becomes infeasible and single-build co-fabrication pays off.\n234\u2192- Migration from the PLA\u2013TPU proxy toward flight-relevant materials and\n235\u2192 impact regimes.\n236\u2192- Safety hardware on Earth (e.g., the tensegrity crutch tip \u2014 future work).\n237\u2192- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238\u2192 handling, testing) in the loop.\n239\u2192\n240\u2192### Evidence / results\n241\u2192\n242\u2192*What we found \u2014 ground the conclusion in shown data*\n243\u2192\n244\u2192Build the talk around a quantitative three-slide sequence (per the\n245\u2192mock-audience report; insert observed values once the campaign completes):\n246\u2192\n247\u21921. **Campaign ledger** \u2014 \"The campaign used N specimens across B adaptive\n248\u2192 batches under a fixed physical-test budget\": initialization vs. adaptive\n249\u2192 batches, replicates, print failures, cycle time, and the numerical value\n250\u2192 (and rationale) of the force cap.\n251\u21922. **Budget-matched baseline** \u2014 \"Adaptive selection improved the feasible\n252\u2192 design set faster than [predeclared baseline]\": performance vs. number of\n253\u2192 physical tests against Sobol/random sampling at the same budget, with\n254\u2192 uncertainty where available.\n255\u21923. **Measured feasible Pareto front** \u2014 specific energy absorption vs.\n256\u2192 compaction efficiency among designs satisfying the force cap, with\n257\u2192 replicate spread, marked infeasible designs, and specimen photos or\n258\u2192 force\u2013time traces for representative Pareto designs.\n259\u2192\n260\u2192Supporting raw-data candidates: drop-test force\u2013time traces, Sobol /\n261\u2192first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262\u2192\n263\u2192### Suggested 15-minute allocation\n264\u2192\n265\u2192*(From the mock-audience report \u2014 results get the largest block.)*\n266\u2192\n267\u2192| Time | Content |\n268\u2192|---|---|\n269\u2192| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270\u2192| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271\u2192| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272\u2192| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273\u2192| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274\u2192| 1 min | Limitations and transfer conditions |\n275\u2192| 1 min | Measured conclusion + bookend |\n276\u2192\n277\u2192Move kernel choices, encodings, and other implementation detail to backup\n278\u2192slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279\u2192[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280\u2192\n281\u2192## Closing\n282\u2192\n283\u2192### Review\n284\u2192\n285\u2192*A recap of the body, leading into the conclusion*\n286\u2192\n287\u2192Using real, measured impact data from additively manufactured\n288\u2192tensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\n289\u2192optimization routine \u2014 we rapidly iterate toward better energy absorbers.\n290\u2192This feedback loop makes tensegrity optimization dramatically faster: dozens\n291\u2192of prints instead of the hundreds a traditional design of experiments over\n292\u2192this design space would require.\n293\u2192\n294\u2192### Conclusion\n295\u2192\n296\u2192*What the above means to the audience in the end*\n297\u2192\n298\u2192Because the loop runs on physical measurements, it is useful precisely where\n299\u2192model calibration is not worth its cost \u2014 and the same closed-loop pattern\n300\u2192should transfer (a conditional claim, pending evidence) to other additively\n301\u2192manufactured architectures whose performance is dominated by\n302\u2192hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303\u2192*use this loop when tests are authoritative, each test is costly, the design\n304\u2192space is mixed, and several performance goals conflict.* For tensegrity\n305\u2192specifically, it opens a path from this T3 proxy study toward lattices,\n306\u2192flight-relevant materials, payload protection on other planets, and safety\n307\u2192hardware here on Earth (e.g., the crutch tip, as future work).\n308\u2192\n309\u2192### Close\n310\u2192\n311\u2192*A way to end the presentation clearly and elegantly*\n312\u2192\n313\u2192Return to the opening image: the next planetary lander's energy absorber \u2014 a\n314\u2192structure that has to survive not one impact but many \u2014 doesn't have to take\n315\u2192years of hand-built prototypes; with this loop, candidate designs can be\n316\u2192designed, printed, and tested against real impacts in weeks instead of months\n317\u2192or longer. End on the measured engineering result and its transfer condition,\n318\u2192then the bookend: every method that compresses design time moves our ventures\n319\u2192\u2014 on this planet and beyond \u2014 forward.\n320\u2192\n321\u2192---\n322\u2192\n323\u2192## Changes from Draft 2\n324\u2192\n325\u2192Applied from @sgbaird's inline review comments on PR #84 and the\n326\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327\u2192\n328\u2192**From the inline review comments:**\n329\u2192\n330\u21921. **Reusability in the hook** (line 22): the tensegrity lander survives\n331\u2192 multiple drops, unlike crushable/sacrificial alternatives.\n332\u21922. **Proxy-system framing** (lines 49, 87): PLA\u2013TPU would never fly; it is a\n333\u2192 proxy for prototyping a self-driving-lab-style workflow \u2014 stated in a\n334\u2192 scope note up front and re-stated right after the hook.\n335\u21923. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336\u2192 heavy-duty simulation falls short of the experiments (millisecond-scale\n337\u2192 peak shock), not that it merely costs too much.\n338\u21924. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339\u2192 complex structures and especially lattices are not.\n340\u21925. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341\u2192 (temperature, print speed, \u2026) only; the rest is explicitly future work.\n342\u21926. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343\u2192 literature search (or validation of experiments against simulation), plus\n344\u2192 the millisecond-resolution peak-shock argument.\n345\u21927. **Optional Point 4 added** (line 97): future applications, with a note it\n346\u2192 can fold into the Conclusion if time is short.\n347\u21928. **Steel-materials point** (line 104): simulations fall short for the\n348\u2192 actual engineering materials (steel struts/cables), not just printed\n349\u2192 PLA\u2013TPU.\n350\u21929. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351\u2192 in minutes, pitched so the audience sees they can use it too.\n352\u219210. **\"Thousands\" \u2192 \"hundreds\"** (line 173): baseline is now a traditional\n353\u2192 design of experiments over a high-dimensional space (hundreds), in the\n354\u2192 main message, Need, Point 1, and Review.\n355\u219211. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356\u2192 or longer\" for candidate designs.\n357\u2192\n358\u2192**From the mock-audience report's priority revisions:**\n359\u2192\n360\u219212. **Quantitative three-slide results sequence** (Priority 1): campaign\n361\u2192 ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front, with a\n362\u2192 fallback main-message wording if the campaign can't yet support the\n363\u2192 efficiency claim.\n364\u219213. **Anti-simulation premise replaced with a scoped cost decision**\n365\u2192 (Priority 2): Point 1 retitled to the information-value message;\n366\u2192 \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367\u2192 anticipated with a backup slide.\n368\u219214. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369\u2192 implementation detail moved to backup slides; Q&A prep pointed at the\n370\u2192 report's claims-vs-evidence table.\n371\u219215. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372\u2192 the main talk, machinery in backup slides; the misleading\n373\u2192 \"qNEHVI over standard expected improvement\" comparison replaced with\n374\u2192 \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375\u219216. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376\u2192 sequence in Point 3, so the structure isn't remembered as a generic crush\n377\u2192 structure or lattice.\n378\u219217. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379\u2192 co-fabrication / no joining of parts,\" with a note to list remaining\n380\u2192 manual steps and an automation-status caveat.\n381\u219218. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382\u2192 compaction efficiency, constraint = force cap, deliverable = *feasible*\n383\u2192 Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384\u219219. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385\u2192 transfer claim marked conditional, and the close ends on the measured\n386\u2192 result before the bookend.\n387\u2192\n388\u2192## Changes from Draft 1\n389\u2192\n390\u2192Feedback from the PR #84 review, applied in Draft 2:\n391\u2192\n392\u21921. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n393\u2192 dozens-not-thousands claim); body and message now carry the same story.\n394\u21922. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n395\u2192 manuscript's positioning (#75/#76) is that the physical experiment is the\n396\u2192 source of truth, explicitly avoiding calibrated FE simulation.\n397\u21923. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n398\u2192 parametric design space and avoid an over-claim to a design-automation\n399\u2192 audience.\n400\u21924. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401\u2192 energy absorption and compaction efficiency, cap peak transmitted force.\n402\u21925. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403\u2192 *and* the space is too big to brute-force \u2014 which makes BO + AM the\n404\u2192 inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405\u2192 above.)*\n406\u21926. **Point 1 given its own content** (the *why* behind the inefficiency)\n407\u2192 instead of restating the Need.\n408\u21927. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409\u2192 the specifics (noise-aware acquisition, small batches, multi-objective).\n410\u21928. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411\u2192 (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412\u2192 above.)*\n413\u21929. **Evidence/results slot added** before the Closing, so the Conclusion is\n414\u2192 grounded in shown data rather than three hedged \"may\"s.\n415\u219210. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n416\u2192 Ball Bot visual; verified video links included.\n417\u219211. **Close bookends the opening** (the next lander's absorber, designed in\n418\u2192 weeks) before the broader humankind line.\n419\u219212. **Spelling**: Doumont (was \"Duomont\").", + "name": "read", + "tool_call_id": "call_93haurOMDGiH2mQFFbaMaYKn", + "info": { + "start_ts": 8147.331065886, + "end_ts": 8147.336467845 + } + }, + { + "role": "tool", + "content": " 1\u2192# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n 2\u2192\n 3\u2192Video: (assigned in #83)\n 4\u2192\n 5\u2192Transcribed from @me-madsen's handwritten notes\n 6\u2192([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7\u2192dated 2026-07-15).\n 8\u2192\n 9\u2192## Overview\n10\u2192\n11\u2192A presentation is all about the right content, displayed the right way, at the\n12\u2192right time.\n13\u2192\n14\u2192- Verbal communication is sequential.\n15\u2192- Message is *interpretation* of the information.\n16\u2192 - Ensure the audience can get the most out of what you're doing.\n17\u2192 - Don't just give them information \u2014 help them understand it, interpret it,\n18\u2192 and internalize it.\n19\u2192\n20\u2192## Doumont's suggested rules for a presentation\n21\u2192\n22\u21920. Ensure you have **messages** for your audience, not just words.\n23\u21921. **Adapt to your audience.**\n24\u2192 - Think like the audience, not the speaker.\n25\u21922. **Maximize signal-to-noise ratio.**\n26\u2192 - Reduce noise (animations, laser pointers, anything that pulls attention\n27\u2192 away from your message).\n28\u21923. **Use effective redundancy.**\n29\u2192 - Tell your message in word *and* on slide: someone deaf should understand\n30\u2192 your messages just as well as someone blind.\n31\u2192 - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32\u2192 date).\n33\u2192\n34\u2192## When creating slides\n35\u2192\n36\u2192- If the audience doesn't need it, why put it on the slides?\n37\u2192- People shouldn't have to count the items on a slide to know how many are\n38\u2192 there.\n39\u2192- Don't use your slides as your \"presentation notes.\"\n40\u2192 - Don't show your notes to the audience.\n41\u2192 - **A bad slide is worse than no slide at all.**\n42\u2192- **One message per slide** is a good rule of thumb.\n43\u2192 - Prioritize the *so what* (or *therefore what*) of your message.\n44\u2192- Make it easy to interpret the information on the slide:\n45\u2192 - 3\u20135 items at most.\n46\u2192 - Align the title left.\n47\u2192 - Do not clutter slides with repetitive or unnecessary information: logos,\n48\u2192 affiliation, date, slide numbers.\n49\u2192- Doumont's suggested workflow:\n50\u2192 1. **Start with the title.**\n51\u2192 - Use a full sentence in the title.\n52\u2192 - The title is the main message of the slide.\n53\u2192 - Make it easy to read: left-aligned, with intentional line breaks.\n54\u2192 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55\u2192- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56\u2192\n57\u2192## General preparation\n58\u2192\n59\u2192- Planning for audience, content, and structure matters more than materials\n60\u2192 (i.e., slides).\n61\u2192- Try using one sheet of paper for presentation preparation.\n62\u2192- It's most important that your presentation is:\n63\u2192 - well planned, structured, and delivered;\n64\u2192 - **if you don't have a lot of time to prepare, don't focus on making\n65\u2192 slides.**\n66\u2192- Practice the presentation a few times. Practice at least once *without* your\n67\u2192 slideshow!\n68\u2192- If doing a handout, keep it separate from the slides you're preparing.\n69\u2192- Impress the audience with your body.\n70\u2192 - Use gestures, not laser pointers.\n71\u2192 - \"Control\" the audience with your body \u2014 keep their attention on your\n72\u2192 presentation.\n73\u2192\n74\u2192## Impressions / what to apply to the IDETC presentation\n75\u2192\n76\u2192\n77\u2192\n78\u2192## Questions / things to discuss\n79\u2192\n80\u2192", + "name": "read", + "tool_call_id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "info": { + "start_ts": 8147.331133416, + "end_ts": 8147.338304015 + } + }, + { + "role": "tool", + "content": " 1\u2192\\documentclass[10pt]{article}\n 2\u2192\n 3\u2192% --- Packages ---\n 4\u2192\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5\u2192\\usepackage[hidelinks]{hyperref}\n 6\u2192\\usepackage{parskip}\n 7\u2192\\usepackage{graphicx}\n 8\u2192\\usepackage{caption}\n 9\u2192\\usepackage{titlesec}\n 10\u2192\\usepackage{wrapfig}\n 11\u2192\\usepackage[numbers,sort&compress]{natbib}\n 12\u2192\n 13\u2192% --- Formatting ---\n 14\u2192\\setlength{\\parskip}{0.2em}\n 15\u2192\\setlength{\\parindent}{0em}\n 16\u2192\\titleformat*{\\section}{\\large\\bfseries}\n 17\u2192\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18\u2192\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19\u2192\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20\u2192\\setlength{\\emergencystretch}{2em}\n 21\u2192\\setlength{\\intextsep}{0.2em}\n 22\u2192\\bibliographystyle{abbrvnat}\n 23\u2192\n 24\u2192\\begin{document}\n 25\u2192\n 26\u2192\\begin{center}\n 27\u2192{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28\u21923D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29\u2192\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30\u2192\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31\u2192\\end{center}\n 32\u2192\n 33\u2192\\vspace{0.4em}\n 34\u2192\n 35\u2192\\section*{Abstract}\n 36\u2192Tensegrity-inspired architectures---rigid struts suspended within a\n 37\u2192continuous flexible network---can exhibit tunable nonlinear\n 38\u2192force--displacement responses and favorable energy absorption per unit\n 39\u2192mass, motivating their use in lightweight energy-absorbing and protective\n 40\u2192structures\n 41\u2192\\citep{skelton2009tensegrity,pajunen2019design}.\n 42\u2192Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43\u2192struts and flexible (TPU) elements in a single build\n 44\u2192\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45\u2192space---strut geometry, tension-element cross-section, connectivity\n 46\u2192topology, and unit-cell tiling---is too large to explore by trial and\n 47\u2192error. We present a closed-loop experimental campaign that uses\n 48\u2192Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49\u2192to drive a design--print--test workflow operating directly on physical\n 50\u2192measurements, without relying on calibrated finite-element simulation\n 51\u2192for objective evaluation.\n 52\u2192\n 53\u2192\\section{Introduction and Motivation}\n 54\u2192\n 55\u2192\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56\u2192\\centering\n 57\u2192\\vspace{-0.4em}\n 58\u2192\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59\u2192\\captionsetup{font=small}\n 60\u2192\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61\u2192Candidate tensegrity-inspired unit cells are instantiated from a\n 62\u2192parameter vector, fabricated by multi-material FDM, and tested under\n 63\u2192quasi-static compression and drop-weight impact. Measured peak force,\n 64\u2192specific energy absorption (SEA), and compaction efficiency update a\n 65\u2192Gaussian-process surrogate that proposes the next batch of designs.}\n 66\u2192\\label{fig:overview}\n 67\u2192\\vspace{-0.4em}\n 68\u2192\\end{wrapfigure}\n 69\u2192\n 70\u2192Tensegrity structures combine compression-only members with a\n 71\u2192pre-tensioned tension network to achieve stiffness without rigid\n 72\u2192joinery~\\citep{skelton2009tensegrity}. Their\n 73\u2192nonlinear, often load-limiting force--displacement response makes them\n 74\u2192attractive for impact mitigation, packaging, and wearable protective\n 75\u2192gear~\\citep{pajunen2019design}. Idealized\n 76\u2192cable--strut tensegrities are difficult to manufacture at scale, but\n 77\u2192\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78\u21923D printing reproduce key tensegrity behaviors---including\n 79\u2192post-buckling stability and load-limiting force--displacement\n 80\u2192response~\\citep{pajunen2019design}---motivating our extension to\n 81\u2192multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82\u2192elements. PLA provides rigid struts; TPU provides flexible,\n 83\u2192rate-dependent tension elements whose viscoelasticity is expected to\n 84\u2192contribute hysteretic energy dissipation under impact loading,\n 85\u2192consistent with the energy-absorbing behavior reported for rigid--TPU\n 86\u2192multi-material prints~\\citep{khatri2024energy}.\n 87\u2192\n 88\u2192The challenge is that small geometric and topological changes can\n 89\u2192produce large changes in the measured response, and high-fidelity\n 90\u2192simulation of FDM specimens is computationally expensive and often\n 91\u2192struggles to capture physical defects such as interfacial slip and\n 92\u2192TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93\u2192trial-and-error sweeps over the joint design space are also infeasible:\n 94\u2192even modest parameter resolutions yield thousands of candidates, each\n 95\u2192requiring print and test time. We instead treat the physical\n 96\u2192experiment as the source of truth and use BO to select which specimens\n 97\u2192are most informative to fabricate next---an approach used in related\n 98\u2192closed-loop materials and structures\n 99\u2192campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100\u2192Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101\u2192set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102\u2192designs using parallel, noisy multi-objective BO driven by measured\n103\u2192$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104\u2192\n105\u2192\\section{Approach}\n106\u2192\\subsection{Closed-Loop Workflow}\n107\u2192Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108\u2192proceeds in four steps: (i)~a parameterized unit-cell design is\n109\u2192instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110\u2192is sliced and printed on a multi-material FDM system; (iii)~it is\n111\u2192tested under quasi-static compression and instrumented drop-weight\n112\u2192impact; and (iv)~the resulting performance metrics update a\n113\u2192Gaussian-process (GP) surrogate, which a batch acquisition function\n114\u2192queries to recommend the next set of candidates. Surrogate updating\n115\u2192and experiment selection are automated; specimen handling, slicing,\n116\u2192and test setup remain manual.\n117\u2192\n118\u2192\\subsection{Design Space}\n119\u2192We parameterize a family of unit cells using a core-wrapping\n120\u2192architecture in which rigid PLA struts are enclosed by continuous TPU\n121\u2192skins; the wrapping rationale follows multi-material FDM strategies in\n122\u2192which rigid panels are wrapped by stretchable soft material to prevent\n123\u2192delamination and improve interfacial bonding at the rigid--flexible\n124\u2192interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125\u2192comprises continuous geometric variables (strut diameter and length,\n126\u2192tension-element width and thickness), an integer strut count per unit\n127\u2192cell, and two categorical variables (connectivity topology, unit-cell\n128\u2192tiling pattern). Categorical choices are encoded explicitly rather\n129\u2192than embedded in a continuous metric, and bounds on the continuous\n130\u2192variables are chosen to remain within the printer's resolution and\n131\u2192overhang limits and to keep specimens in a common form factor for the\n132\u2192test fixtures.\n133\u2192\n134\u2192\\subsection{Surrogate and Acquisition}\n135\u2192The GP surrogate is trained on three measured performance metrics:\n136\u2192peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137\u2192(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138\u2192objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139\u2192likelihood with priors to avoid pathological length scales at small $n$.\n140\u2192Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141\u2192interfacial defects motivates our use of q-noisy expected hypervolume\n142\u2192improvement (qNEHVI), which explicitly accounts for observation noise\n143\u2192in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144\u2192expected-improvement variants. Print failures are treated as a\n145\u2192probabilistic feasibility constraint: the acquisition score for each\n146\u2192candidate is its qNEHVI improvement weighted by the GP-estimated\n147\u2192probability of successful fabrication, following recent constrained\n148\u2192multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149\u2192Where applicable, we use numerically stable log-space variants of\n150\u2192improvement-based acquisitions to mitigate vanishing-gradient\n151\u2192pathologies as data and constraints\n152\u2192accumulate~\\citep{ament2023logei}.\n153\u2192\n154\u2192\\section{Methods}\n155\u2192\\subsection{Fabrication}\n156\u2192Specimens are fabricated on a multi-material FDM system capable of\n157\u2192co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158\u2192Print parameters (temperature, line width, infill, retraction) are\n159\u2192held fixed within a batch to isolate the effect of the design\n160\u2192variables. Each specimen is weighed and dimensionally inspected prior\n161\u2192to testing so that mass and geometric deviations are available as\n162\u2192covariates.\n163\u2192\n164\u2192\\subsection{Mechanical Testing}\n165\u2192Quasi-static compression is performed on a screw-driven load frame at\n166\u2192a fixed nominal strain rate, with force--displacement recorded to\n167\u2192densification. Drop-weight impact testing uses an instrumented tup at\n168\u2192a fixed impact energy; transmitted force is captured at high sample\n169\u2192rate, and specimens are imaged before and after impact to document\n170\u2192failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171\u2192unit mass, and compaction efficiency $\\eta$, defined consistently\n172\u2192across the campaign. A control specimen from an earlier batch is\n173\u2192retested periodically to monitor TPU batch-to-batch and printer drift.\n174\u2192\n175\u2192\\section{Expected Outcomes and Discussion}\n176\u2192We will report how BO-selected designs evolve across iterations, how\n177\u2192GP surrogate predictions and uncertainties change as data accumulate,\n178\u2192and which Pareto-efficient trade-offs emerge between SEA and peak\n179\u2192transmitted force within the explored design space. We will also\n180\u2192discuss practical lessons for operating the closed-loop workflow,\n181\u2192including handling of print failures, batch-to-batch variability in\n182\u2192TPU response, the influence of the rigid--flexible interface on\n183\u2192energy absorption, and the exploration--exploitation balance of the\n184\u2192acquisition function under realistic experimental noise. We expect\n185\u2192the campaign to clarify where physical experimentation is genuinely\n186\u2192required versus where simpler analytical or multi-fidelity surrogate\n187\u2192shortcuts~\\citep{mo2023accelerated} could be\n188\u2192substituted in future work.\n189\u2192\n190\u2192\\section{Conclusion}\n191\u2192This work demonstrates a partially automated, experiment-driven\n192\u2192closed-loop pipeline for designing multi-material 3D-printed\n193\u2192tensegrity-inspired energy absorbers, with surrogate updating and\n194\u2192candidate selection automated and fabrication and testing kept manual.\n195\u2192By updating a GP surrogate directly from physical measurements, the\n196\u2192workflow avoids direct dependence on calibrated finite-element\n197\u2192simulation for objective evaluation while still providing principled,\n198\u2192multi-objective design recommendations. The same closed-loop pattern is\n199\u2192expected to transfer to other multi-material additively manufactured\n200\u2192architectures whose performance is dominated by hard-to-simulate\n201\u2192effects.\n202\u2192\n203\u2192{\\tiny\n204\u2192\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205\u2192\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206\u2192% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207\u2192% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208\u2192\\providecommand{\\doi}[1]{}\n209\u2192\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210\u2192\\bibliography{references}\n211\u2192}\n212\u2192\n213\u2192\\end{document}", + "name": "read", + "tool_call_id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "info": { + "start_ts": 8147.332633466, + "end_ts": 8147.339043095 + } + } + ], + "next_observation": [ + { + "role": "tool", + "content": " 1\u2192# IDETC Tensegrity Slides \u2014 Draft 1 (extracted content)\n 2\u2192\n 3\u2192Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4\u2192\n 5\u2192\n 6\u2192## Slide 1 \u2014 layout: Title Only\n 7\u2192\n 8\u2192**On-slide text:**\n 9\u2192- **Title 1**: Let\u2019s build better tensegrity structures faster\n 10\u2192- **TextBox 4**: Marcus Madsen\n 11\u2192Undergraduate researcher under Sterling Baird, Ph.D.\n 12\u2192- **TextBox 6**: IDETC 2026\n 13\u2192\n 14\u2192**Non-text shapes / media / placeholders:**\n 15\u2192- [PICTURE (13)] Picture 3\n 16\u2192- [PICTURE (13)] Picture 8\n 17\u2192\n 18\u2192**Presenter notes:**\n 19\u2192> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20\u2192> \n 21\u2192> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22\u2192\n 23\u2192\n 24\u2192## Slide 2 \u2014 layout: Title Only\n 25\u2192\n 26\u2192**On-slide text:**\n 27\u2192- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings\u2014and to problems closer to home.\n 28\u2192\n 29\u2192**Non-text shapes / media / placeholders:**\n 30\u2192- [MEDIA (16)] Online Media 5\n 31\u2192\n 32\u2192**Presenter notes:**\n 33\u2192> Replace this video with a downloaded and trimmed version of just the fall. \n 34\u2192> \n 35\u2192> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle \u2014 thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander\u00a0is\u00a0the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit\u00a0and get back up\u00a0\u2014 it survives multiple drops and keeps working.\n 36\u2192\n 37\u2192\n 38\u2192## Slide 3 (HIDDEN) \u2014 layout: Title Only\n 39\u2192\n 40\u2192**On-slide text:**\n 41\u2192- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42\u2192- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43\u2192- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44\u2192\n 45\u2192**Presenter notes:**\n 46\u2192> This might be better explained purely vocally while on the previous slide.\n 47\u2192\n 48\u2192\n 49\u2192## Slide 4 \u2014 layout: Title Only\n 50\u2192\n 51\u2192**On-slide text:**\n 52\u2192- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53\u2192- **TextBox 3**: Iteration\n 54\u2192- **TextBox 5**: Building\n 55\u2192- **TextBox 7**: Testing\n 56\u2192\n 57\u2192**Non-text shapes / media / placeholders:**\n 58\u2192- [MEDIA (16)] AdobeStock_207717222\n 59\u2192- [MEDIA (16)] Online Media 2\n 60\u2192- [PICTURE (13)] Picture 14\n 61\u2192- [PICTURE (13)] Picture 16\n 62\u2192- [PICTURE (13)] Picture 18\n 63\u2192\n 64\u2192**Presenter notes:**\n 65\u2192> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn\u2019t become distracting because it\u2019s fun to watch it spin.\n 66\u2192> \n 67\u2192> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68\u2192\n 69\u2192\n 70\u2192## Slide 5 (HIDDEN) \u2014 layout: Title Only\n 71\u2192\n 72\u2192**On-slide text:**\n 73\u2192- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74\u2192- **TextBox 14**: Baird et al. Matter (2022)\n 75\u2192\n 76\u2192**Non-text shapes / media / placeholders:**\n 77\u2192- [GROUP (6)] Group 2\n 78\u2192\n 79\u2192**Presenter notes:**\n 80\u2192> Consult Sterling on this slide. I don\u2019t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it\u2019s well enough known that it\u2019d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81\u2192\n 82\u2192\n 83\u2192## Slide 6 (HIDDEN) \u2014 layout: Title Only\n 84\u2192\n 85\u2192**On-slide text:**\n 86\u2192- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87\u2192- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88\u2192\n 89\u2192**Presenter notes:**\n 90\u2192> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91\u2192\n 92\u2192\n 93\u2192## Slide 7 \u2014 layout: Title Only\n 94\u2192\n 95\u2192**On-slide text:**\n 96\u2192- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97\u2192- **TextBox 5**: 6 variables\n 98\u2192- **TextBox 7**: 2 types of input data\n 99\u2192- **TextBox 9**: 2 objectives\n100\u2192- **TextBox 17**: *image of blackbox*\n101\u2192\n102\u2192**Non-text shapes / media / placeholders:**\n103\u2192- [LINE (9)] Connector: Curved 11\n104\u2192- [LINE (9)] Connector: Curved 13\n105\u2192- [LINE (9)] Connector: Curved 15\n106\u2192\n107\u2192**Presenter notes:**\n108\u2192> Very rough slide as I\u2019m not entirely sure what graphic or text to put on this, partially because I\u2019m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109\u2192\n110\u2192\n111\u2192## Slide 8 \u2014 layout: Title Only\n112\u2192\n113\u2192**On-slide text:**\n114\u2192- **Title 1**: Multi-material additive manufacturing supplies that data fast \u2014 single-build co-fabrication, with no joining of parts.\n115\u2192\n116\u2192**Non-text shapes / media / placeholders:**\n117\u2192- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118\u2192- [PICTURE (13)] Picture 4\n119\u2192\n120\u2192**Presenter notes:**\n121\u2192> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122\u2192\n123\u2192\n124\u2192## Slide 9 \u2014 layout: Title Only\n125\u2192\n126\u2192**On-slide text:**\n127\u2192- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128\u2192- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129\u2192- **TextBox 5**: *placeholder for slo motion of drop test*\n130\u2192\n131\u2192\n132\u2192## Slide 10 \u2014 layout: Title Only\n133\u2192\n134\u2192**On-slide text:**\n135\u2192- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136\u2192- **TextBox 3**: Printing\n137\u2192- **TextBox 5**: Testing\n138\u2192- **TextBox 7**: BO feed\n139\u2192\n140\u2192**Non-text shapes / media / placeholders:**\n141\u2192- [PICTURE (13)] Picture 14\n142\u2192- [PICTURE (13)] Picture 16\n143\u2192- [PICTURE (13)] Picture 18\n144\u2192- [PICTURE (13)] Graphic 8\n145\u2192\n146\u2192**Presenter notes:**\n147\u2192> I think it might be better if we replaced \u201cprinting\u201d with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148\u2192\n149\u2192\n150\u2192## Slide 11 (HIDDEN) \u2014 layout: Title Only\n151\u2192\n152\u2192**On-slide text:**\n153\u2192- **Title 1**: Reserved for results\n154\u2192\n155\u2192**Presenter notes:**\n156\u2192> Would be helpful if we could show that this process works in this case.\n157\u2192\n158\u2192\n159\u2192## Slide 12 \u2014 layout: Title Only\n160\u2192\n161\u2192**On-slide text:**\n162\u2192- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163\u2192- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164\u2192- **TextBox 3**: *image to show lattice structure\n165\u2192\n166\u2192\n167\u2192## Slide 13 \u2014 layout: Title Only\n168\u2192\n169\u2192**Non-text shapes / media / placeholders:**\n170\u2192- [PICTURE (13)] Picture 3\n171\u2192\n172\u2192**Presenter notes:**\n173\u2192> Slide for questions at the end\n174\u2192\n175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "info": { + "start_ts": 8147.330900226, + "end_ts": 8147.337548875 + } + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Presentation \u2014 Doumont Presentation Structure Template\n 2\u2192\n 3\u2192Transcribed from Jean-luc Doumont's presentation-structure template\n 4\u2192([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5\u2192adapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\n 6\u2192All rights reserved. Can be downloaded from .\n 7\u2192\n 8\u2192> To design your presentation, write down your ideas for each component below.\n 9\u2192> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10\u2192\n 11\u2192**Draft 3** \u2014 applies the inline review comments from @sgbaird on PR #84 and the\n 12\u2192priority revisions from the\n 13\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14\u2192Summaries of what changed are at the bottom of this file\n 15\u2192([Draft 2 \u2192 3](#changes-from-draft-2), [Draft 1 \u2192 2](#changes-from-draft-1)).\n 16\u2192\n 17\u2192**Scope note (frame everything below with this):** the study presented is the\n 18\u2192optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19\u2192processing parameters** (e.g., nozzle temperature, print speed). PLA\u2013TPU is a\n 20\u2192**proxy system** \u2014 not flight material \u2014 used to prototype a self-driving-lab\n 21\u2192style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22\u2192The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23\u2192beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24\u2192\n 25\u2192## Opening\n 26\u2192\n 27\u2192### Attention getter\n 28\u2192\n 29\u2192*A way to lead the audience to the need efficiently*\n 30\u2192\n 31\u2192Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32\u2192landings \u2014 and to problems closer to home.\n 33\u2192\n 34\u2192One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35\u2192clip) do the work:\n 36\u2192\n 37\u2192> Landing a payload on another planet means surviving an impact in conditions\n 38\u2192> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n 39\u2192> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40\u2192> structure: the lander *is* the energy absorber. And unlike crushable\n 41\u2192> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42\u2192> hit **and get back up** \u2014 it survives multiple drops and keeps working.\n 43\u2192\n 44\u2192Candidate Super Ball Bot videos to show (verified links):\n 45\u2192\n 46\u2192- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n 47\u2192 (official channel); the canonical overview.\n 48\u2192- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49\u2192 \u2014 Wall Street Journal; prototype drop footage.\n 50\u2192- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51\u2192 \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n 52\u2192\n 53\u2192Immediately after the hook, state the present study scope in one sentence\n 54\u2192(per the scope note above): *we study a printed PLA\u2013TPU T3 prism as a proxy\n 55\u2192system for prototyping an autonomous tensegrity-optimization workflow* \u2014 so\n 56\u2192the planetary framing never outruns the demonstrated scope.\n 57\u2192\n 58\u2192\n 60\u2192\n 61\u2192### Need\n 62\u2192\n 63\u2192*A difference between actual and desired situations*\n 64\u2192\n 65\u2192**Actual situation** (a two-sided pincer \u2014 stated as a scoped engineering\n 66\u2192judgment, not an absolute):\n 67\u2192\n 68\u2192- For these structures, obtaining trustworthy objective values from a\n 69\u2192 validated high-fidelity model would require substantial calibration of\n 70\u2192 interface, rate, contact, and defect behavior \u2014 and even heavy-duty\n 71\u2192 simulation falls short of the experiments we are running: peak transmitted\n 72\u2192 shock is a millisecond-scale transient that is notoriously difficult to\n 73\u2192 resolve numerically. This holds even for idealized cable\u2013strut tensegrities\n 74\u2192 in their actual engineering materials (steel or similar), not just for\n 75\u2192 printed PLA\u2013TPU.\n 76\u2192 \n 79\u2192- Exhaustive physical search fails on cost: a traditional design of\n 80\u2192 experiments over this relatively high-dimensional space (geometry + FDM\n 81\u2192 process parameters) would demand hundreds of specimens, each costing print\n 82\u2192 and test time.\n 83\u2192- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84\u2192 more complex structures \u2014 especially tensegrity *lattices* \u2014 become\n 85\u2192 extremely difficult and ultimately infeasible to hand-build.\n 86\u2192\n 87\u2192**Desired situation:**\n 88\u2192\n 89\u2192- Trustworthy performance data without hand assembly (easy creation of\n 90\u2192 tensegrity structures, scalable beyond a single prism).\n 91\u2192- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n 92\u2192 not the hundreds a traditional design of experiments would require.\n 93\u2192\n 94\u2192### Task\n 95\u2192\n 96\u2192*What I decided/was asked to do to address the need*\n 97\u2192\n 98\u2192Build a closed-loop design\u2013print\u2013test workflow that optimizes a **T3-prism\n 99\u2192tensegrity-inspired energy absorber together with its FDM processing\n100\u2192parameters** (e.g., temperature, print speed) directly from real, measured\n101\u2192impact data \u2014 maximizing energy absorbed per gram (specific energy\n102\u2192absorption) and compaction efficiency, subject to a cap on the peak force\n103\u2192transmitted to the payload. Extensions beyond the T3 prism are future work.\n104\u2192\n105\u2192### Main message\n106\u2192\n107\u2192*The one sentence I want my audience to remember*\n108\u2192\n109\u2192By closing the loop between multi-material 3D printing and Bayesian\n110\u2192optimization, we can optimize tensegrity-inspired energy absorbers directly\n111\u2192from real impact data \u2014 in dozens of prints, not the hundreds a traditional\n112\u2192design of experiments would demand.\n113\u2192\n114\u2192\n118\u2192\n119\u2192### Preview\n120\u2192\n121\u2192*A map of the body (ideally three points, max. five)*\n122\u2192\n123\u21921. When model calibration and exhaustive testing are both costly, each\n124\u2192 physical specimen must be chosen for its information value \u2014 so the\n125\u2192 measured experiment is our source of truth.\n126\u21922. Bayesian optimization makes physical experimentation affordable: it handles\n127\u2192 noisy measurements, small batches, and the constrained multi-objective\n128\u2192 trade-off \u2014 and Honegumi let us scaffold it in minutes.\n129\u21923. Multi-material additive manufacturing closes the loop: single-build PLA\u2013TPU\n130\u2192 co-fabrication turns each proposed design into test data in hours, with no\n131\u2192 joining of parts.\n132\u21924. *(Optional fourth point, if time allows)* The same closed loop points at\n133\u2192 what comes next: tensegrity lattices, flight-relevant materials, and\n134\u2192 safety hardware on Earth.\n135\u2192\n136\u2192## Body\n137\u2192\n138\u2192### Point 1\n139\u2192\n140\u2192When model calibration and exhaustive testing are both costly, each physical\n141\u2192specimen must be chosen for its information value.\n142\u2192\n143\u2192- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144\u2192 these structures, a sufficiently calibrated multiphysics model is expensive\n145\u2192 to build relative to the design campaign itself \u2014 and the hardest quantity,\n146\u2192 peak transmitted shock, is a millisecond-scale transient that even\n147\u2192 heavy-duty simulation struggles to resolve. The gap persists in the actual\n148\u2192 engineering materials a real lander would use (steel struts and cables),\n149\u2192 so this is not merely an artifact of printed PLA\u2013TPU.\n150\u2192- The alternative \u2014 sweeping the design space physically \u2014 fails on cost: a\n151\u2192 traditional design of experiments over geometry plus FDM process parameters\n152\u2192 would take hundreds of specimens, each needing print + test time.\n153\u2192- Therefore: treat the physical experiment as the authoritative source of\n154\u2192 objective values, and be ruthless about *which* experiments to run.\n155\u2192- Anticipate the FEA-veteran pushback (\"inconvenient \u2260 untrustworthy\"): frame\n156\u2192 it as a cost-and-dependence decision, and keep a backup slide with the\n157\u2192 literature (or our own validation) on simulation\u2013experiment discrepancy for\n158\u2192 impact transients.\n159\u2192\n160\u2192### Transition\n161\u2192\n162\u2192If every data point must be a real experiment, we need a method that extracts\n163\u2192the most from every specimen \u2014 we need to make the process radically more\n164\u2192sample-efficient.\n165\u2192\n166\u2192### Point 2\n167\u2192\n168\u2192Bayesian optimization makes physical experimentation affordable, even with\n169\u2192limited, noisy data.\n170\u2192\n171\u2192- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172\u2192 visual in the main talk; implementation detail goes to backup slides.\n173\u2192 Spoken version:\n174\u2192 > \"After each batch, the model estimates both expected performance and\n175\u2192 > uncertainty across the design space. The acquisition rule selects a small\n176\u2192 > next batch that is most likely to expand the feasible trade-off frontier,\n177\u2192 > while accounting for noisy tests and print failures.\"\n178\u2192- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179\u2192 motivate a noise-aware acquisition function (qNEHVI) rather than\n180\u2192 non-noise-aware alternatives. (Backup slide: how observation noise is\n181\u2192 actually modeled \u2014 replicates, noise identification \u2014 and budget-matched\n182\u2192 baselines such as Sobol/random sampling.)\n183\u2192- Small parallel batches: the surrogate proposes the next batch of specimens\n184\u2192 to print, balancing exploration and exploitation.\n185\u2192- Constrained multi-objective by construction: **objectives** = maximize\n186\u2192 specific energy absorption and compaction efficiency; **constraint** = peak\n187\u2192 transmitted force must stay under a cap; the deliverable is the *feasible*\n188\u2192 Pareto front between the two objectives among force-cap-satisfying designs.\n189\u2192 (Keep this formulation consistent everywhere \u2014 do not present \"SEA vs. peak\n190\u2192 force\" as the trade-off.)\n191\u2192- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192\u2192 this advanced task (noisy, batched, constrained, multi-objective) in\n193\u2192 minutes using [Honegumi](https://honegumi.readthedocs.io/) \u2014 the audience\n194\u2192 can do the same for their own problems; show the code-template picker\n195\u2192 briefly so they see how low the barrier is.\n196\u2192\n197\u2192### Transition\n198\u2192\n199\u2192The optimizer is no longer the bottleneck \u2014 the limit is now providing real\n200\u2192data quickly enough.\n201\u2192\n202\u2192### Point 3\n203\u2192\n204\u2192Multi-material additive manufacturing supplies that data fast \u2014 single-build\n205\u2192co-fabrication, with no joining of parts.\n206\u2192\n207\u2192- Single-build PLA\u2013TPU co-fabrication: rigid struts and flexible tension\n208\u2192 network in one print \u2014 no joining or hand assembly of members, which closes\n209\u2192 the \"easy creation\" gap from the Need and is what makes future lattices\n210\u2192 feasible at all. (Be precise: if support removal, trimming, or conditioning\n211\u2192 remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212\u2192 assembly.\")\n213\u2192- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214\u2192 as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215\u2192 pre-tension status) plus a short deformation sequence \u2014 so the EDL engineer\n216\u2192 doesn't file it as a \"crush structure\" and the grad student can tell it from\n217\u2192 an ordinary lattice.\n218\u2192- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219\u2192 tested specimen (quasi-static compression + instrumented drop-weight impact)\n220\u2192 in hours. (Backup slide: representative print-to-result time breakdown and\n221\u2192 which steps remain manual \u2014 candidate selection is automated; slicing,\n222\u2192 handling, and testing are not yet.)\n223\u2192- Now we have the data we need to complete the loop.\n224\u2192\n225\u2192### Point 4 (optional \u2014 future applications)\n226\u2192\n227\u2192*Include if timing allows; otherwise fold into the Conclusion.*\n228\u2192\n229\u2192The same closed loop transfers wherever tests are authoritative, each test is\n230\u2192costly, the design space is mixed, and several performance goals conflict:\n231\u2192\n232\u2192- Tensegrity *lattices* and more complex architectures \u2014 exactly where hand\n233\u2192 assembly becomes infeasible and single-build co-fabrication pays off.\n234\u2192- Migration from the PLA\u2013TPU proxy toward flight-relevant materials and\n235\u2192 impact regimes.\n236\u2192- Safety hardware on Earth (e.g., the tensegrity crutch tip \u2014 future work).\n237\u2192- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238\u2192 handling, testing) in the loop.\n239\u2192\n240\u2192### Evidence / results\n241\u2192\n242\u2192*What we found \u2014 ground the conclusion in shown data*\n243\u2192\n244\u2192Build the talk around a quantitative three-slide sequence (per the\n245\u2192mock-audience report; insert observed values once the campaign completes):\n246\u2192\n247\u21921. **Campaign ledger** \u2014 \"The campaign used N specimens across B adaptive\n248\u2192 batches under a fixed physical-test budget\": initialization vs. adaptive\n249\u2192 batches, replicates, print failures, cycle time, and the numerical value\n250\u2192 (and rationale) of the force cap.\n251\u21922. **Budget-matched baseline** \u2014 \"Adaptive selection improved the feasible\n252\u2192 design set faster than [predeclared baseline]\": performance vs. number of\n253\u2192 physical tests against Sobol/random sampling at the same budget, with\n254\u2192 uncertainty where available.\n255\u21923. **Measured feasible Pareto front** \u2014 specific energy absorption vs.\n256\u2192 compaction efficiency among designs satisfying the force cap, with\n257\u2192 replicate spread, marked infeasible designs, and specimen photos or\n258\u2192 force\u2013time traces for representative Pareto designs.\n259\u2192\n260\u2192Supporting raw-data candidates: drop-test force\u2013time traces, Sobol /\n261\u2192first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262\u2192\n263\u2192### Suggested 15-minute allocation\n264\u2192\n265\u2192*(From the mock-audience report \u2014 results get the largest block.)*\n266\u2192\n267\u2192| Time | Content |\n268\u2192|---|---|\n269\u2192| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270\u2192| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271\u2192| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272\u2192| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273\u2192| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274\u2192| 1 min | Limitations and transfer conditions |\n275\u2192| 1 min | Measured conclusion + bookend |\n276\u2192\n277\u2192Move kernel choices, encodings, and other implementation detail to backup\n278\u2192slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279\u2192[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280\u2192\n281\u2192## Closing\n282\u2192\n283\u2192### Review\n284\u2192\n285\u2192*A recap of the body, leading into the conclusion*\n286\u2192\n287\u2192Using real, measured impact data from additively manufactured\n288\u2192tensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\n289\u2192optimization routine \u2014 we rapidly iterate toward better energy absorbers.\n290\u2192This feedback loop makes tensegrity optimization dramatically faster: dozens\n291\u2192of prints instead of the hundreds a traditional design of experiments over\n292\u2192this design space would require.\n293\u2192\n294\u2192### Conclusion\n295\u2192\n296\u2192*What the above means to the audience in the end*\n297\u2192\n298\u2192Because the loop runs on physical measurements, it is useful precisely where\n299\u2192model calibration is not worth its cost \u2014 and the same closed-loop pattern\n300\u2192should transfer (a conditional claim, pending evidence) to other additively\n301\u2192manufactured architectures whose performance is dominated by\n302\u2192hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303\u2192*use this loop when tests are authoritative, each test is costly, the design\n304\u2192space is mixed, and several performance goals conflict.* For tensegrity\n305\u2192specifically, it opens a path from this T3 proxy study toward lattices,\n306\u2192flight-relevant materials, payload protection on other planets, and safety\n307\u2192hardware here on Earth (e.g., the crutch tip, as future work).\n308\u2192\n309\u2192### Close\n310\u2192\n311\u2192*A way to end the presentation clearly and elegantly*\n312\u2192\n313\u2192Return to the opening image: the next planetary lander's energy absorber \u2014 a\n314\u2192structure that has to survive not one impact but many \u2014 doesn't have to take\n315\u2192years of hand-built prototypes; with this loop, candidate designs can be\n316\u2192designed, printed, and tested against real impacts in weeks instead of months\n317\u2192or longer. End on the measured engineering result and its transfer condition,\n318\u2192then the bookend: every method that compresses design time moves our ventures\n319\u2192\u2014 on this planet and beyond \u2014 forward.\n320\u2192\n321\u2192---\n322\u2192\n323\u2192## Changes from Draft 2\n324\u2192\n325\u2192Applied from @sgbaird's inline review comments on PR #84 and the\n326\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327\u2192\n328\u2192**From the inline review comments:**\n329\u2192\n330\u21921. **Reusability in the hook** (line 22): the tensegrity lander survives\n331\u2192 multiple drops, unlike crushable/sacrificial alternatives.\n332\u21922. **Proxy-system framing** (lines 49, 87): PLA\u2013TPU would never fly; it is a\n333\u2192 proxy for prototyping a self-driving-lab-style workflow \u2014 stated in a\n334\u2192 scope note up front and re-stated right after the hook.\n335\u21923. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336\u2192 heavy-duty simulation falls short of the experiments (millisecond-scale\n337\u2192 peak shock), not that it merely costs too much.\n338\u21924. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339\u2192 complex structures and especially lattices are not.\n340\u21925. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341\u2192 (temperature, print speed, \u2026) only; the rest is explicitly future work.\n342\u21926. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343\u2192 literature search (or validation of experiments against simulation), plus\n344\u2192 the millisecond-resolution peak-shock argument.\n345\u21927. **Optional Point 4 added** (line 97): future applications, with a note it\n346\u2192 can fold into the Conclusion if time is short.\n347\u21928. **Steel-materials point** (line 104): simulations fall short for the\n348\u2192 actual engineering materials (steel struts/cables), not just printed\n349\u2192 PLA\u2013TPU.\n350\u21929. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351\u2192 in minutes, pitched so the audience sees they can use it too.\n352\u219210. **\"Thousands\" \u2192 \"hundreds\"** (line 173): baseline is now a traditional\n353\u2192 design of experiments over a high-dimensional space (hundreds), in the\n354\u2192 main message, Need, Point 1, and Review.\n355\u219211. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356\u2192 or longer\" for candidate designs.\n357\u2192\n358\u2192**From the mock-audience report's priority revisions:**\n359\u2192\n360\u219212. **Quantitative three-slide results sequence** (Priority 1): campaign\n361\u2192 ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front, with a\n362\u2192 fallback main-message wording if the campaign can't yet support the\n363\u2192 efficiency claim.\n364\u219213. **Anti-simulation premise replaced with a scoped cost decision**\n365\u2192 (Priority 2): Point 1 retitled to the information-value message;\n366\u2192 \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367\u2192 anticipated with a backup slide.\n368\u219214. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369\u2192 implementation detail moved to backup slides; Q&A prep pointed at the\n370\u2192 report's claims-vs-evidence table.\n371\u219215. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372\u2192 the main talk, machinery in backup slides; the misleading\n373\u2192 \"qNEHVI over standard expected improvement\" comparison replaced with\n374\u2192 \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375\u219216. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376\u2192 sequence in Point 3, so the structure isn't remembered as a generic crush\n377\u2192 structure or lattice.\n378\u219217. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379\u2192 co-fabrication / no joining of parts,\" with a note to list remaining\n380\u2192 manual steps and an automation-status caveat.\n381\u219218. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382\u2192 compaction efficiency, constraint = force cap, deliverable = *feasible*\n383\u2192 Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384\u219219. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385\u2192 transfer claim marked conditional, and the close ends on the measured\n386\u2192 result before the bookend.\n387\u2192\n388\u2192## Changes from Draft 1\n389\u2192\n390\u2192Feedback from the PR #84 review, applied in Draft 2:\n391\u2192\n392\u21921. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n393\u2192 dozens-not-thousands claim); body and message now carry the same story.\n394\u21922. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n395\u2192 manuscript's positioning (#75/#76) is that the physical experiment is the\n396\u2192 source of truth, explicitly avoiding calibrated FE simulation.\n397\u21923. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n398\u2192 parametric design space and avoid an over-claim to a design-automation\n399\u2192 audience.\n400\u21924. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401\u2192 energy absorption and compaction efficiency, cap peak transmitted force.\n402\u21925. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403\u2192 *and* the space is too big to brute-force \u2014 which makes BO + AM the\n404\u2192 inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405\u2192 above.)*\n406\u21926. **Point 1 given its own content** (the *why* behind the inefficiency)\n407\u2192 instead of restating the Need.\n408\u21927. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409\u2192 the specifics (noise-aware acquisition, small batches, multi-objective).\n410\u21928. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411\u2192 (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412\u2192 above.)*\n413\u21929. **Evidence/results slot added** before the Closing, so the Conclusion is\n414\u2192 grounded in shown data rather than three hedged \"may\"s.\n415\u219210. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n416\u2192 Ball Bot visual; verified video links included.\n417\u219211. **Close bookends the opening** (the next lander's absorber, designed in\n418\u2192 weeks) before the broader humankind line.\n419\u219212. **Spelling**: Doumont (was \"Duomont\").", + "name": "read", + "tool_call_id": "call_93haurOMDGiH2mQFFbaMaYKn", + "info": { + "start_ts": 8147.331065886, + "end_ts": 8147.336467845 + } + }, + { + "role": "tool", + "content": " 1\u2192# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n 2\u2192\n 3\u2192Video: (assigned in #83)\n 4\u2192\n 5\u2192Transcribed from @me-madsen's handwritten notes\n 6\u2192([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7\u2192dated 2026-07-15).\n 8\u2192\n 9\u2192## Overview\n10\u2192\n11\u2192A presentation is all about the right content, displayed the right way, at the\n12\u2192right time.\n13\u2192\n14\u2192- Verbal communication is sequential.\n15\u2192- Message is *interpretation* of the information.\n16\u2192 - Ensure the audience can get the most out of what you're doing.\n17\u2192 - Don't just give them information \u2014 help them understand it, interpret it,\n18\u2192 and internalize it.\n19\u2192\n20\u2192## Doumont's suggested rules for a presentation\n21\u2192\n22\u21920. Ensure you have **messages** for your audience, not just words.\n23\u21921. **Adapt to your audience.**\n24\u2192 - Think like the audience, not the speaker.\n25\u21922. **Maximize signal-to-noise ratio.**\n26\u2192 - Reduce noise (animations, laser pointers, anything that pulls attention\n27\u2192 away from your message).\n28\u21923. **Use effective redundancy.**\n29\u2192 - Tell your message in word *and* on slide: someone deaf should understand\n30\u2192 your messages just as well as someone blind.\n31\u2192 - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32\u2192 date).\n33\u2192\n34\u2192## When creating slides\n35\u2192\n36\u2192- If the audience doesn't need it, why put it on the slides?\n37\u2192- People shouldn't have to count the items on a slide to know how many are\n38\u2192 there.\n39\u2192- Don't use your slides as your \"presentation notes.\"\n40\u2192 - Don't show your notes to the audience.\n41\u2192 - **A bad slide is worse than no slide at all.**\n42\u2192- **One message per slide** is a good rule of thumb.\n43\u2192 - Prioritize the *so what* (or *therefore what*) of your message.\n44\u2192- Make it easy to interpret the information on the slide:\n45\u2192 - 3\u20135 items at most.\n46\u2192 - Align the title left.\n47\u2192 - Do not clutter slides with repetitive or unnecessary information: logos,\n48\u2192 affiliation, date, slide numbers.\n49\u2192- Doumont's suggested workflow:\n50\u2192 1. **Start with the title.**\n51\u2192 - Use a full sentence in the title.\n52\u2192 - The title is the main message of the slide.\n53\u2192 - Make it easy to read: left-aligned, with intentional line breaks.\n54\u2192 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55\u2192- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56\u2192\n57\u2192## General preparation\n58\u2192\n59\u2192- Planning for audience, content, and structure matters more than materials\n60\u2192 (i.e., slides).\n61\u2192- Try using one sheet of paper for presentation preparation.\n62\u2192- It's most important that your presentation is:\n63\u2192 - well planned, structured, and delivered;\n64\u2192 - **if you don't have a lot of time to prepare, don't focus on making\n65\u2192 slides.**\n66\u2192- Practice the presentation a few times. Practice at least once *without* your\n67\u2192 slideshow!\n68\u2192- If doing a handout, keep it separate from the slides you're preparing.\n69\u2192- Impress the audience with your body.\n70\u2192 - Use gestures, not laser pointers.\n71\u2192 - \"Control\" the audience with your body \u2014 keep their attention on your\n72\u2192 presentation.\n73\u2192\n74\u2192## Impressions / what to apply to the IDETC presentation\n75\u2192\n76\u2192\n77\u2192\n78\u2192## Questions / things to discuss\n79\u2192\n80\u2192", + "name": "read", + "tool_call_id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "info": { + "start_ts": 8147.331133416, + "end_ts": 8147.338304015 + } + }, + { + "role": "tool", + "content": " 1\u2192\\documentclass[10pt]{article}\n 2\u2192\n 3\u2192% --- Packages ---\n 4\u2192\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5\u2192\\usepackage[hidelinks]{hyperref}\n 6\u2192\\usepackage{parskip}\n 7\u2192\\usepackage{graphicx}\n 8\u2192\\usepackage{caption}\n 9\u2192\\usepackage{titlesec}\n 10\u2192\\usepackage{wrapfig}\n 11\u2192\\usepackage[numbers,sort&compress]{natbib}\n 12\u2192\n 13\u2192% --- Formatting ---\n 14\u2192\\setlength{\\parskip}{0.2em}\n 15\u2192\\setlength{\\parindent}{0em}\n 16\u2192\\titleformat*{\\section}{\\large\\bfseries}\n 17\u2192\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18\u2192\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19\u2192\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20\u2192\\setlength{\\emergencystretch}{2em}\n 21\u2192\\setlength{\\intextsep}{0.2em}\n 22\u2192\\bibliographystyle{abbrvnat}\n 23\u2192\n 24\u2192\\begin{document}\n 25\u2192\n 26\u2192\\begin{center}\n 27\u2192{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28\u21923D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29\u2192\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30\u2192\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31\u2192\\end{center}\n 32\u2192\n 33\u2192\\vspace{0.4em}\n 34\u2192\n 35\u2192\\section*{Abstract}\n 36\u2192Tensegrity-inspired architectures---rigid struts suspended within a\n 37\u2192continuous flexible network---can exhibit tunable nonlinear\n 38\u2192force--displacement responses and favorable energy absorption per unit\n 39\u2192mass, motivating their use in lightweight energy-absorbing and protective\n 40\u2192structures\n 41\u2192\\citep{skelton2009tensegrity,pajunen2019design}.\n 42\u2192Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43\u2192struts and flexible (TPU) elements in a single build\n 44\u2192\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45\u2192space---strut geometry, tension-element cross-section, connectivity\n 46\u2192topology, and unit-cell tiling---is too large to explore by trial and\n 47\u2192error. We present a closed-loop experimental campaign that uses\n 48\u2192Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49\u2192to drive a design--print--test workflow operating directly on physical\n 50\u2192measurements, without relying on calibrated finite-element simulation\n 51\u2192for objective evaluation.\n 52\u2192\n 53\u2192\\section{Introduction and Motivation}\n 54\u2192\n 55\u2192\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56\u2192\\centering\n 57\u2192\\vspace{-0.4em}\n 58\u2192\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59\u2192\\captionsetup{font=small}\n 60\u2192\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61\u2192Candidate tensegrity-inspired unit cells are instantiated from a\n 62\u2192parameter vector, fabricated by multi-material FDM, and tested under\n 63\u2192quasi-static compression and drop-weight impact. Measured peak force,\n 64\u2192specific energy absorption (SEA), and compaction efficiency update a\n 65\u2192Gaussian-process surrogate that proposes the next batch of designs.}\n 66\u2192\\label{fig:overview}\n 67\u2192\\vspace{-0.4em}\n 68\u2192\\end{wrapfigure}\n 69\u2192\n 70\u2192Tensegrity structures combine compression-only members with a\n 71\u2192pre-tensioned tension network to achieve stiffness without rigid\n 72\u2192joinery~\\citep{skelton2009tensegrity}. Their\n 73\u2192nonlinear, often load-limiting force--displacement response makes them\n 74\u2192attractive for impact mitigation, packaging, and wearable protective\n 75\u2192gear~\\citep{pajunen2019design}. Idealized\n 76\u2192cable--strut tensegrities are difficult to manufacture at scale, but\n 77\u2192\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78\u21923D printing reproduce key tensegrity behaviors---including\n 79\u2192post-buckling stability and load-limiting force--displacement\n 80\u2192response~\\citep{pajunen2019design}---motivating our extension to\n 81\u2192multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82\u2192elements. PLA provides rigid struts; TPU provides flexible,\n 83\u2192rate-dependent tension elements whose viscoelasticity is expected to\n 84\u2192contribute hysteretic energy dissipation under impact loading,\n 85\u2192consistent with the energy-absorbing behavior reported for rigid--TPU\n 86\u2192multi-material prints~\\citep{khatri2024energy}.\n 87\u2192\n 88\u2192The challenge is that small geometric and topological changes can\n 89\u2192produce large changes in the measured response, and high-fidelity\n 90\u2192simulation of FDM specimens is computationally expensive and often\n 91\u2192struggles to capture physical defects such as interfacial slip and\n 92\u2192TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93\u2192trial-and-error sweeps over the joint design space are also infeasible:\n 94\u2192even modest parameter resolutions yield thousands of candidates, each\n 95\u2192requiring print and test time. We instead treat the physical\n 96\u2192experiment as the source of truth and use BO to select which specimens\n 97\u2192are most informative to fabricate next---an approach used in related\n 98\u2192closed-loop materials and structures\n 99\u2192campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100\u2192Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101\u2192set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102\u2192designs using parallel, noisy multi-objective BO driven by measured\n103\u2192$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104\u2192\n105\u2192\\section{Approach}\n106\u2192\\subsection{Closed-Loop Workflow}\n107\u2192Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108\u2192proceeds in four steps: (i)~a parameterized unit-cell design is\n109\u2192instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110\u2192is sliced and printed on a multi-material FDM system; (iii)~it is\n111\u2192tested under quasi-static compression and instrumented drop-weight\n112\u2192impact; and (iv)~the resulting performance metrics update a\n113\u2192Gaussian-process (GP) surrogate, which a batch acquisition function\n114\u2192queries to recommend the next set of candidates. Surrogate updating\n115\u2192and experiment selection are automated; specimen handling, slicing,\n116\u2192and test setup remain manual.\n117\u2192\n118\u2192\\subsection{Design Space}\n119\u2192We parameterize a family of unit cells using a core-wrapping\n120\u2192architecture in which rigid PLA struts are enclosed by continuous TPU\n121\u2192skins; the wrapping rationale follows multi-material FDM strategies in\n122\u2192which rigid panels are wrapped by stretchable soft material to prevent\n123\u2192delamination and improve interfacial bonding at the rigid--flexible\n124\u2192interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125\u2192comprises continuous geometric variables (strut diameter and length,\n126\u2192tension-element width and thickness), an integer strut count per unit\n127\u2192cell, and two categorical variables (connectivity topology, unit-cell\n128\u2192tiling pattern). Categorical choices are encoded explicitly rather\n129\u2192than embedded in a continuous metric, and bounds on the continuous\n130\u2192variables are chosen to remain within the printer's resolution and\n131\u2192overhang limits and to keep specimens in a common form factor for the\n132\u2192test fixtures.\n133\u2192\n134\u2192\\subsection{Surrogate and Acquisition}\n135\u2192The GP surrogate is trained on three measured performance metrics:\n136\u2192peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137\u2192(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138\u2192objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139\u2192likelihood with priors to avoid pathological length scales at small $n$.\n140\u2192Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141\u2192interfacial defects motivates our use of q-noisy expected hypervolume\n142\u2192improvement (qNEHVI), which explicitly accounts for observation noise\n143\u2192in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144\u2192expected-improvement variants. Print failures are treated as a\n145\u2192probabilistic feasibility constraint: the acquisition score for each\n146\u2192candidate is its qNEHVI improvement weighted by the GP-estimated\n147\u2192probability of successful fabrication, following recent constrained\n148\u2192multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149\u2192Where applicable, we use numerically stable log-space variants of\n150\u2192improvement-based acquisitions to mitigate vanishing-gradient\n151\u2192pathologies as data and constraints\n152\u2192accumulate~\\citep{ament2023logei}.\n153\u2192\n154\u2192\\section{Methods}\n155\u2192\\subsection{Fabrication}\n156\u2192Specimens are fabricated on a multi-material FDM system capable of\n157\u2192co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158\u2192Print parameters (temperature, line width, infill, retraction) are\n159\u2192held fixed within a batch to isolate the effect of the design\n160\u2192variables. Each specimen is weighed and dimensionally inspected prior\n161\u2192to testing so that mass and geometric deviations are available as\n162\u2192covariates.\n163\u2192\n164\u2192\\subsection{Mechanical Testing}\n165\u2192Quasi-static compression is performed on a screw-driven load frame at\n166\u2192a fixed nominal strain rate, with force--displacement recorded to\n167\u2192densification. Drop-weight impact testing uses an instrumented tup at\n168\u2192a fixed impact energy; transmitted force is captured at high sample\n169\u2192rate, and specimens are imaged before and after impact to document\n170\u2192failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171\u2192unit mass, and compaction efficiency $\\eta$, defined consistently\n172\u2192across the campaign. A control specimen from an earlier batch is\n173\u2192retested periodically to monitor TPU batch-to-batch and printer drift.\n174\u2192\n175\u2192\\section{Expected Outcomes and Discussion}\n176\u2192We will report how BO-selected designs evolve across iterations, how\n177\u2192GP surrogate predictions and uncertainties change as data accumulate,\n178\u2192and which Pareto-efficient trade-offs emerge between SEA and peak\n179\u2192transmitted force within the explored design space. We will also\n180\u2192discuss practical lessons for operating the closed-loop workflow,\n181\u2192including handling of print failures, batch-to-batch variability in\n182\u2192TPU response, the influence of the rigid--flexible interface on\n183\u2192energy absorption, and the exploration--exploitation balance of the\n184\u2192acquisition function under realistic experimental noise. We expect\n185\u2192the campaign to clarify where physical experimentation is genuinely\n186\u2192required versus where simpler analytical or multi-fidelity surrogate\n187\u2192shortcuts~\\citep{mo2023accelerated} could be\n188\u2192substituted in future work.\n189\u2192\n190\u2192\\section{Conclusion}\n191\u2192This work demonstrates a partially automated, experiment-driven\n192\u2192closed-loop pipeline for designing multi-material 3D-printed\n193\u2192tensegrity-inspired energy absorbers, with surrogate updating and\n194\u2192candidate selection automated and fabrication and testing kept manual.\n195\u2192By updating a GP surrogate directly from physical measurements, the\n196\u2192workflow avoids direct dependence on calibrated finite-element\n197\u2192simulation for objective evaluation while still providing principled,\n198\u2192multi-objective design recommendations. The same closed-loop pattern is\n199\u2192expected to transfer to other multi-material additively manufactured\n200\u2192architectures whose performance is dominated by hard-to-simulate\n201\u2192effects.\n202\u2192\n203\u2192{\\tiny\n204\u2192\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205\u2192\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206\u2192% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207\u2192% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208\u2192\\providecommand{\\doi}[1]{}\n209\u2192\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210\u2192\\bibliography{references}\n211\u2192}\n212\u2192\n213\u2192\\end{document}", + "name": "read", + "tool_call_id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "info": { + "start_ts": 8147.332633466, + "end_ts": 8147.339043095 + } + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "callback_type": "after_agent_get_asv" + }, + "action": { + "call_id": { + "run_id": "f8b11daf-a506-414f-887c-a22c881e34cd", + "fwd_id": "38d5e2c5-ee05-46de-9864-cfd697621798" + }, + "op_name": "IdentityOp-10b149563727", + "op_class_name": "ldp.graph.common_ops.IdentityOp", + "value": { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_Dr3LokFRC7vtM1o4UzyNoEFW", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\", \"offset\": 174, \"limit\": 100}", + "name": "read" + } + } + ] + }, + "logprob": null + } + } + }, + "created_at": "2026-07-26T02:29:25.713555Z" + }, + { + "id": "334728fa-670d-472e-bfe8-bd2fd82d4c38", + "trajectory_id": "afade0b1-f49e-4031-a86d-cb5ec4652536", + "trajectory_timestep": 0, + "step": "before_transition", + "state": { + "transition": { + "timestep": 0, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "tool", + "content": " 1\u2192# IDETC Tensegrity Slides \u2014 Draft 1 (extracted content)\n 2\u2192\n 3\u2192Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4\u2192\n 5\u2192\n 6\u2192## Slide 1 \u2014 layout: Title Only\n 7\u2192\n 8\u2192**On-slide text:**\n 9\u2192- **Title 1**: Let\u2019s build better tensegrity structures faster\n 10\u2192- **TextBox 4**: Marcus Madsen\n 11\u2192Undergraduate researcher under Sterling Baird, Ph.D.\n 12\u2192- **TextBox 6**: IDETC 2026\n 13\u2192\n 14\u2192**Non-text shapes / media / placeholders:**\n 15\u2192- [PICTURE (13)] Picture 3\n 16\u2192- [PICTURE (13)] Picture 8\n 17\u2192\n 18\u2192**Presenter notes:**\n 19\u2192> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20\u2192> \n 21\u2192> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22\u2192\n 23\u2192\n 24\u2192## Slide 2 \u2014 layout: Title Only\n 25\u2192\n 26\u2192**On-slide text:**\n 27\u2192- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings\u2014and to problems closer to home.\n 28\u2192\n 29\u2192**Non-text shapes / media / placeholders:**\n 30\u2192- [MEDIA (16)] Online Media 5\n 31\u2192\n 32\u2192**Presenter notes:**\n 33\u2192> Replace this video with a downloaded and trimmed version of just the fall. \n 34\u2192> \n 35\u2192> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle \u2014 thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander\u00a0is\u00a0the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit\u00a0and get back up\u00a0\u2014 it survives multiple drops and keeps working.\n 36\u2192\n 37\u2192\n 38\u2192## Slide 3 (HIDDEN) \u2014 layout: Title Only\n 39\u2192\n 40\u2192**On-slide text:**\n 41\u2192- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42\u2192- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43\u2192- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44\u2192\n 45\u2192**Presenter notes:**\n 46\u2192> This might be better explained purely vocally while on the previous slide.\n 47\u2192\n 48\u2192\n 49\u2192## Slide 4 \u2014 layout: Title Only\n 50\u2192\n 51\u2192**On-slide text:**\n 52\u2192- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53\u2192- **TextBox 3**: Iteration\n 54\u2192- **TextBox 5**: Building\n 55\u2192- **TextBox 7**: Testing\n 56\u2192\n 57\u2192**Non-text shapes / media / placeholders:**\n 58\u2192- [MEDIA (16)] AdobeStock_207717222\n 59\u2192- [MEDIA (16)] Online Media 2\n 60\u2192- [PICTURE (13)] Picture 14\n 61\u2192- [PICTURE (13)] Picture 16\n 62\u2192- [PICTURE (13)] Picture 18\n 63\u2192\n 64\u2192**Presenter notes:**\n 65\u2192> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn\u2019t become distracting because it\u2019s fun to watch it spin.\n 66\u2192> \n 67\u2192> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68\u2192\n 69\u2192\n 70\u2192## Slide 5 (HIDDEN) \u2014 layout: Title Only\n 71\u2192\n 72\u2192**On-slide text:**\n 73\u2192- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74\u2192- **TextBox 14**: Baird et al. Matter (2022)\n 75\u2192\n 76\u2192**Non-text shapes / media / placeholders:**\n 77\u2192- [GROUP (6)] Group 2\n 78\u2192\n 79\u2192**Presenter notes:**\n 80\u2192> Consult Sterling on this slide. I don\u2019t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it\u2019s well enough known that it\u2019d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81\u2192\n 82\u2192\n 83\u2192## Slide 6 (HIDDEN) \u2014 layout: Title Only\n 84\u2192\n 85\u2192**On-slide text:**\n 86\u2192- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87\u2192- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88\u2192\n 89\u2192**Presenter notes:**\n 90\u2192> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91\u2192\n 92\u2192\n 93\u2192## Slide 7 \u2014 layout: Title Only\n 94\u2192\n 95\u2192**On-slide text:**\n 96\u2192- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97\u2192- **TextBox 5**: 6 variables\n 98\u2192- **TextBox 7**: 2 types of input data\n 99\u2192- **TextBox 9**: 2 objectives\n100\u2192- **TextBox 17**: *image of blackbox*\n101\u2192\n102\u2192**Non-text shapes / media / placeholders:**\n103\u2192- [LINE (9)] Connector: Curved 11\n104\u2192- [LINE (9)] Connector: Curved 13\n105\u2192- [LINE (9)] Connector: Curved 15\n106\u2192\n107\u2192**Presenter notes:**\n108\u2192> Very rough slide as I\u2019m not entirely sure what graphic or text to put on this, partially because I\u2019m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109\u2192\n110\u2192\n111\u2192## Slide 8 \u2014 layout: Title Only\n112\u2192\n113\u2192**On-slide text:**\n114\u2192- **Title 1**: Multi-material additive manufacturing supplies that data fast \u2014 single-build co-fabrication, with no joining of parts.\n115\u2192\n116\u2192**Non-text shapes / media / placeholders:**\n117\u2192- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118\u2192- [PICTURE (13)] Picture 4\n119\u2192\n120\u2192**Presenter notes:**\n121\u2192> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122\u2192\n123\u2192\n124\u2192## Slide 9 \u2014 layout: Title Only\n125\u2192\n126\u2192**On-slide text:**\n127\u2192- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128\u2192- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129\u2192- **TextBox 5**: *placeholder for slo motion of drop test*\n130\u2192\n131\u2192\n132\u2192## Slide 10 \u2014 layout: Title Only\n133\u2192\n134\u2192**On-slide text:**\n135\u2192- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136\u2192- **TextBox 3**: Printing\n137\u2192- **TextBox 5**: Testing\n138\u2192- **TextBox 7**: BO feed\n139\u2192\n140\u2192**Non-text shapes / media / placeholders:**\n141\u2192- [PICTURE (13)] Picture 14\n142\u2192- [PICTURE (13)] Picture 16\n143\u2192- [PICTURE (13)] Picture 18\n144\u2192- [PICTURE (13)] Graphic 8\n145\u2192\n146\u2192**Presenter notes:**\n147\u2192> I think it might be better if we replaced \u201cprinting\u201d with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148\u2192\n149\u2192\n150\u2192## Slide 11 (HIDDEN) \u2014 layout: Title Only\n151\u2192\n152\u2192**On-slide text:**\n153\u2192- **Title 1**: Reserved for results\n154\u2192\n155\u2192**Presenter notes:**\n156\u2192> Would be helpful if we could show that this process works in this case.\n157\u2192\n158\u2192\n159\u2192## Slide 12 \u2014 layout: Title Only\n160\u2192\n161\u2192**On-slide text:**\n162\u2192- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163\u2192- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164\u2192- **TextBox 3**: *image to show lattice structure\n165\u2192\n166\u2192\n167\u2192## Slide 13 \u2014 layout: Title Only\n168\u2192\n169\u2192**Non-text shapes / media / placeholders:**\n170\u2192- [PICTURE (13)] Picture 3\n171\u2192\n172\u2192**Presenter notes:**\n173\u2192> Slide for questions at the end\n174\u2192\n175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "info": { + "start_ts": 8147.330900226, + "end_ts": 8147.337548875 + } + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Presentation \u2014 Doumont Presentation Structure Template\n 2\u2192\n 3\u2192Transcribed from Jean-luc Doumont's presentation-structure template\n 4\u2192([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5\u2192adapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\n 6\u2192All rights reserved. Can be downloaded from .\n 7\u2192\n 8\u2192> To design your presentation, write down your ideas for each component below.\n 9\u2192> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10\u2192\n 11\u2192**Draft 3** \u2014 applies the inline review comments from @sgbaird on PR #84 and the\n 12\u2192priority revisions from the\n 13\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14\u2192Summaries of what changed are at the bottom of this file\n 15\u2192([Draft 2 \u2192 3](#changes-from-draft-2), [Draft 1 \u2192 2](#changes-from-draft-1)).\n 16\u2192\n 17\u2192**Scope note (frame everything below with this):** the study presented is the\n 18\u2192optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19\u2192processing parameters** (e.g., nozzle temperature, print speed). PLA\u2013TPU is a\n 20\u2192**proxy system** \u2014 not flight material \u2014 used to prototype a self-driving-lab\n 21\u2192style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22\u2192The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23\u2192beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24\u2192\n 25\u2192## Opening\n 26\u2192\n 27\u2192### Attention getter\n 28\u2192\n 29\u2192*A way to lead the audience to the need efficiently*\n 30\u2192\n 31\u2192Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32\u2192landings \u2014 and to problems closer to home.\n 33\u2192\n 34\u2192One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35\u2192clip) do the work:\n 36\u2192\n 37\u2192> Landing a payload on another planet means surviving an impact in conditions\n 38\u2192> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n 39\u2192> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40\u2192> structure: the lander *is* the energy absorber. And unlike crushable\n 41\u2192> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42\u2192> hit **and get back up** \u2014 it survives multiple drops and keeps working.\n 43\u2192\n 44\u2192Candidate Super Ball Bot videos to show (verified links):\n 45\u2192\n 46\u2192- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n 47\u2192 (official channel); the canonical overview.\n 48\u2192- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49\u2192 \u2014 Wall Street Journal; prototype drop footage.\n 50\u2192- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51\u2192 \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n 52\u2192\n 53\u2192Immediately after the hook, state the present study scope in one sentence\n 54\u2192(per the scope note above): *we study a printed PLA\u2013TPU T3 prism as a proxy\n 55\u2192system for prototyping an autonomous tensegrity-optimization workflow* \u2014 so\n 56\u2192the planetary framing never outruns the demonstrated scope.\n 57\u2192\n 58\u2192\n 60\u2192\n 61\u2192### Need\n 62\u2192\n 63\u2192*A difference between actual and desired situations*\n 64\u2192\n 65\u2192**Actual situation** (a two-sided pincer \u2014 stated as a scoped engineering\n 66\u2192judgment, not an absolute):\n 67\u2192\n 68\u2192- For these structures, obtaining trustworthy objective values from a\n 69\u2192 validated high-fidelity model would require substantial calibration of\n 70\u2192 interface, rate, contact, and defect behavior \u2014 and even heavy-duty\n 71\u2192 simulation falls short of the experiments we are running: peak transmitted\n 72\u2192 shock is a millisecond-scale transient that is notoriously difficult to\n 73\u2192 resolve numerically. This holds even for idealized cable\u2013strut tensegrities\n 74\u2192 in their actual engineering materials (steel or similar), not just for\n 75\u2192 printed PLA\u2013TPU.\n 76\u2192 \n 79\u2192- Exhaustive physical search fails on cost: a traditional design of\n 80\u2192 experiments over this relatively high-dimensional space (geometry + FDM\n 81\u2192 process parameters) would demand hundreds of specimens, each costing print\n 82\u2192 and test time.\n 83\u2192- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84\u2192 more complex structures \u2014 especially tensegrity *lattices* \u2014 become\n 85\u2192 extremely difficult and ultimately infeasible to hand-build.\n 86\u2192\n 87\u2192**Desired situation:**\n 88\u2192\n 89\u2192- Trustworthy performance data without hand assembly (easy creation of\n 90\u2192 tensegrity structures, scalable beyond a single prism).\n 91\u2192- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n 92\u2192 not the hundreds a traditional design of experiments would require.\n 93\u2192\n 94\u2192### Task\n 95\u2192\n 96\u2192*What I decided/was asked to do to address the need*\n 97\u2192\n 98\u2192Build a closed-loop design\u2013print\u2013test workflow that optimizes a **T3-prism\n 99\u2192tensegrity-inspired energy absorber together with its FDM processing\n100\u2192parameters** (e.g., temperature, print speed) directly from real, measured\n101\u2192impact data \u2014 maximizing energy absorbed per gram (specific energy\n102\u2192absorption) and compaction efficiency, subject to a cap on the peak force\n103\u2192transmitted to the payload. Extensions beyond the T3 prism are future work.\n104\u2192\n105\u2192### Main message\n106\u2192\n107\u2192*The one sentence I want my audience to remember*\n108\u2192\n109\u2192By closing the loop between multi-material 3D printing and Bayesian\n110\u2192optimization, we can optimize tensegrity-inspired energy absorbers directly\n111\u2192from real impact data \u2014 in dozens of prints, not the hundreds a traditional\n112\u2192design of experiments would demand.\n113\u2192\n114\u2192\n118\u2192\n119\u2192### Preview\n120\u2192\n121\u2192*A map of the body (ideally three points, max. five)*\n122\u2192\n123\u21921. When model calibration and exhaustive testing are both costly, each\n124\u2192 physical specimen must be chosen for its information value \u2014 so the\n125\u2192 measured experiment is our source of truth.\n126\u21922. Bayesian optimization makes physical experimentation affordable: it handles\n127\u2192 noisy measurements, small batches, and the constrained multi-objective\n128\u2192 trade-off \u2014 and Honegumi let us scaffold it in minutes.\n129\u21923. Multi-material additive manufacturing closes the loop: single-build PLA\u2013TPU\n130\u2192 co-fabrication turns each proposed design into test data in hours, with no\n131\u2192 joining of parts.\n132\u21924. *(Optional fourth point, if time allows)* The same closed loop points at\n133\u2192 what comes next: tensegrity lattices, flight-relevant materials, and\n134\u2192 safety hardware on Earth.\n135\u2192\n136\u2192## Body\n137\u2192\n138\u2192### Point 1\n139\u2192\n140\u2192When model calibration and exhaustive testing are both costly, each physical\n141\u2192specimen must be chosen for its information value.\n142\u2192\n143\u2192- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144\u2192 these structures, a sufficiently calibrated multiphysics model is expensive\n145\u2192 to build relative to the design campaign itself \u2014 and the hardest quantity,\n146\u2192 peak transmitted shock, is a millisecond-scale transient that even\n147\u2192 heavy-duty simulation struggles to resolve. The gap persists in the actual\n148\u2192 engineering materials a real lander would use (steel struts and cables),\n149\u2192 so this is not merely an artifact of printed PLA\u2013TPU.\n150\u2192- The alternative \u2014 sweeping the design space physically \u2014 fails on cost: a\n151\u2192 traditional design of experiments over geometry plus FDM process parameters\n152\u2192 would take hundreds of specimens, each needing print + test time.\n153\u2192- Therefore: treat the physical experiment as the authoritative source of\n154\u2192 objective values, and be ruthless about *which* experiments to run.\n155\u2192- Anticipate the FEA-veteran pushback (\"inconvenient \u2260 untrustworthy\"): frame\n156\u2192 it as a cost-and-dependence decision, and keep a backup slide with the\n157\u2192 literature (or our own validation) on simulation\u2013experiment discrepancy for\n158\u2192 impact transients.\n159\u2192\n160\u2192### Transition\n161\u2192\n162\u2192If every data point must be a real experiment, we need a method that extracts\n163\u2192the most from every specimen \u2014 we need to make the process radically more\n164\u2192sample-efficient.\n165\u2192\n166\u2192### Point 2\n167\u2192\n168\u2192Bayesian optimization makes physical experimentation affordable, even with\n169\u2192limited, noisy data.\n170\u2192\n171\u2192- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172\u2192 visual in the main talk; implementation detail goes to backup slides.\n173\u2192 Spoken version:\n174\u2192 > \"After each batch, the model estimates both expected performance and\n175\u2192 > uncertainty across the design space. The acquisition rule selects a small\n176\u2192 > next batch that is most likely to expand the feasible trade-off frontier,\n177\u2192 > while accounting for noisy tests and print failures.\"\n178\u2192- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179\u2192 motivate a noise-aware acquisition function (qNEHVI) rather than\n180\u2192 non-noise-aware alternatives. (Backup slide: how observation noise is\n181\u2192 actually modeled \u2014 replicates, noise identification \u2014 and budget-matched\n182\u2192 baselines such as Sobol/random sampling.)\n183\u2192- Small parallel batches: the surrogate proposes the next batch of specimens\n184\u2192 to print, balancing exploration and exploitation.\n185\u2192- Constrained multi-objective by construction: **objectives** = maximize\n186\u2192 specific energy absorption and compaction efficiency; **constraint** = peak\n187\u2192 transmitted force must stay under a cap; the deliverable is the *feasible*\n188\u2192 Pareto front between the two objectives among force-cap-satisfying designs.\n189\u2192 (Keep this formulation consistent everywhere \u2014 do not present \"SEA vs. peak\n190\u2192 force\" as the trade-off.)\n191\u2192- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192\u2192 this advanced task (noisy, batched, constrained, multi-objective) in\n193\u2192 minutes using [Honegumi](https://honegumi.readthedocs.io/) \u2014 the audience\n194\u2192 can do the same for their own problems; show the code-template picker\n195\u2192 briefly so they see how low the barrier is.\n196\u2192\n197\u2192### Transition\n198\u2192\n199\u2192The optimizer is no longer the bottleneck \u2014 the limit is now providing real\n200\u2192data quickly enough.\n201\u2192\n202\u2192### Point 3\n203\u2192\n204\u2192Multi-material additive manufacturing supplies that data fast \u2014 single-build\n205\u2192co-fabrication, with no joining of parts.\n206\u2192\n207\u2192- Single-build PLA\u2013TPU co-fabrication: rigid struts and flexible tension\n208\u2192 network in one print \u2014 no joining or hand assembly of members, which closes\n209\u2192 the \"easy creation\" gap from the Need and is what makes future lattices\n210\u2192 feasible at all. (Be precise: if support removal, trimming, or conditioning\n211\u2192 remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212\u2192 assembly.\")\n213\u2192- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214\u2192 as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215\u2192 pre-tension status) plus a short deformation sequence \u2014 so the EDL engineer\n216\u2192 doesn't file it as a \"crush structure\" and the grad student can tell it from\n217\u2192 an ordinary lattice.\n218\u2192- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219\u2192 tested specimen (quasi-static compression + instrumented drop-weight impact)\n220\u2192 in hours. (Backup slide: representative print-to-result time breakdown and\n221\u2192 which steps remain manual \u2014 candidate selection is automated; slicing,\n222\u2192 handling, and testing are not yet.)\n223\u2192- Now we have the data we need to complete the loop.\n224\u2192\n225\u2192### Point 4 (optional \u2014 future applications)\n226\u2192\n227\u2192*Include if timing allows; otherwise fold into the Conclusion.*\n228\u2192\n229\u2192The same closed loop transfers wherever tests are authoritative, each test is\n230\u2192costly, the design space is mixed, and several performance goals conflict:\n231\u2192\n232\u2192- Tensegrity *lattices* and more complex architectures \u2014 exactly where hand\n233\u2192 assembly becomes infeasible and single-build co-fabrication pays off.\n234\u2192- Migration from the PLA\u2013TPU proxy toward flight-relevant materials and\n235\u2192 impact regimes.\n236\u2192- Safety hardware on Earth (e.g., the tensegrity crutch tip \u2014 future work).\n237\u2192- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238\u2192 handling, testing) in the loop.\n239\u2192\n240\u2192### Evidence / results\n241\u2192\n242\u2192*What we found \u2014 ground the conclusion in shown data*\n243\u2192\n244\u2192Build the talk around a quantitative three-slide sequence (per the\n245\u2192mock-audience report; insert observed values once the campaign completes):\n246\u2192\n247\u21921. **Campaign ledger** \u2014 \"The campaign used N specimens across B adaptive\n248\u2192 batches under a fixed physical-test budget\": initialization vs. adaptive\n249\u2192 batches, replicates, print failures, cycle time, and the numerical value\n250\u2192 (and rationale) of the force cap.\n251\u21922. **Budget-matched baseline** \u2014 \"Adaptive selection improved the feasible\n252\u2192 design set faster than [predeclared baseline]\": performance vs. number of\n253\u2192 physical tests against Sobol/random sampling at the same budget, with\n254\u2192 uncertainty where available.\n255\u21923. **Measured feasible Pareto front** \u2014 specific energy absorption vs.\n256\u2192 compaction efficiency among designs satisfying the force cap, with\n257\u2192 replicate spread, marked infeasible designs, and specimen photos or\n258\u2192 force\u2013time traces for representative Pareto designs.\n259\u2192\n260\u2192Supporting raw-data candidates: drop-test force\u2013time traces, Sobol /\n261\u2192first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262\u2192\n263\u2192### Suggested 15-minute allocation\n264\u2192\n265\u2192*(From the mock-audience report \u2014 results get the largest block.)*\n266\u2192\n267\u2192| Time | Content |\n268\u2192|---|---|\n269\u2192| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270\u2192| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271\u2192| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272\u2192| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273\u2192| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274\u2192| 1 min | Limitations and transfer conditions |\n275\u2192| 1 min | Measured conclusion + bookend |\n276\u2192\n277\u2192Move kernel choices, encodings, and other implementation detail to backup\n278\u2192slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279\u2192[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280\u2192\n281\u2192## Closing\n282\u2192\n283\u2192### Review\n284\u2192\n285\u2192*A recap of the body, leading into the conclusion*\n286\u2192\n287\u2192Using real, measured impact data from additively manufactured\n288\u2192tensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\n289\u2192optimization routine \u2014 we rapidly iterate toward better energy absorbers.\n290\u2192This feedback loop makes tensegrity optimization dramatically faster: dozens\n291\u2192of prints instead of the hundreds a traditional design of experiments over\n292\u2192this design space would require.\n293\u2192\n294\u2192### Conclusion\n295\u2192\n296\u2192*What the above means to the audience in the end*\n297\u2192\n298\u2192Because the loop runs on physical measurements, it is useful precisely where\n299\u2192model calibration is not worth its cost \u2014 and the same closed-loop pattern\n300\u2192should transfer (a conditional claim, pending evidence) to other additively\n301\u2192manufactured architectures whose performance is dominated by\n302\u2192hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303\u2192*use this loop when tests are authoritative, each test is costly, the design\n304\u2192space is mixed, and several performance goals conflict.* For tensegrity\n305\u2192specifically, it opens a path from this T3 proxy study toward lattices,\n306\u2192flight-relevant materials, payload protection on other planets, and safety\n307\u2192hardware here on Earth (e.g., the crutch tip, as future work).\n308\u2192\n309\u2192### Close\n310\u2192\n311\u2192*A way to end the presentation clearly and elegantly*\n312\u2192\n313\u2192Return to the opening image: the next planetary lander's energy absorber \u2014 a\n314\u2192structure that has to survive not one impact but many \u2014 doesn't have to take\n315\u2192years of hand-built prototypes; with this loop, candidate designs can be\n316\u2192designed, printed, and tested against real impacts in weeks instead of months\n317\u2192or longer. End on the measured engineering result and its transfer condition,\n318\u2192then the bookend: every method that compresses design time moves our ventures\n319\u2192\u2014 on this planet and beyond \u2014 forward.\n320\u2192\n321\u2192---\n322\u2192\n323\u2192## Changes from Draft 2\n324\u2192\n325\u2192Applied from @sgbaird's inline review comments on PR #84 and the\n326\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327\u2192\n328\u2192**From the inline review comments:**\n329\u2192\n330\u21921. **Reusability in the hook** (line 22): the tensegrity lander survives\n331\u2192 multiple drops, unlike crushable/sacrificial alternatives.\n332\u21922. **Proxy-system framing** (lines 49, 87): PLA\u2013TPU would never fly; it is a\n333\u2192 proxy for prototyping a self-driving-lab-style workflow \u2014 stated in a\n334\u2192 scope note up front and re-stated right after the hook.\n335\u21923. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336\u2192 heavy-duty simulation falls short of the experiments (millisecond-scale\n337\u2192 peak shock), not that it merely costs too much.\n338\u21924. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339\u2192 complex structures and especially lattices are not.\n340\u21925. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341\u2192 (temperature, print speed, \u2026) only; the rest is explicitly future work.\n342\u21926. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343\u2192 literature search (or validation of experiments against simulation), plus\n344\u2192 the millisecond-resolution peak-shock argument.\n345\u21927. **Optional Point 4 added** (line 97): future applications, with a note it\n346\u2192 can fold into the Conclusion if time is short.\n347\u21928. **Steel-materials point** (line 104): simulations fall short for the\n348\u2192 actual engineering materials (steel struts/cables), not just printed\n349\u2192 PLA\u2013TPU.\n350\u21929. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351\u2192 in minutes, pitched so the audience sees they can use it too.\n352\u219210. **\"Thousands\" \u2192 \"hundreds\"** (line 173): baseline is now a traditional\n353\u2192 design of experiments over a high-dimensional space (hundreds), in the\n354\u2192 main message, Need, Point 1, and Review.\n355\u219211. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356\u2192 or longer\" for candidate designs.\n357\u2192\n358\u2192**From the mock-audience report's priority revisions:**\n359\u2192\n360\u219212. **Quantitative three-slide results sequence** (Priority 1): campaign\n361\u2192 ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front, with a\n362\u2192 fallback main-message wording if the campaign can't yet support the\n363\u2192 efficiency claim.\n364\u219213. **Anti-simulation premise replaced with a scoped cost decision**\n365\u2192 (Priority 2): Point 1 retitled to the information-value message;\n366\u2192 \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367\u2192 anticipated with a backup slide.\n368\u219214. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369\u2192 implementation detail moved to backup slides; Q&A prep pointed at the\n370\u2192 report's claims-vs-evidence table.\n371\u219215. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372\u2192 the main talk, machinery in backup slides; the misleading\n373\u2192 \"qNEHVI over standard expected improvement\" comparison replaced with\n374\u2192 \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375\u219216. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376\u2192 sequence in Point 3, so the structure isn't remembered as a generic crush\n377\u2192 structure or lattice.\n378\u219217. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379\u2192 co-fabrication / no joining of parts,\" with a note to list remaining\n380\u2192 manual steps and an automation-status caveat.\n381\u219218. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382\u2192 compaction efficiency, constraint = force cap, deliverable = *feasible*\n383\u2192 Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384\u219219. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385\u2192 transfer claim marked conditional, and the close ends on the measured\n386\u2192 result before the bookend.\n387\u2192\n388\u2192## Changes from Draft 1\n389\u2192\n390\u2192Feedback from the PR #84 review, applied in Draft 2:\n391\u2192\n392\u21921. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n393\u2192 dozens-not-thousands claim); body and message now carry the same story.\n394\u21922. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n395\u2192 manuscript's positioning (#75/#76) is that the physical experiment is the\n396\u2192 source of truth, explicitly avoiding calibrated FE simulation.\n397\u21923. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n398\u2192 parametric design space and avoid an over-claim to a design-automation\n399\u2192 audience.\n400\u21924. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401\u2192 energy absorption and compaction efficiency, cap peak transmitted force.\n402\u21925. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403\u2192 *and* the space is too big to brute-force \u2014 which makes BO + AM the\n404\u2192 inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405\u2192 above.)*\n406\u21926. **Point 1 given its own content** (the *why* behind the inefficiency)\n407\u2192 instead of restating the Need.\n408\u21927. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409\u2192 the specifics (noise-aware acquisition, small batches, multi-objective).\n410\u21928. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411\u2192 (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412\u2192 above.)*\n413\u21929. **Evidence/results slot added** before the Closing, so the Conclusion is\n414\u2192 grounded in shown data rather than three hedged \"may\"s.\n415\u219210. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n416\u2192 Ball Bot visual; verified video links included.\n417\u219211. **Close bookends the opening** (the next lander's absorber, designed in\n418\u2192 weeks) before the broader humankind line.\n419\u219212. **Spelling**: Doumont (was \"Duomont\").", + "name": "read", + "tool_call_id": "call_93haurOMDGiH2mQFFbaMaYKn", + "info": { + "start_ts": 8147.331065886, + "end_ts": 8147.336467845 + } + }, + { + "role": "tool", + "content": " 1\u2192# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n 2\u2192\n 3\u2192Video: (assigned in #83)\n 4\u2192\n 5\u2192Transcribed from @me-madsen's handwritten notes\n 6\u2192([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7\u2192dated 2026-07-15).\n 8\u2192\n 9\u2192## Overview\n10\u2192\n11\u2192A presentation is all about the right content, displayed the right way, at the\n12\u2192right time.\n13\u2192\n14\u2192- Verbal communication is sequential.\n15\u2192- Message is *interpretation* of the information.\n16\u2192 - Ensure the audience can get the most out of what you're doing.\n17\u2192 - Don't just give them information \u2014 help them understand it, interpret it,\n18\u2192 and internalize it.\n19\u2192\n20\u2192## Doumont's suggested rules for a presentation\n21\u2192\n22\u21920. Ensure you have **messages** for your audience, not just words.\n23\u21921. **Adapt to your audience.**\n24\u2192 - Think like the audience, not the speaker.\n25\u21922. **Maximize signal-to-noise ratio.**\n26\u2192 - Reduce noise (animations, laser pointers, anything that pulls attention\n27\u2192 away from your message).\n28\u21923. **Use effective redundancy.**\n29\u2192 - Tell your message in word *and* on slide: someone deaf should understand\n30\u2192 your messages just as well as someone blind.\n31\u2192 - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32\u2192 date).\n33\u2192\n34\u2192## When creating slides\n35\u2192\n36\u2192- If the audience doesn't need it, why put it on the slides?\n37\u2192- People shouldn't have to count the items on a slide to know how many are\n38\u2192 there.\n39\u2192- Don't use your slides as your \"presentation notes.\"\n40\u2192 - Don't show your notes to the audience.\n41\u2192 - **A bad slide is worse than no slide at all.**\n42\u2192- **One message per slide** is a good rule of thumb.\n43\u2192 - Prioritize the *so what* (or *therefore what*) of your message.\n44\u2192- Make it easy to interpret the information on the slide:\n45\u2192 - 3\u20135 items at most.\n46\u2192 - Align the title left.\n47\u2192 - Do not clutter slides with repetitive or unnecessary information: logos,\n48\u2192 affiliation, date, slide numbers.\n49\u2192- Doumont's suggested workflow:\n50\u2192 1. **Start with the title.**\n51\u2192 - Use a full sentence in the title.\n52\u2192 - The title is the main message of the slide.\n53\u2192 - Make it easy to read: left-aligned, with intentional line breaks.\n54\u2192 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55\u2192- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56\u2192\n57\u2192## General preparation\n58\u2192\n59\u2192- Planning for audience, content, and structure matters more than materials\n60\u2192 (i.e., slides).\n61\u2192- Try using one sheet of paper for presentation preparation.\n62\u2192- It's most important that your presentation is:\n63\u2192 - well planned, structured, and delivered;\n64\u2192 - **if you don't have a lot of time to prepare, don't focus on making\n65\u2192 slides.**\n66\u2192- Practice the presentation a few times. Practice at least once *without* your\n67\u2192 slideshow!\n68\u2192- If doing a handout, keep it separate from the slides you're preparing.\n69\u2192- Impress the audience with your body.\n70\u2192 - Use gestures, not laser pointers.\n71\u2192 - \"Control\" the audience with your body \u2014 keep their attention on your\n72\u2192 presentation.\n73\u2192\n74\u2192## Impressions / what to apply to the IDETC presentation\n75\u2192\n76\u2192\n77\u2192\n78\u2192## Questions / things to discuss\n79\u2192\n80\u2192", + "name": "read", + "tool_call_id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "info": { + "start_ts": 8147.331133416, + "end_ts": 8147.338304015 + } + }, + { + "role": "tool", + "content": " 1\u2192\\documentclass[10pt]{article}\n 2\u2192\n 3\u2192% --- Packages ---\n 4\u2192\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5\u2192\\usepackage[hidelinks]{hyperref}\n 6\u2192\\usepackage{parskip}\n 7\u2192\\usepackage{graphicx}\n 8\u2192\\usepackage{caption}\n 9\u2192\\usepackage{titlesec}\n 10\u2192\\usepackage{wrapfig}\n 11\u2192\\usepackage[numbers,sort&compress]{natbib}\n 12\u2192\n 13\u2192% --- Formatting ---\n 14\u2192\\setlength{\\parskip}{0.2em}\n 15\u2192\\setlength{\\parindent}{0em}\n 16\u2192\\titleformat*{\\section}{\\large\\bfseries}\n 17\u2192\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18\u2192\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19\u2192\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20\u2192\\setlength{\\emergencystretch}{2em}\n 21\u2192\\setlength{\\intextsep}{0.2em}\n 22\u2192\\bibliographystyle{abbrvnat}\n 23\u2192\n 24\u2192\\begin{document}\n 25\u2192\n 26\u2192\\begin{center}\n 27\u2192{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28\u21923D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29\u2192\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30\u2192\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31\u2192\\end{center}\n 32\u2192\n 33\u2192\\vspace{0.4em}\n 34\u2192\n 35\u2192\\section*{Abstract}\n 36\u2192Tensegrity-inspired architectures---rigid struts suspended within a\n 37\u2192continuous flexible network---can exhibit tunable nonlinear\n 38\u2192force--displacement responses and favorable energy absorption per unit\n 39\u2192mass, motivating their use in lightweight energy-absorbing and protective\n 40\u2192structures\n 41\u2192\\citep{skelton2009tensegrity,pajunen2019design}.\n 42\u2192Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43\u2192struts and flexible (TPU) elements in a single build\n 44\u2192\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45\u2192space---strut geometry, tension-element cross-section, connectivity\n 46\u2192topology, and unit-cell tiling---is too large to explore by trial and\n 47\u2192error. We present a closed-loop experimental campaign that uses\n 48\u2192Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49\u2192to drive a design--print--test workflow operating directly on physical\n 50\u2192measurements, without relying on calibrated finite-element simulation\n 51\u2192for objective evaluation.\n 52\u2192\n 53\u2192\\section{Introduction and Motivation}\n 54\u2192\n 55\u2192\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56\u2192\\centering\n 57\u2192\\vspace{-0.4em}\n 58\u2192\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59\u2192\\captionsetup{font=small}\n 60\u2192\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61\u2192Candidate tensegrity-inspired unit cells are instantiated from a\n 62\u2192parameter vector, fabricated by multi-material FDM, and tested under\n 63\u2192quasi-static compression and drop-weight impact. Measured peak force,\n 64\u2192specific energy absorption (SEA), and compaction efficiency update a\n 65\u2192Gaussian-process surrogate that proposes the next batch of designs.}\n 66\u2192\\label{fig:overview}\n 67\u2192\\vspace{-0.4em}\n 68\u2192\\end{wrapfigure}\n 69\u2192\n 70\u2192Tensegrity structures combine compression-only members with a\n 71\u2192pre-tensioned tension network to achieve stiffness without rigid\n 72\u2192joinery~\\citep{skelton2009tensegrity}. Their\n 73\u2192nonlinear, often load-limiting force--displacement response makes them\n 74\u2192attractive for impact mitigation, packaging, and wearable protective\n 75\u2192gear~\\citep{pajunen2019design}. Idealized\n 76\u2192cable--strut tensegrities are difficult to manufacture at scale, but\n 77\u2192\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78\u21923D printing reproduce key tensegrity behaviors---including\n 79\u2192post-buckling stability and load-limiting force--displacement\n 80\u2192response~\\citep{pajunen2019design}---motivating our extension to\n 81\u2192multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82\u2192elements. PLA provides rigid struts; TPU provides flexible,\n 83\u2192rate-dependent tension elements whose viscoelasticity is expected to\n 84\u2192contribute hysteretic energy dissipation under impact loading,\n 85\u2192consistent with the energy-absorbing behavior reported for rigid--TPU\n 86\u2192multi-material prints~\\citep{khatri2024energy}.\n 87\u2192\n 88\u2192The challenge is that small geometric and topological changes can\n 89\u2192produce large changes in the measured response, and high-fidelity\n 90\u2192simulation of FDM specimens is computationally expensive and often\n 91\u2192struggles to capture physical defects such as interfacial slip and\n 92\u2192TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93\u2192trial-and-error sweeps over the joint design space are also infeasible:\n 94\u2192even modest parameter resolutions yield thousands of candidates, each\n 95\u2192requiring print and test time. We instead treat the physical\n 96\u2192experiment as the source of truth and use BO to select which specimens\n 97\u2192are most informative to fabricate next---an approach used in related\n 98\u2192closed-loop materials and structures\n 99\u2192campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100\u2192Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101\u2192set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102\u2192designs using parallel, noisy multi-objective BO driven by measured\n103\u2192$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104\u2192\n105\u2192\\section{Approach}\n106\u2192\\subsection{Closed-Loop Workflow}\n107\u2192Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108\u2192proceeds in four steps: (i)~a parameterized unit-cell design is\n109\u2192instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110\u2192is sliced and printed on a multi-material FDM system; (iii)~it is\n111\u2192tested under quasi-static compression and instrumented drop-weight\n112\u2192impact; and (iv)~the resulting performance metrics update a\n113\u2192Gaussian-process (GP) surrogate, which a batch acquisition function\n114\u2192queries to recommend the next set of candidates. Surrogate updating\n115\u2192and experiment selection are automated; specimen handling, slicing,\n116\u2192and test setup remain manual.\n117\u2192\n118\u2192\\subsection{Design Space}\n119\u2192We parameterize a family of unit cells using a core-wrapping\n120\u2192architecture in which rigid PLA struts are enclosed by continuous TPU\n121\u2192skins; the wrapping rationale follows multi-material FDM strategies in\n122\u2192which rigid panels are wrapped by stretchable soft material to prevent\n123\u2192delamination and improve interfacial bonding at the rigid--flexible\n124\u2192interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125\u2192comprises continuous geometric variables (strut diameter and length,\n126\u2192tension-element width and thickness), an integer strut count per unit\n127\u2192cell, and two categorical variables (connectivity topology, unit-cell\n128\u2192tiling pattern). Categorical choices are encoded explicitly rather\n129\u2192than embedded in a continuous metric, and bounds on the continuous\n130\u2192variables are chosen to remain within the printer's resolution and\n131\u2192overhang limits and to keep specimens in a common form factor for the\n132\u2192test fixtures.\n133\u2192\n134\u2192\\subsection{Surrogate and Acquisition}\n135\u2192The GP surrogate is trained on three measured performance metrics:\n136\u2192peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137\u2192(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138\u2192objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139\u2192likelihood with priors to avoid pathological length scales at small $n$.\n140\u2192Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141\u2192interfacial defects motivates our use of q-noisy expected hypervolume\n142\u2192improvement (qNEHVI), which explicitly accounts for observation noise\n143\u2192in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144\u2192expected-improvement variants. Print failures are treated as a\n145\u2192probabilistic feasibility constraint: the acquisition score for each\n146\u2192candidate is its qNEHVI improvement weighted by the GP-estimated\n147\u2192probability of successful fabrication, following recent constrained\n148\u2192multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149\u2192Where applicable, we use numerically stable log-space variants of\n150\u2192improvement-based acquisitions to mitigate vanishing-gradient\n151\u2192pathologies as data and constraints\n152\u2192accumulate~\\citep{ament2023logei}.\n153\u2192\n154\u2192\\section{Methods}\n155\u2192\\subsection{Fabrication}\n156\u2192Specimens are fabricated on a multi-material FDM system capable of\n157\u2192co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158\u2192Print parameters (temperature, line width, infill, retraction) are\n159\u2192held fixed within a batch to isolate the effect of the design\n160\u2192variables. Each specimen is weighed and dimensionally inspected prior\n161\u2192to testing so that mass and geometric deviations are available as\n162\u2192covariates.\n163\u2192\n164\u2192\\subsection{Mechanical Testing}\n165\u2192Quasi-static compression is performed on a screw-driven load frame at\n166\u2192a fixed nominal strain rate, with force--displacement recorded to\n167\u2192densification. Drop-weight impact testing uses an instrumented tup at\n168\u2192a fixed impact energy; transmitted force is captured at high sample\n169\u2192rate, and specimens are imaged before and after impact to document\n170\u2192failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171\u2192unit mass, and compaction efficiency $\\eta$, defined consistently\n172\u2192across the campaign. A control specimen from an earlier batch is\n173\u2192retested periodically to monitor TPU batch-to-batch and printer drift.\n174\u2192\n175\u2192\\section{Expected Outcomes and Discussion}\n176\u2192We will report how BO-selected designs evolve across iterations, how\n177\u2192GP surrogate predictions and uncertainties change as data accumulate,\n178\u2192and which Pareto-efficient trade-offs emerge between SEA and peak\n179\u2192transmitted force within the explored design space. We will also\n180\u2192discuss practical lessons for operating the closed-loop workflow,\n181\u2192including handling of print failures, batch-to-batch variability in\n182\u2192TPU response, the influence of the rigid--flexible interface on\n183\u2192energy absorption, and the exploration--exploitation balance of the\n184\u2192acquisition function under realistic experimental noise. We expect\n185\u2192the campaign to clarify where physical experimentation is genuinely\n186\u2192required versus where simpler analytical or multi-fidelity surrogate\n187\u2192shortcuts~\\citep{mo2023accelerated} could be\n188\u2192substituted in future work.\n189\u2192\n190\u2192\\section{Conclusion}\n191\u2192This work demonstrates a partially automated, experiment-driven\n192\u2192closed-loop pipeline for designing multi-material 3D-printed\n193\u2192tensegrity-inspired energy absorbers, with surrogate updating and\n194\u2192candidate selection automated and fabrication and testing kept manual.\n195\u2192By updating a GP surrogate directly from physical measurements, the\n196\u2192workflow avoids direct dependence on calibrated finite-element\n197\u2192simulation for objective evaluation while still providing principled,\n198\u2192multi-objective design recommendations. The same closed-loop pattern is\n199\u2192expected to transfer to other multi-material additively manufactured\n200\u2192architectures whose performance is dominated by hard-to-simulate\n201\u2192effects.\n202\u2192\n203\u2192{\\tiny\n204\u2192\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205\u2192\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206\u2192% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207\u2192% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208\u2192\\providecommand{\\doi}[1]{}\n209\u2192\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210\u2192\\bibliography{references}\n211\u2192}\n212\u2192\n213\u2192\\end{document}", + "name": "read", + "tool_call_id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "info": { + "start_ts": 8147.332633466, + "end_ts": 8147.339043095 + } + } + ], + "next_observation": [ + { + "role": "tool", + "content": " 1\u2192# IDETC Tensegrity Slides \u2014 Draft 1 (extracted content)\n 2\u2192\n 3\u2192Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4\u2192\n 5\u2192\n 6\u2192## Slide 1 \u2014 layout: Title Only\n 7\u2192\n 8\u2192**On-slide text:**\n 9\u2192- **Title 1**: Let\u2019s build better tensegrity structures faster\n 10\u2192- **TextBox 4**: Marcus Madsen\n 11\u2192Undergraduate researcher under Sterling Baird, Ph.D.\n 12\u2192- **TextBox 6**: IDETC 2026\n 13\u2192\n 14\u2192**Non-text shapes / media / placeholders:**\n 15\u2192- [PICTURE (13)] Picture 3\n 16\u2192- [PICTURE (13)] Picture 8\n 17\u2192\n 18\u2192**Presenter notes:**\n 19\u2192> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20\u2192> \n 21\u2192> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22\u2192\n 23\u2192\n 24\u2192## Slide 2 \u2014 layout: Title Only\n 25\u2192\n 26\u2192**On-slide text:**\n 27\u2192- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings\u2014and to problems closer to home.\n 28\u2192\n 29\u2192**Non-text shapes / media / placeholders:**\n 30\u2192- [MEDIA (16)] Online Media 5\n 31\u2192\n 32\u2192**Presenter notes:**\n 33\u2192> Replace this video with a downloaded and trimmed version of just the fall. \n 34\u2192> \n 35\u2192> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle \u2014 thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander\u00a0is\u00a0the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit\u00a0and get back up\u00a0\u2014 it survives multiple drops and keeps working.\n 36\u2192\n 37\u2192\n 38\u2192## Slide 3 (HIDDEN) \u2014 layout: Title Only\n 39\u2192\n 40\u2192**On-slide text:**\n 41\u2192- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42\u2192- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43\u2192- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44\u2192\n 45\u2192**Presenter notes:**\n 46\u2192> This might be better explained purely vocally while on the previous slide.\n 47\u2192\n 48\u2192\n 49\u2192## Slide 4 \u2014 layout: Title Only\n 50\u2192\n 51\u2192**On-slide text:**\n 52\u2192- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53\u2192- **TextBox 3**: Iteration\n 54\u2192- **TextBox 5**: Building\n 55\u2192- **TextBox 7**: Testing\n 56\u2192\n 57\u2192**Non-text shapes / media / placeholders:**\n 58\u2192- [MEDIA (16)] AdobeStock_207717222\n 59\u2192- [MEDIA (16)] Online Media 2\n 60\u2192- [PICTURE (13)] Picture 14\n 61\u2192- [PICTURE (13)] Picture 16\n 62\u2192- [PICTURE (13)] Picture 18\n 63\u2192\n 64\u2192**Presenter notes:**\n 65\u2192> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn\u2019t become distracting because it\u2019s fun to watch it spin.\n 66\u2192> \n 67\u2192> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68\u2192\n 69\u2192\n 70\u2192## Slide 5 (HIDDEN) \u2014 layout: Title Only\n 71\u2192\n 72\u2192**On-slide text:**\n 73\u2192- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74\u2192- **TextBox 14**: Baird et al. Matter (2022)\n 75\u2192\n 76\u2192**Non-text shapes / media / placeholders:**\n 77\u2192- [GROUP (6)] Group 2\n 78\u2192\n 79\u2192**Presenter notes:**\n 80\u2192> Consult Sterling on this slide. I don\u2019t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it\u2019s well enough known that it\u2019d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81\u2192\n 82\u2192\n 83\u2192## Slide 6 (HIDDEN) \u2014 layout: Title Only\n 84\u2192\n 85\u2192**On-slide text:**\n 86\u2192- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87\u2192- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88\u2192\n 89\u2192**Presenter notes:**\n 90\u2192> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91\u2192\n 92\u2192\n 93\u2192## Slide 7 \u2014 layout: Title Only\n 94\u2192\n 95\u2192**On-slide text:**\n 96\u2192- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97\u2192- **TextBox 5**: 6 variables\n 98\u2192- **TextBox 7**: 2 types of input data\n 99\u2192- **TextBox 9**: 2 objectives\n100\u2192- **TextBox 17**: *image of blackbox*\n101\u2192\n102\u2192**Non-text shapes / media / placeholders:**\n103\u2192- [LINE (9)] Connector: Curved 11\n104\u2192- [LINE (9)] Connector: Curved 13\n105\u2192- [LINE (9)] Connector: Curved 15\n106\u2192\n107\u2192**Presenter notes:**\n108\u2192> Very rough slide as I\u2019m not entirely sure what graphic or text to put on this, partially because I\u2019m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109\u2192\n110\u2192\n111\u2192## Slide 8 \u2014 layout: Title Only\n112\u2192\n113\u2192**On-slide text:**\n114\u2192- **Title 1**: Multi-material additive manufacturing supplies that data fast \u2014 single-build co-fabrication, with no joining of parts.\n115\u2192\n116\u2192**Non-text shapes / media / placeholders:**\n117\u2192- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118\u2192- [PICTURE (13)] Picture 4\n119\u2192\n120\u2192**Presenter notes:**\n121\u2192> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122\u2192\n123\u2192\n124\u2192## Slide 9 \u2014 layout: Title Only\n125\u2192\n126\u2192**On-slide text:**\n127\u2192- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128\u2192- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129\u2192- **TextBox 5**: *placeholder for slo motion of drop test*\n130\u2192\n131\u2192\n132\u2192## Slide 10 \u2014 layout: Title Only\n133\u2192\n134\u2192**On-slide text:**\n135\u2192- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136\u2192- **TextBox 3**: Printing\n137\u2192- **TextBox 5**: Testing\n138\u2192- **TextBox 7**: BO feed\n139\u2192\n140\u2192**Non-text shapes / media / placeholders:**\n141\u2192- [PICTURE (13)] Picture 14\n142\u2192- [PICTURE (13)] Picture 16\n143\u2192- [PICTURE (13)] Picture 18\n144\u2192- [PICTURE (13)] Graphic 8\n145\u2192\n146\u2192**Presenter notes:**\n147\u2192> I think it might be better if we replaced \u201cprinting\u201d with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148\u2192\n149\u2192\n150\u2192## Slide 11 (HIDDEN) \u2014 layout: Title Only\n151\u2192\n152\u2192**On-slide text:**\n153\u2192- **Title 1**: Reserved for results\n154\u2192\n155\u2192**Presenter notes:**\n156\u2192> Would be helpful if we could show that this process works in this case.\n157\u2192\n158\u2192\n159\u2192## Slide 12 \u2014 layout: Title Only\n160\u2192\n161\u2192**On-slide text:**\n162\u2192- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163\u2192- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164\u2192- **TextBox 3**: *image to show lattice structure\n165\u2192\n166\u2192\n167\u2192## Slide 13 \u2014 layout: Title Only\n168\u2192\n169\u2192**Non-text shapes / media / placeholders:**\n170\u2192- [PICTURE (13)] Picture 3\n171\u2192\n172\u2192**Presenter notes:**\n173\u2192> Slide for questions at the end\n174\u2192\n175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "info": { + "start_ts": 8147.330900226, + "end_ts": 8147.337548875 + } + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Presentation \u2014 Doumont Presentation Structure Template\n 2\u2192\n 3\u2192Transcribed from Jean-luc Doumont's presentation-structure template\n 4\u2192([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5\u2192adapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\n 6\u2192All rights reserved. Can be downloaded from .\n 7\u2192\n 8\u2192> To design your presentation, write down your ideas for each component below.\n 9\u2192> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10\u2192\n 11\u2192**Draft 3** \u2014 applies the inline review comments from @sgbaird on PR #84 and the\n 12\u2192priority revisions from the\n 13\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14\u2192Summaries of what changed are at the bottom of this file\n 15\u2192([Draft 2 \u2192 3](#changes-from-draft-2), [Draft 1 \u2192 2](#changes-from-draft-1)).\n 16\u2192\n 17\u2192**Scope note (frame everything below with this):** the study presented is the\n 18\u2192optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19\u2192processing parameters** (e.g., nozzle temperature, print speed). PLA\u2013TPU is a\n 20\u2192**proxy system** \u2014 not flight material \u2014 used to prototype a self-driving-lab\n 21\u2192style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22\u2192The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23\u2192beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24\u2192\n 25\u2192## Opening\n 26\u2192\n 27\u2192### Attention getter\n 28\u2192\n 29\u2192*A way to lead the audience to the need efficiently*\n 30\u2192\n 31\u2192Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32\u2192landings \u2014 and to problems closer to home.\n 33\u2192\n 34\u2192One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35\u2192clip) do the work:\n 36\u2192\n 37\u2192> Landing a payload on another planet means surviving an impact in conditions\n 38\u2192> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n 39\u2192> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40\u2192> structure: the lander *is* the energy absorber. And unlike crushable\n 41\u2192> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42\u2192> hit **and get back up** \u2014 it survives multiple drops and keeps working.\n 43\u2192\n 44\u2192Candidate Super Ball Bot videos to show (verified links):\n 45\u2192\n 46\u2192- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n 47\u2192 (official channel); the canonical overview.\n 48\u2192- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49\u2192 \u2014 Wall Street Journal; prototype drop footage.\n 50\u2192- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51\u2192 \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n 52\u2192\n 53\u2192Immediately after the hook, state the present study scope in one sentence\n 54\u2192(per the scope note above): *we study a printed PLA\u2013TPU T3 prism as a proxy\n 55\u2192system for prototyping an autonomous tensegrity-optimization workflow* \u2014 so\n 56\u2192the planetary framing never outruns the demonstrated scope.\n 57\u2192\n 58\u2192\n 60\u2192\n 61\u2192### Need\n 62\u2192\n 63\u2192*A difference between actual and desired situations*\n 64\u2192\n 65\u2192**Actual situation** (a two-sided pincer \u2014 stated as a scoped engineering\n 66\u2192judgment, not an absolute):\n 67\u2192\n 68\u2192- For these structures, obtaining trustworthy objective values from a\n 69\u2192 validated high-fidelity model would require substantial calibration of\n 70\u2192 interface, rate, contact, and defect behavior \u2014 and even heavy-duty\n 71\u2192 simulation falls short of the experiments we are running: peak transmitted\n 72\u2192 shock is a millisecond-scale transient that is notoriously difficult to\n 73\u2192 resolve numerically. This holds even for idealized cable\u2013strut tensegrities\n 74\u2192 in their actual engineering materials (steel or similar), not just for\n 75\u2192 printed PLA\u2013TPU.\n 76\u2192 \n 79\u2192- Exhaustive physical search fails on cost: a traditional design of\n 80\u2192 experiments over this relatively high-dimensional space (geometry + FDM\n 81\u2192 process parameters) would demand hundreds of specimens, each costing print\n 82\u2192 and test time.\n 83\u2192- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84\u2192 more complex structures \u2014 especially tensegrity *lattices* \u2014 become\n 85\u2192 extremely difficult and ultimately infeasible to hand-build.\n 86\u2192\n 87\u2192**Desired situation:**\n 88\u2192\n 89\u2192- Trustworthy performance data without hand assembly (easy creation of\n 90\u2192 tensegrity structures, scalable beyond a single prism).\n 91\u2192- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n 92\u2192 not the hundreds a traditional design of experiments would require.\n 93\u2192\n 94\u2192### Task\n 95\u2192\n 96\u2192*What I decided/was asked to do to address the need*\n 97\u2192\n 98\u2192Build a closed-loop design\u2013print\u2013test workflow that optimizes a **T3-prism\n 99\u2192tensegrity-inspired energy absorber together with its FDM processing\n100\u2192parameters** (e.g., temperature, print speed) directly from real, measured\n101\u2192impact data \u2014 maximizing energy absorbed per gram (specific energy\n102\u2192absorption) and compaction efficiency, subject to a cap on the peak force\n103\u2192transmitted to the payload. Extensions beyond the T3 prism are future work.\n104\u2192\n105\u2192### Main message\n106\u2192\n107\u2192*The one sentence I want my audience to remember*\n108\u2192\n109\u2192By closing the loop between multi-material 3D printing and Bayesian\n110\u2192optimization, we can optimize tensegrity-inspired energy absorbers directly\n111\u2192from real impact data \u2014 in dozens of prints, not the hundreds a traditional\n112\u2192design of experiments would demand.\n113\u2192\n114\u2192\n118\u2192\n119\u2192### Preview\n120\u2192\n121\u2192*A map of the body (ideally three points, max. five)*\n122\u2192\n123\u21921. When model calibration and exhaustive testing are both costly, each\n124\u2192 physical specimen must be chosen for its information value \u2014 so the\n125\u2192 measured experiment is our source of truth.\n126\u21922. Bayesian optimization makes physical experimentation affordable: it handles\n127\u2192 noisy measurements, small batches, and the constrained multi-objective\n128\u2192 trade-off \u2014 and Honegumi let us scaffold it in minutes.\n129\u21923. Multi-material additive manufacturing closes the loop: single-build PLA\u2013TPU\n130\u2192 co-fabrication turns each proposed design into test data in hours, with no\n131\u2192 joining of parts.\n132\u21924. *(Optional fourth point, if time allows)* The same closed loop points at\n133\u2192 what comes next: tensegrity lattices, flight-relevant materials, and\n134\u2192 safety hardware on Earth.\n135\u2192\n136\u2192## Body\n137\u2192\n138\u2192### Point 1\n139\u2192\n140\u2192When model calibration and exhaustive testing are both costly, each physical\n141\u2192specimen must be chosen for its information value.\n142\u2192\n143\u2192- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144\u2192 these structures, a sufficiently calibrated multiphysics model is expensive\n145\u2192 to build relative to the design campaign itself \u2014 and the hardest quantity,\n146\u2192 peak transmitted shock, is a millisecond-scale transient that even\n147\u2192 heavy-duty simulation struggles to resolve. The gap persists in the actual\n148\u2192 engineering materials a real lander would use (steel struts and cables),\n149\u2192 so this is not merely an artifact of printed PLA\u2013TPU.\n150\u2192- The alternative \u2014 sweeping the design space physically \u2014 fails on cost: a\n151\u2192 traditional design of experiments over geometry plus FDM process parameters\n152\u2192 would take hundreds of specimens, each needing print + test time.\n153\u2192- Therefore: treat the physical experiment as the authoritative source of\n154\u2192 objective values, and be ruthless about *which* experiments to run.\n155\u2192- Anticipate the FEA-veteran pushback (\"inconvenient \u2260 untrustworthy\"): frame\n156\u2192 it as a cost-and-dependence decision, and keep a backup slide with the\n157\u2192 literature (or our own validation) on simulation\u2013experiment discrepancy for\n158\u2192 impact transients.\n159\u2192\n160\u2192### Transition\n161\u2192\n162\u2192If every data point must be a real experiment, we need a method that extracts\n163\u2192the most from every specimen \u2014 we need to make the process radically more\n164\u2192sample-efficient.\n165\u2192\n166\u2192### Point 2\n167\u2192\n168\u2192Bayesian optimization makes physical experimentation affordable, even with\n169\u2192limited, noisy data.\n170\u2192\n171\u2192- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172\u2192 visual in the main talk; implementation detail goes to backup slides.\n173\u2192 Spoken version:\n174\u2192 > \"After each batch, the model estimates both expected performance and\n175\u2192 > uncertainty across the design space. The acquisition rule selects a small\n176\u2192 > next batch that is most likely to expand the feasible trade-off frontier,\n177\u2192 > while accounting for noisy tests and print failures.\"\n178\u2192- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179\u2192 motivate a noise-aware acquisition function (qNEHVI) rather than\n180\u2192 non-noise-aware alternatives. (Backup slide: how observation noise is\n181\u2192 actually modeled \u2014 replicates, noise identification \u2014 and budget-matched\n182\u2192 baselines such as Sobol/random sampling.)\n183\u2192- Small parallel batches: the surrogate proposes the next batch of specimens\n184\u2192 to print, balancing exploration and exploitation.\n185\u2192- Constrained multi-objective by construction: **objectives** = maximize\n186\u2192 specific energy absorption and compaction efficiency; **constraint** = peak\n187\u2192 transmitted force must stay under a cap; the deliverable is the *feasible*\n188\u2192 Pareto front between the two objectives among force-cap-satisfying designs.\n189\u2192 (Keep this formulation consistent everywhere \u2014 do not present \"SEA vs. peak\n190\u2192 force\" as the trade-off.)\n191\u2192- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192\u2192 this advanced task (noisy, batched, constrained, multi-objective) in\n193\u2192 minutes using [Honegumi](https://honegumi.readthedocs.io/) \u2014 the audience\n194\u2192 can do the same for their own problems; show the code-template picker\n195\u2192 briefly so they see how low the barrier is.\n196\u2192\n197\u2192### Transition\n198\u2192\n199\u2192The optimizer is no longer the bottleneck \u2014 the limit is now providing real\n200\u2192data quickly enough.\n201\u2192\n202\u2192### Point 3\n203\u2192\n204\u2192Multi-material additive manufacturing supplies that data fast \u2014 single-build\n205\u2192co-fabrication, with no joining of parts.\n206\u2192\n207\u2192- Single-build PLA\u2013TPU co-fabrication: rigid struts and flexible tension\n208\u2192 network in one print \u2014 no joining or hand assembly of members, which closes\n209\u2192 the \"easy creation\" gap from the Need and is what makes future lattices\n210\u2192 feasible at all. (Be precise: if support removal, trimming, or conditioning\n211\u2192 remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212\u2192 assembly.\")\n213\u2192- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214\u2192 as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215\u2192 pre-tension status) plus a short deformation sequence \u2014 so the EDL engineer\n216\u2192 doesn't file it as a \"crush structure\" and the grad student can tell it from\n217\u2192 an ordinary lattice.\n218\u2192- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219\u2192 tested specimen (quasi-static compression + instrumented drop-weight impact)\n220\u2192 in hours. (Backup slide: representative print-to-result time breakdown and\n221\u2192 which steps remain manual \u2014 candidate selection is automated; slicing,\n222\u2192 handling, and testing are not yet.)\n223\u2192- Now we have the data we need to complete the loop.\n224\u2192\n225\u2192### Point 4 (optional \u2014 future applications)\n226\u2192\n227\u2192*Include if timing allows; otherwise fold into the Conclusion.*\n228\u2192\n229\u2192The same closed loop transfers wherever tests are authoritative, each test is\n230\u2192costly, the design space is mixed, and several performance goals conflict:\n231\u2192\n232\u2192- Tensegrity *lattices* and more complex architectures \u2014 exactly where hand\n233\u2192 assembly becomes infeasible and single-build co-fabrication pays off.\n234\u2192- Migration from the PLA\u2013TPU proxy toward flight-relevant materials and\n235\u2192 impact regimes.\n236\u2192- Safety hardware on Earth (e.g., the tensegrity crutch tip \u2014 future work).\n237\u2192- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238\u2192 handling, testing) in the loop.\n239\u2192\n240\u2192### Evidence / results\n241\u2192\n242\u2192*What we found \u2014 ground the conclusion in shown data*\n243\u2192\n244\u2192Build the talk around a quantitative three-slide sequence (per the\n245\u2192mock-audience report; insert observed values once the campaign completes):\n246\u2192\n247\u21921. **Campaign ledger** \u2014 \"The campaign used N specimens across B adaptive\n248\u2192 batches under a fixed physical-test budget\": initialization vs. adaptive\n249\u2192 batches, replicates, print failures, cycle time, and the numerical value\n250\u2192 (and rationale) of the force cap.\n251\u21922. **Budget-matched baseline** \u2014 \"Adaptive selection improved the feasible\n252\u2192 design set faster than [predeclared baseline]\": performance vs. number of\n253\u2192 physical tests against Sobol/random sampling at the same budget, with\n254\u2192 uncertainty where available.\n255\u21923. **Measured feasible Pareto front** \u2014 specific energy absorption vs.\n256\u2192 compaction efficiency among designs satisfying the force cap, with\n257\u2192 replicate spread, marked infeasible designs, and specimen photos or\n258\u2192 force\u2013time traces for representative Pareto designs.\n259\u2192\n260\u2192Supporting raw-data candidates: drop-test force\u2013time traces, Sobol /\n261\u2192first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262\u2192\n263\u2192### Suggested 15-minute allocation\n264\u2192\n265\u2192*(From the mock-audience report \u2014 results get the largest block.)*\n266\u2192\n267\u2192| Time | Content |\n268\u2192|---|---|\n269\u2192| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270\u2192| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271\u2192| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272\u2192| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273\u2192| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274\u2192| 1 min | Limitations and transfer conditions |\n275\u2192| 1 min | Measured conclusion + bookend |\n276\u2192\n277\u2192Move kernel choices, encodings, and other implementation detail to backup\n278\u2192slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279\u2192[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280\u2192\n281\u2192## Closing\n282\u2192\n283\u2192### Review\n284\u2192\n285\u2192*A recap of the body, leading into the conclusion*\n286\u2192\n287\u2192Using real, measured impact data from additively manufactured\n288\u2192tensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\n289\u2192optimization routine \u2014 we rapidly iterate toward better energy absorbers.\n290\u2192This feedback loop makes tensegrity optimization dramatically faster: dozens\n291\u2192of prints instead of the hundreds a traditional design of experiments over\n292\u2192this design space would require.\n293\u2192\n294\u2192### Conclusion\n295\u2192\n296\u2192*What the above means to the audience in the end*\n297\u2192\n298\u2192Because the loop runs on physical measurements, it is useful precisely where\n299\u2192model calibration is not worth its cost \u2014 and the same closed-loop pattern\n300\u2192should transfer (a conditional claim, pending evidence) to other additively\n301\u2192manufactured architectures whose performance is dominated by\n302\u2192hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303\u2192*use this loop when tests are authoritative, each test is costly, the design\n304\u2192space is mixed, and several performance goals conflict.* For tensegrity\n305\u2192specifically, it opens a path from this T3 proxy study toward lattices,\n306\u2192flight-relevant materials, payload protection on other planets, and safety\n307\u2192hardware here on Earth (e.g., the crutch tip, as future work).\n308\u2192\n309\u2192### Close\n310\u2192\n311\u2192*A way to end the presentation clearly and elegantly*\n312\u2192\n313\u2192Return to the opening image: the next planetary lander's energy absorber \u2014 a\n314\u2192structure that has to survive not one impact but many \u2014 doesn't have to take\n315\u2192years of hand-built prototypes; with this loop, candidate designs can be\n316\u2192designed, printed, and tested against real impacts in weeks instead of months\n317\u2192or longer. End on the measured engineering result and its transfer condition,\n318\u2192then the bookend: every method that compresses design time moves our ventures\n319\u2192\u2014 on this planet and beyond \u2014 forward.\n320\u2192\n321\u2192---\n322\u2192\n323\u2192## Changes from Draft 2\n324\u2192\n325\u2192Applied from @sgbaird's inline review comments on PR #84 and the\n326\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327\u2192\n328\u2192**From the inline review comments:**\n329\u2192\n330\u21921. **Reusability in the hook** (line 22): the tensegrity lander survives\n331\u2192 multiple drops, unlike crushable/sacrificial alternatives.\n332\u21922. **Proxy-system framing** (lines 49, 87): PLA\u2013TPU would never fly; it is a\n333\u2192 proxy for prototyping a self-driving-lab-style workflow \u2014 stated in a\n334\u2192 scope note up front and re-stated right after the hook.\n335\u21923. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336\u2192 heavy-duty simulation falls short of the experiments (millisecond-scale\n337\u2192 peak shock), not that it merely costs too much.\n338\u21924. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339\u2192 complex structures and especially lattices are not.\n340\u21925. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341\u2192 (temperature, print speed, \u2026) only; the rest is explicitly future work.\n342\u21926. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343\u2192 literature search (or validation of experiments against simulation), plus\n344\u2192 the millisecond-resolution peak-shock argument.\n345\u21927. **Optional Point 4 added** (line 97): future applications, with a note it\n346\u2192 can fold into the Conclusion if time is short.\n347\u21928. **Steel-materials point** (line 104): simulations fall short for the\n348\u2192 actual engineering materials (steel struts/cables), not just printed\n349\u2192 PLA\u2013TPU.\n350\u21929. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351\u2192 in minutes, pitched so the audience sees they can use it too.\n352\u219210. **\"Thousands\" \u2192 \"hundreds\"** (line 173): baseline is now a traditional\n353\u2192 design of experiments over a high-dimensional space (hundreds), in the\n354\u2192 main message, Need, Point 1, and Review.\n355\u219211. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356\u2192 or longer\" for candidate designs.\n357\u2192\n358\u2192**From the mock-audience report's priority revisions:**\n359\u2192\n360\u219212. **Quantitative three-slide results sequence** (Priority 1): campaign\n361\u2192 ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front, with a\n362\u2192 fallback main-message wording if the campaign can't yet support the\n363\u2192 efficiency claim.\n364\u219213. **Anti-simulation premise replaced with a scoped cost decision**\n365\u2192 (Priority 2): Point 1 retitled to the information-value message;\n366\u2192 \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367\u2192 anticipated with a backup slide.\n368\u219214. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369\u2192 implementation detail moved to backup slides; Q&A prep pointed at the\n370\u2192 report's claims-vs-evidence table.\n371\u219215. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372\u2192 the main talk, machinery in backup slides; the misleading\n373\u2192 \"qNEHVI over standard expected improvement\" comparison replaced with\n374\u2192 \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375\u219216. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376\u2192 sequence in Point 3, so the structure isn't remembered as a generic crush\n377\u2192 structure or lattice.\n378\u219217. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379\u2192 co-fabrication / no joining of parts,\" with a note to list remaining\n380\u2192 manual steps and an automation-status caveat.\n381\u219218. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382\u2192 compaction efficiency, constraint = force cap, deliverable = *feasible*\n383\u2192 Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384\u219219. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385\u2192 transfer claim marked conditional, and the close ends on the measured\n386\u2192 result before the bookend.\n387\u2192\n388\u2192## Changes from Draft 1\n389\u2192\n390\u2192Feedback from the PR #84 review, applied in Draft 2:\n391\u2192\n392\u21921. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n393\u2192 dozens-not-thousands claim); body and message now carry the same story.\n394\u21922. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n395\u2192 manuscript's positioning (#75/#76) is that the physical experiment is the\n396\u2192 source of truth, explicitly avoiding calibrated FE simulation.\n397\u21923. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n398\u2192 parametric design space and avoid an over-claim to a design-automation\n399\u2192 audience.\n400\u21924. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401\u2192 energy absorption and compaction efficiency, cap peak transmitted force.\n402\u21925. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403\u2192 *and* the space is too big to brute-force \u2014 which makes BO + AM the\n404\u2192 inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405\u2192 above.)*\n406\u21926. **Point 1 given its own content** (the *why* behind the inefficiency)\n407\u2192 instead of restating the Need.\n408\u21927. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409\u2192 the specifics (noise-aware acquisition, small batches, multi-objective).\n410\u21928. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411\u2192 (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412\u2192 above.)*\n413\u21929. **Evidence/results slot added** before the Closing, so the Conclusion is\n414\u2192 grounded in shown data rather than three hedged \"may\"s.\n415\u219210. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n416\u2192 Ball Bot visual; verified video links included.\n417\u219211. **Close bookends the opening** (the next lander's absorber, designed in\n418\u2192 weeks) before the broader humankind line.\n419\u219212. **Spelling**: Doumont (was \"Duomont\").", + "name": "read", + "tool_call_id": "call_93haurOMDGiH2mQFFbaMaYKn", + "info": { + "start_ts": 8147.331065886, + "end_ts": 8147.336467845 + } + }, + { + "role": "tool", + "content": " 1\u2192# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n 2\u2192\n 3\u2192Video: (assigned in #83)\n 4\u2192\n 5\u2192Transcribed from @me-madsen's handwritten notes\n 6\u2192([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7\u2192dated 2026-07-15).\n 8\u2192\n 9\u2192## Overview\n10\u2192\n11\u2192A presentation is all about the right content, displayed the right way, at the\n12\u2192right time.\n13\u2192\n14\u2192- Verbal communication is sequential.\n15\u2192- Message is *interpretation* of the information.\n16\u2192 - Ensure the audience can get the most out of what you're doing.\n17\u2192 - Don't just give them information \u2014 help them understand it, interpret it,\n18\u2192 and internalize it.\n19\u2192\n20\u2192## Doumont's suggested rules for a presentation\n21\u2192\n22\u21920. Ensure you have **messages** for your audience, not just words.\n23\u21921. **Adapt to your audience.**\n24\u2192 - Think like the audience, not the speaker.\n25\u21922. **Maximize signal-to-noise ratio.**\n26\u2192 - Reduce noise (animations, laser pointers, anything that pulls attention\n27\u2192 away from your message).\n28\u21923. **Use effective redundancy.**\n29\u2192 - Tell your message in word *and* on slide: someone deaf should understand\n30\u2192 your messages just as well as someone blind.\n31\u2192 - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32\u2192 date).\n33\u2192\n34\u2192## When creating slides\n35\u2192\n36\u2192- If the audience doesn't need it, why put it on the slides?\n37\u2192- People shouldn't have to count the items on a slide to know how many are\n38\u2192 there.\n39\u2192- Don't use your slides as your \"presentation notes.\"\n40\u2192 - Don't show your notes to the audience.\n41\u2192 - **A bad slide is worse than no slide at all.**\n42\u2192- **One message per slide** is a good rule of thumb.\n43\u2192 - Prioritize the *so what* (or *therefore what*) of your message.\n44\u2192- Make it easy to interpret the information on the slide:\n45\u2192 - 3\u20135 items at most.\n46\u2192 - Align the title left.\n47\u2192 - Do not clutter slides with repetitive or unnecessary information: logos,\n48\u2192 affiliation, date, slide numbers.\n49\u2192- Doumont's suggested workflow:\n50\u2192 1. **Start with the title.**\n51\u2192 - Use a full sentence in the title.\n52\u2192 - The title is the main message of the slide.\n53\u2192 - Make it easy to read: left-aligned, with intentional line breaks.\n54\u2192 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55\u2192- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56\u2192\n57\u2192## General preparation\n58\u2192\n59\u2192- Planning for audience, content, and structure matters more than materials\n60\u2192 (i.e., slides).\n61\u2192- Try using one sheet of paper for presentation preparation.\n62\u2192- It's most important that your presentation is:\n63\u2192 - well planned, structured, and delivered;\n64\u2192 - **if you don't have a lot of time to prepare, don't focus on making\n65\u2192 slides.**\n66\u2192- Practice the presentation a few times. Practice at least once *without* your\n67\u2192 slideshow!\n68\u2192- If doing a handout, keep it separate from the slides you're preparing.\n69\u2192- Impress the audience with your body.\n70\u2192 - Use gestures, not laser pointers.\n71\u2192 - \"Control\" the audience with your body \u2014 keep their attention on your\n72\u2192 presentation.\n73\u2192\n74\u2192## Impressions / what to apply to the IDETC presentation\n75\u2192\n76\u2192\n77\u2192\n78\u2192## Questions / things to discuss\n79\u2192\n80\u2192", + "name": "read", + "tool_call_id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "info": { + "start_ts": 8147.331133416, + "end_ts": 8147.338304015 + } + }, + { + "role": "tool", + "content": " 1\u2192\\documentclass[10pt]{article}\n 2\u2192\n 3\u2192% --- Packages ---\n 4\u2192\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5\u2192\\usepackage[hidelinks]{hyperref}\n 6\u2192\\usepackage{parskip}\n 7\u2192\\usepackage{graphicx}\n 8\u2192\\usepackage{caption}\n 9\u2192\\usepackage{titlesec}\n 10\u2192\\usepackage{wrapfig}\n 11\u2192\\usepackage[numbers,sort&compress]{natbib}\n 12\u2192\n 13\u2192% --- Formatting ---\n 14\u2192\\setlength{\\parskip}{0.2em}\n 15\u2192\\setlength{\\parindent}{0em}\n 16\u2192\\titleformat*{\\section}{\\large\\bfseries}\n 17\u2192\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18\u2192\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19\u2192\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20\u2192\\setlength{\\emergencystretch}{2em}\n 21\u2192\\setlength{\\intextsep}{0.2em}\n 22\u2192\\bibliographystyle{abbrvnat}\n 23\u2192\n 24\u2192\\begin{document}\n 25\u2192\n 26\u2192\\begin{center}\n 27\u2192{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28\u21923D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29\u2192\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30\u2192\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31\u2192\\end{center}\n 32\u2192\n 33\u2192\\vspace{0.4em}\n 34\u2192\n 35\u2192\\section*{Abstract}\n 36\u2192Tensegrity-inspired architectures---rigid struts suspended within a\n 37\u2192continuous flexible network---can exhibit tunable nonlinear\n 38\u2192force--displacement responses and favorable energy absorption per unit\n 39\u2192mass, motivating their use in lightweight energy-absorbing and protective\n 40\u2192structures\n 41\u2192\\citep{skelton2009tensegrity,pajunen2019design}.\n 42\u2192Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43\u2192struts and flexible (TPU) elements in a single build\n 44\u2192\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45\u2192space---strut geometry, tension-element cross-section, connectivity\n 46\u2192topology, and unit-cell tiling---is too large to explore by trial and\n 47\u2192error. We present a closed-loop experimental campaign that uses\n 48\u2192Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49\u2192to drive a design--print--test workflow operating directly on physical\n 50\u2192measurements, without relying on calibrated finite-element simulation\n 51\u2192for objective evaluation.\n 52\u2192\n 53\u2192\\section{Introduction and Motivation}\n 54\u2192\n 55\u2192\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56\u2192\\centering\n 57\u2192\\vspace{-0.4em}\n 58\u2192\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59\u2192\\captionsetup{font=small}\n 60\u2192\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61\u2192Candidate tensegrity-inspired unit cells are instantiated from a\n 62\u2192parameter vector, fabricated by multi-material FDM, and tested under\n 63\u2192quasi-static compression and drop-weight impact. Measured peak force,\n 64\u2192specific energy absorption (SEA), and compaction efficiency update a\n 65\u2192Gaussian-process surrogate that proposes the next batch of designs.}\n 66\u2192\\label{fig:overview}\n 67\u2192\\vspace{-0.4em}\n 68\u2192\\end{wrapfigure}\n 69\u2192\n 70\u2192Tensegrity structures combine compression-only members with a\n 71\u2192pre-tensioned tension network to achieve stiffness without rigid\n 72\u2192joinery~\\citep{skelton2009tensegrity}. Their\n 73\u2192nonlinear, often load-limiting force--displacement response makes them\n 74\u2192attractive for impact mitigation, packaging, and wearable protective\n 75\u2192gear~\\citep{pajunen2019design}. Idealized\n 76\u2192cable--strut tensegrities are difficult to manufacture at scale, but\n 77\u2192\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78\u21923D printing reproduce key tensegrity behaviors---including\n 79\u2192post-buckling stability and load-limiting force--displacement\n 80\u2192response~\\citep{pajunen2019design}---motivating our extension to\n 81\u2192multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82\u2192elements. PLA provides rigid struts; TPU provides flexible,\n 83\u2192rate-dependent tension elements whose viscoelasticity is expected to\n 84\u2192contribute hysteretic energy dissipation under impact loading,\n 85\u2192consistent with the energy-absorbing behavior reported for rigid--TPU\n 86\u2192multi-material prints~\\citep{khatri2024energy}.\n 87\u2192\n 88\u2192The challenge is that small geometric and topological changes can\n 89\u2192produce large changes in the measured response, and high-fidelity\n 90\u2192simulation of FDM specimens is computationally expensive and often\n 91\u2192struggles to capture physical defects such as interfacial slip and\n 92\u2192TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93\u2192trial-and-error sweeps over the joint design space are also infeasible:\n 94\u2192even modest parameter resolutions yield thousands of candidates, each\n 95\u2192requiring print and test time. We instead treat the physical\n 96\u2192experiment as the source of truth and use BO to select which specimens\n 97\u2192are most informative to fabricate next---an approach used in related\n 98\u2192closed-loop materials and structures\n 99\u2192campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100\u2192Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101\u2192set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102\u2192designs using parallel, noisy multi-objective BO driven by measured\n103\u2192$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104\u2192\n105\u2192\\section{Approach}\n106\u2192\\subsection{Closed-Loop Workflow}\n107\u2192Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108\u2192proceeds in four steps: (i)~a parameterized unit-cell design is\n109\u2192instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110\u2192is sliced and printed on a multi-material FDM system; (iii)~it is\n111\u2192tested under quasi-static compression and instrumented drop-weight\n112\u2192impact; and (iv)~the resulting performance metrics update a\n113\u2192Gaussian-process (GP) surrogate, which a batch acquisition function\n114\u2192queries to recommend the next set of candidates. Surrogate updating\n115\u2192and experiment selection are automated; specimen handling, slicing,\n116\u2192and test setup remain manual.\n117\u2192\n118\u2192\\subsection{Design Space}\n119\u2192We parameterize a family of unit cells using a core-wrapping\n120\u2192architecture in which rigid PLA struts are enclosed by continuous TPU\n121\u2192skins; the wrapping rationale follows multi-material FDM strategies in\n122\u2192which rigid panels are wrapped by stretchable soft material to prevent\n123\u2192delamination and improve interfacial bonding at the rigid--flexible\n124\u2192interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125\u2192comprises continuous geometric variables (strut diameter and length,\n126\u2192tension-element width and thickness), an integer strut count per unit\n127\u2192cell, and two categorical variables (connectivity topology, unit-cell\n128\u2192tiling pattern). Categorical choices are encoded explicitly rather\n129\u2192than embedded in a continuous metric, and bounds on the continuous\n130\u2192variables are chosen to remain within the printer's resolution and\n131\u2192overhang limits and to keep specimens in a common form factor for the\n132\u2192test fixtures.\n133\u2192\n134\u2192\\subsection{Surrogate and Acquisition}\n135\u2192The GP surrogate is trained on three measured performance metrics:\n136\u2192peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137\u2192(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138\u2192objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139\u2192likelihood with priors to avoid pathological length scales at small $n$.\n140\u2192Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141\u2192interfacial defects motivates our use of q-noisy expected hypervolume\n142\u2192improvement (qNEHVI), which explicitly accounts for observation noise\n143\u2192in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144\u2192expected-improvement variants. Print failures are treated as a\n145\u2192probabilistic feasibility constraint: the acquisition score for each\n146\u2192candidate is its qNEHVI improvement weighted by the GP-estimated\n147\u2192probability of successful fabrication, following recent constrained\n148\u2192multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149\u2192Where applicable, we use numerically stable log-space variants of\n150\u2192improvement-based acquisitions to mitigate vanishing-gradient\n151\u2192pathologies as data and constraints\n152\u2192accumulate~\\citep{ament2023logei}.\n153\u2192\n154\u2192\\section{Methods}\n155\u2192\\subsection{Fabrication}\n156\u2192Specimens are fabricated on a multi-material FDM system capable of\n157\u2192co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158\u2192Print parameters (temperature, line width, infill, retraction) are\n159\u2192held fixed within a batch to isolate the effect of the design\n160\u2192variables. Each specimen is weighed and dimensionally inspected prior\n161\u2192to testing so that mass and geometric deviations are available as\n162\u2192covariates.\n163\u2192\n164\u2192\\subsection{Mechanical Testing}\n165\u2192Quasi-static compression is performed on a screw-driven load frame at\n166\u2192a fixed nominal strain rate, with force--displacement recorded to\n167\u2192densification. Drop-weight impact testing uses an instrumented tup at\n168\u2192a fixed impact energy; transmitted force is captured at high sample\n169\u2192rate, and specimens are imaged before and after impact to document\n170\u2192failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171\u2192unit mass, and compaction efficiency $\\eta$, defined consistently\n172\u2192across the campaign. A control specimen from an earlier batch is\n173\u2192retested periodically to monitor TPU batch-to-batch and printer drift.\n174\u2192\n175\u2192\\section{Expected Outcomes and Discussion}\n176\u2192We will report how BO-selected designs evolve across iterations, how\n177\u2192GP surrogate predictions and uncertainties change as data accumulate,\n178\u2192and which Pareto-efficient trade-offs emerge between SEA and peak\n179\u2192transmitted force within the explored design space. We will also\n180\u2192discuss practical lessons for operating the closed-loop workflow,\n181\u2192including handling of print failures, batch-to-batch variability in\n182\u2192TPU response, the influence of the rigid--flexible interface on\n183\u2192energy absorption, and the exploration--exploitation balance of the\n184\u2192acquisition function under realistic experimental noise. We expect\n185\u2192the campaign to clarify where physical experimentation is genuinely\n186\u2192required versus where simpler analytical or multi-fidelity surrogate\n187\u2192shortcuts~\\citep{mo2023accelerated} could be\n188\u2192substituted in future work.\n189\u2192\n190\u2192\\section{Conclusion}\n191\u2192This work demonstrates a partially automated, experiment-driven\n192\u2192closed-loop pipeline for designing multi-material 3D-printed\n193\u2192tensegrity-inspired energy absorbers, with surrogate updating and\n194\u2192candidate selection automated and fabrication and testing kept manual.\n195\u2192By updating a GP surrogate directly from physical measurements, the\n196\u2192workflow avoids direct dependence on calibrated finite-element\n197\u2192simulation for objective evaluation while still providing principled,\n198\u2192multi-objective design recommendations. The same closed-loop pattern is\n199\u2192expected to transfer to other multi-material additively manufactured\n200\u2192architectures whose performance is dominated by hard-to-simulate\n201\u2192effects.\n202\u2192\n203\u2192{\\tiny\n204\u2192\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205\u2192\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206\u2192% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207\u2192% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208\u2192\\providecommand{\\doi}[1]{}\n209\u2192\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210\u2192\\bibliography{references}\n211\u2192}\n212\u2192\n213\u2192\\end{document}", + "name": "read", + "tool_call_id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "info": { + "start_ts": 8147.332633466, + "end_ts": 8147.339043095 + } + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "callback_type": "before_transition" + }, + "action": { + "call_id": { + "run_id": "d134fa21-46a1-4b97-b793-63625703b95d", + "fwd_id": "1dbeb877-a294-4690-8008-386a66c012b6" + }, + "op_name": "IdentityOp-4f030d6055f0", + "op_class_name": "ldp.graph.common_ops.IdentityOp", + "value": { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ] + }, + "logprob": null + } + } + }, + "created_at": "2026-07-26T02:29:22.720513Z" + }, + { + "id": "e0e34e01-1cc9-4bf5-b2ff-65c701702402", + "trajectory_id": "afade0b1-f49e-4031-a86d-cb5ec4652536", + "trajectory_timestep": 0, + "step": "after_transition", + "state": { + "transition": { + "timestep": 0, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. 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Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + } + ], + "next_observation": [ + { + "role": "tool", + "content": " 1\u2192# IDETC Tensegrity Slides \u2014 Draft 1 (extracted content)\n 2\u2192\n 3\u2192Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4\u2192\n 5\u2192\n 6\u2192## Slide 1 \u2014 layout: Title Only\n 7\u2192\n 8\u2192**On-slide text:**\n 9\u2192- **Title 1**: Let\u2019s build better tensegrity structures faster\n 10\u2192- **TextBox 4**: Marcus Madsen\n 11\u2192Undergraduate researcher under Sterling Baird, Ph.D.\n 12\u2192- **TextBox 6**: IDETC 2026\n 13\u2192\n 14\u2192**Non-text shapes / media / placeholders:**\n 15\u2192- [PICTURE (13)] Picture 3\n 16\u2192- [PICTURE (13)] Picture 8\n 17\u2192\n 18\u2192**Presenter notes:**\n 19\u2192> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20\u2192> \n 21\u2192> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22\u2192\n 23\u2192\n 24\u2192## Slide 2 \u2014 layout: Title Only\n 25\u2192\n 26\u2192**On-slide text:**\n 27\u2192- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings\u2014and to problems closer to home.\n 28\u2192\n 29\u2192**Non-text shapes / media / placeholders:**\n 30\u2192- [MEDIA (16)] Online Media 5\n 31\u2192\n 32\u2192**Presenter notes:**\n 33\u2192> Replace this video with a downloaded and trimmed version of just the fall. \n 34\u2192> \n 35\u2192> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle \u2014 thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander\u00a0is\u00a0the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit\u00a0and get back up\u00a0\u2014 it survives multiple drops and keeps working.\n 36\u2192\n 37\u2192\n 38\u2192## Slide 3 (HIDDEN) \u2014 layout: Title Only\n 39\u2192\n 40\u2192**On-slide text:**\n 41\u2192- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42\u2192- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43\u2192- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44\u2192\n 45\u2192**Presenter notes:**\n 46\u2192> This might be better explained purely vocally while on the previous slide.\n 47\u2192\n 48\u2192\n 49\u2192## Slide 4 \u2014 layout: Title Only\n 50\u2192\n 51\u2192**On-slide text:**\n 52\u2192- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53\u2192- **TextBox 3**: Iteration\n 54\u2192- **TextBox 5**: Building\n 55\u2192- **TextBox 7**: Testing\n 56\u2192\n 57\u2192**Non-text shapes / media / placeholders:**\n 58\u2192- [MEDIA (16)] AdobeStock_207717222\n 59\u2192- [MEDIA (16)] Online Media 2\n 60\u2192- [PICTURE (13)] Picture 14\n 61\u2192- [PICTURE (13)] Picture 16\n 62\u2192- [PICTURE (13)] Picture 18\n 63\u2192\n 64\u2192**Presenter notes:**\n 65\u2192> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn\u2019t become distracting because it\u2019s fun to watch it spin.\n 66\u2192> \n 67\u2192> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68\u2192\n 69\u2192\n 70\u2192## Slide 5 (HIDDEN) \u2014 layout: Title Only\n 71\u2192\n 72\u2192**On-slide text:**\n 73\u2192- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74\u2192- **TextBox 14**: Baird et al. Matter (2022)\n 75\u2192\n 76\u2192**Non-text shapes / media / placeholders:**\n 77\u2192- [GROUP (6)] Group 2\n 78\u2192\n 79\u2192**Presenter notes:**\n 80\u2192> Consult Sterling on this slide. I don\u2019t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it\u2019s well enough known that it\u2019d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81\u2192\n 82\u2192\n 83\u2192## Slide 6 (HIDDEN) \u2014 layout: Title Only\n 84\u2192\n 85\u2192**On-slide text:**\n 86\u2192- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87\u2192- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88\u2192\n 89\u2192**Presenter notes:**\n 90\u2192> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91\u2192\n 92\u2192\n 93\u2192## Slide 7 \u2014 layout: Title Only\n 94\u2192\n 95\u2192**On-slide text:**\n 96\u2192- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97\u2192- **TextBox 5**: 6 variables\n 98\u2192- **TextBox 7**: 2 types of input data\n 99\u2192- **TextBox 9**: 2 objectives\n100\u2192- **TextBox 17**: *image of blackbox*\n101\u2192\n102\u2192**Non-text shapes / media / placeholders:**\n103\u2192- [LINE (9)] Connector: Curved 11\n104\u2192- [LINE (9)] Connector: Curved 13\n105\u2192- [LINE (9)] Connector: Curved 15\n106\u2192\n107\u2192**Presenter notes:**\n108\u2192> Very rough slide as I\u2019m not entirely sure what graphic or text to put on this, partially because I\u2019m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109\u2192\n110\u2192\n111\u2192## Slide 8 \u2014 layout: Title Only\n112\u2192\n113\u2192**On-slide text:**\n114\u2192- **Title 1**: Multi-material additive manufacturing supplies that data fast \u2014 single-build co-fabrication, with no joining of parts.\n115\u2192\n116\u2192**Non-text shapes / media / placeholders:**\n117\u2192- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118\u2192- [PICTURE (13)] Picture 4\n119\u2192\n120\u2192**Presenter notes:**\n121\u2192> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122\u2192\n123\u2192\n124\u2192## Slide 9 \u2014 layout: Title Only\n125\u2192\n126\u2192**On-slide text:**\n127\u2192- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128\u2192- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129\u2192- **TextBox 5**: *placeholder for slo motion of drop test*\n130\u2192\n131\u2192\n132\u2192## Slide 10 \u2014 layout: Title Only\n133\u2192\n134\u2192**On-slide text:**\n135\u2192- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136\u2192- **TextBox 3**: Printing\n137\u2192- **TextBox 5**: Testing\n138\u2192- **TextBox 7**: BO feed\n139\u2192\n140\u2192**Non-text shapes / media / placeholders:**\n141\u2192- [PICTURE (13)] Picture 14\n142\u2192- [PICTURE (13)] Picture 16\n143\u2192- [PICTURE (13)] Picture 18\n144\u2192- [PICTURE (13)] Graphic 8\n145\u2192\n146\u2192**Presenter notes:**\n147\u2192> I think it might be better if we replaced \u201cprinting\u201d with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148\u2192\n149\u2192\n150\u2192## Slide 11 (HIDDEN) \u2014 layout: Title Only\n151\u2192\n152\u2192**On-slide text:**\n153\u2192- **Title 1**: Reserved for results\n154\u2192\n155\u2192**Presenter notes:**\n156\u2192> Would be helpful if we could show that this process works in this case.\n157\u2192\n158\u2192\n159\u2192## Slide 12 \u2014 layout: Title Only\n160\u2192\n161\u2192**On-slide text:**\n162\u2192- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163\u2192- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164\u2192- **TextBox 3**: *image to show lattice structure\n165\u2192\n166\u2192\n167\u2192## Slide 13 \u2014 layout: Title Only\n168\u2192\n169\u2192**Non-text shapes / media / placeholders:**\n170\u2192- [PICTURE (13)] Picture 3\n171\u2192\n172\u2192**Presenter notes:**\n173\u2192> Slide for questions at the end\n174\u2192\n175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "info": { + "start_ts": 8147.330900226, + "end_ts": 8147.337548875 + } + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Presentation \u2014 Doumont Presentation Structure Template\n 2\u2192\n 3\u2192Transcribed from Jean-luc Doumont's presentation-structure template\n 4\u2192([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5\u2192adapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\n 6\u2192All rights reserved. Can be downloaded from .\n 7\u2192\n 8\u2192> To design your presentation, write down your ideas for each component below.\n 9\u2192> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10\u2192\n 11\u2192**Draft 3** \u2014 applies the inline review comments from @sgbaird on PR #84 and the\n 12\u2192priority revisions from the\n 13\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14\u2192Summaries of what changed are at the bottom of this file\n 15\u2192([Draft 2 \u2192 3](#changes-from-draft-2), [Draft 1 \u2192 2](#changes-from-draft-1)).\n 16\u2192\n 17\u2192**Scope note (frame everything below with this):** the study presented is the\n 18\u2192optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19\u2192processing parameters** (e.g., nozzle temperature, print speed). PLA\u2013TPU is a\n 20\u2192**proxy system** \u2014 not flight material \u2014 used to prototype a self-driving-lab\n 21\u2192style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22\u2192The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23\u2192beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24\u2192\n 25\u2192## Opening\n 26\u2192\n 27\u2192### Attention getter\n 28\u2192\n 29\u2192*A way to lead the audience to the need efficiently*\n 30\u2192\n 31\u2192Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32\u2192landings \u2014 and to problems closer to home.\n 33\u2192\n 34\u2192One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35\u2192clip) do the work:\n 36\u2192\n 37\u2192> Landing a payload on another planet means surviving an impact in conditions\n 38\u2192> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n 39\u2192> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40\u2192> structure: the lander *is* the energy absorber. And unlike crushable\n 41\u2192> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42\u2192> hit **and get back up** \u2014 it survives multiple drops and keeps working.\n 43\u2192\n 44\u2192Candidate Super Ball Bot videos to show (verified links):\n 45\u2192\n 46\u2192- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n 47\u2192 (official channel); the canonical overview.\n 48\u2192- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49\u2192 \u2014 Wall Street Journal; prototype drop footage.\n 50\u2192- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51\u2192 \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n 52\u2192\n 53\u2192Immediately after the hook, state the present study scope in one sentence\n 54\u2192(per the scope note above): *we study a printed PLA\u2013TPU T3 prism as a proxy\n 55\u2192system for prototyping an autonomous tensegrity-optimization workflow* \u2014 so\n 56\u2192the planetary framing never outruns the demonstrated scope.\n 57\u2192\n 58\u2192\n 60\u2192\n 61\u2192### Need\n 62\u2192\n 63\u2192*A difference between actual and desired situations*\n 64\u2192\n 65\u2192**Actual situation** (a two-sided pincer \u2014 stated as a scoped engineering\n 66\u2192judgment, not an absolute):\n 67\u2192\n 68\u2192- For these structures, obtaining trustworthy objective values from a\n 69\u2192 validated high-fidelity model would require substantial calibration of\n 70\u2192 interface, rate, contact, and defect behavior \u2014 and even heavy-duty\n 71\u2192 simulation falls short of the experiments we are running: peak transmitted\n 72\u2192 shock is a millisecond-scale transient that is notoriously difficult to\n 73\u2192 resolve numerically. This holds even for idealized cable\u2013strut tensegrities\n 74\u2192 in their actual engineering materials (steel or similar), not just for\n 75\u2192 printed PLA\u2013TPU.\n 76\u2192 \n 79\u2192- Exhaustive physical search fails on cost: a traditional design of\n 80\u2192 experiments over this relatively high-dimensional space (geometry + FDM\n 81\u2192 process parameters) would demand hundreds of specimens, each costing print\n 82\u2192 and test time.\n 83\u2192- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84\u2192 more complex structures \u2014 especially tensegrity *lattices* \u2014 become\n 85\u2192 extremely difficult and ultimately infeasible to hand-build.\n 86\u2192\n 87\u2192**Desired situation:**\n 88\u2192\n 89\u2192- Trustworthy performance data without hand assembly (easy creation of\n 90\u2192 tensegrity structures, scalable beyond a single prism).\n 91\u2192- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n 92\u2192 not the hundreds a traditional design of experiments would require.\n 93\u2192\n 94\u2192### Task\n 95\u2192\n 96\u2192*What I decided/was asked to do to address the need*\n 97\u2192\n 98\u2192Build a closed-loop design\u2013print\u2013test workflow that optimizes a **T3-prism\n 99\u2192tensegrity-inspired energy absorber together with its FDM processing\n100\u2192parameters** (e.g., temperature, print speed) directly from real, measured\n101\u2192impact data \u2014 maximizing energy absorbed per gram (specific energy\n102\u2192absorption) and compaction efficiency, subject to a cap on the peak force\n103\u2192transmitted to the payload. Extensions beyond the T3 prism are future work.\n104\u2192\n105\u2192### Main message\n106\u2192\n107\u2192*The one sentence I want my audience to remember*\n108\u2192\n109\u2192By closing the loop between multi-material 3D printing and Bayesian\n110\u2192optimization, we can optimize tensegrity-inspired energy absorbers directly\n111\u2192from real impact data \u2014 in dozens of prints, not the hundreds a traditional\n112\u2192design of experiments would demand.\n113\u2192\n114\u2192\n118\u2192\n119\u2192### Preview\n120\u2192\n121\u2192*A map of the body (ideally three points, max. five)*\n122\u2192\n123\u21921. When model calibration and exhaustive testing are both costly, each\n124\u2192 physical specimen must be chosen for its information value \u2014 so the\n125\u2192 measured experiment is our source of truth.\n126\u21922. Bayesian optimization makes physical experimentation affordable: it handles\n127\u2192 noisy measurements, small batches, and the constrained multi-objective\n128\u2192 trade-off \u2014 and Honegumi let us scaffold it in minutes.\n129\u21923. Multi-material additive manufacturing closes the loop: single-build PLA\u2013TPU\n130\u2192 co-fabrication turns each proposed design into test data in hours, with no\n131\u2192 joining of parts.\n132\u21924. *(Optional fourth point, if time allows)* The same closed loop points at\n133\u2192 what comes next: tensegrity lattices, flight-relevant materials, and\n134\u2192 safety hardware on Earth.\n135\u2192\n136\u2192## Body\n137\u2192\n138\u2192### Point 1\n139\u2192\n140\u2192When model calibration and exhaustive testing are both costly, each physical\n141\u2192specimen must be chosen for its information value.\n142\u2192\n143\u2192- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144\u2192 these structures, a sufficiently calibrated multiphysics model is expensive\n145\u2192 to build relative to the design campaign itself \u2014 and the hardest quantity,\n146\u2192 peak transmitted shock, is a millisecond-scale transient that even\n147\u2192 heavy-duty simulation struggles to resolve. The gap persists in the actual\n148\u2192 engineering materials a real lander would use (steel struts and cables),\n149\u2192 so this is not merely an artifact of printed PLA\u2013TPU.\n150\u2192- The alternative \u2014 sweeping the design space physically \u2014 fails on cost: a\n151\u2192 traditional design of experiments over geometry plus FDM process parameters\n152\u2192 would take hundreds of specimens, each needing print + test time.\n153\u2192- Therefore: treat the physical experiment as the authoritative source of\n154\u2192 objective values, and be ruthless about *which* experiments to run.\n155\u2192- Anticipate the FEA-veteran pushback (\"inconvenient \u2260 untrustworthy\"): frame\n156\u2192 it as a cost-and-dependence decision, and keep a backup slide with the\n157\u2192 literature (or our own validation) on simulation\u2013experiment discrepancy for\n158\u2192 impact transients.\n159\u2192\n160\u2192### Transition\n161\u2192\n162\u2192If every data point must be a real experiment, we need a method that extracts\n163\u2192the most from every specimen \u2014 we need to make the process radically more\n164\u2192sample-efficient.\n165\u2192\n166\u2192### Point 2\n167\u2192\n168\u2192Bayesian optimization makes physical experimentation affordable, even with\n169\u2192limited, noisy data.\n170\u2192\n171\u2192- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172\u2192 visual in the main talk; implementation detail goes to backup slides.\n173\u2192 Spoken version:\n174\u2192 > \"After each batch, the model estimates both expected performance and\n175\u2192 > uncertainty across the design space. The acquisition rule selects a small\n176\u2192 > next batch that is most likely to expand the feasible trade-off frontier,\n177\u2192 > while accounting for noisy tests and print failures.\"\n178\u2192- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179\u2192 motivate a noise-aware acquisition function (qNEHVI) rather than\n180\u2192 non-noise-aware alternatives. (Backup slide: how observation noise is\n181\u2192 actually modeled \u2014 replicates, noise identification \u2014 and budget-matched\n182\u2192 baselines such as Sobol/random sampling.)\n183\u2192- Small parallel batches: the surrogate proposes the next batch of specimens\n184\u2192 to print, balancing exploration and exploitation.\n185\u2192- Constrained multi-objective by construction: **objectives** = maximize\n186\u2192 specific energy absorption and compaction efficiency; **constraint** = peak\n187\u2192 transmitted force must stay under a cap; the deliverable is the *feasible*\n188\u2192 Pareto front between the two objectives among force-cap-satisfying designs.\n189\u2192 (Keep this formulation consistent everywhere \u2014 do not present \"SEA vs. peak\n190\u2192 force\" as the trade-off.)\n191\u2192- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192\u2192 this advanced task (noisy, batched, constrained, multi-objective) in\n193\u2192 minutes using [Honegumi](https://honegumi.readthedocs.io/) \u2014 the audience\n194\u2192 can do the same for their own problems; show the code-template picker\n195\u2192 briefly so they see how low the barrier is.\n196\u2192\n197\u2192### Transition\n198\u2192\n199\u2192The optimizer is no longer the bottleneck \u2014 the limit is now providing real\n200\u2192data quickly enough.\n201\u2192\n202\u2192### Point 3\n203\u2192\n204\u2192Multi-material additive manufacturing supplies that data fast \u2014 single-build\n205\u2192co-fabrication, with no joining of parts.\n206\u2192\n207\u2192- Single-build PLA\u2013TPU co-fabrication: rigid struts and flexible tension\n208\u2192 network in one print \u2014 no joining or hand assembly of members, which closes\n209\u2192 the \"easy creation\" gap from the Need and is what makes future lattices\n210\u2192 feasible at all. (Be precise: if support removal, trimming, or conditioning\n211\u2192 remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212\u2192 assembly.\")\n213\u2192- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214\u2192 as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215\u2192 pre-tension status) plus a short deformation sequence \u2014 so the EDL engineer\n216\u2192 doesn't file it as a \"crush structure\" and the grad student can tell it from\n217\u2192 an ordinary lattice.\n218\u2192- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219\u2192 tested specimen (quasi-static compression + instrumented drop-weight impact)\n220\u2192 in hours. (Backup slide: representative print-to-result time breakdown and\n221\u2192 which steps remain manual \u2014 candidate selection is automated; slicing,\n222\u2192 handling, and testing are not yet.)\n223\u2192- Now we have the data we need to complete the loop.\n224\u2192\n225\u2192### Point 4 (optional \u2014 future applications)\n226\u2192\n227\u2192*Include if timing allows; otherwise fold into the Conclusion.*\n228\u2192\n229\u2192The same closed loop transfers wherever tests are authoritative, each test is\n230\u2192costly, the design space is mixed, and several performance goals conflict:\n231\u2192\n232\u2192- Tensegrity *lattices* and more complex architectures \u2014 exactly where hand\n233\u2192 assembly becomes infeasible and single-build co-fabrication pays off.\n234\u2192- Migration from the PLA\u2013TPU proxy toward flight-relevant materials and\n235\u2192 impact regimes.\n236\u2192- Safety hardware on Earth (e.g., the tensegrity crutch tip \u2014 future work).\n237\u2192- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238\u2192 handling, testing) in the loop.\n239\u2192\n240\u2192### Evidence / results\n241\u2192\n242\u2192*What we found \u2014 ground the conclusion in shown data*\n243\u2192\n244\u2192Build the talk around a quantitative three-slide sequence (per the\n245\u2192mock-audience report; insert observed values once the campaign completes):\n246\u2192\n247\u21921. **Campaign ledger** \u2014 \"The campaign used N specimens across B adaptive\n248\u2192 batches under a fixed physical-test budget\": initialization vs. adaptive\n249\u2192 batches, replicates, print failures, cycle time, and the numerical value\n250\u2192 (and rationale) of the force cap.\n251\u21922. **Budget-matched baseline** \u2014 \"Adaptive selection improved the feasible\n252\u2192 design set faster than [predeclared baseline]\": performance vs. number of\n253\u2192 physical tests against Sobol/random sampling at the same budget, with\n254\u2192 uncertainty where available.\n255\u21923. **Measured feasible Pareto front** \u2014 specific energy absorption vs.\n256\u2192 compaction efficiency among designs satisfying the force cap, with\n257\u2192 replicate spread, marked infeasible designs, and specimen photos or\n258\u2192 force\u2013time traces for representative Pareto designs.\n259\u2192\n260\u2192Supporting raw-data candidates: drop-test force\u2013time traces, Sobol /\n261\u2192first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262\u2192\n263\u2192### Suggested 15-minute allocation\n264\u2192\n265\u2192*(From the mock-audience report \u2014 results get the largest block.)*\n266\u2192\n267\u2192| Time | Content |\n268\u2192|---|---|\n269\u2192| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270\u2192| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271\u2192| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272\u2192| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273\u2192| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274\u2192| 1 min | Limitations and transfer conditions |\n275\u2192| 1 min | Measured conclusion + bookend |\n276\u2192\n277\u2192Move kernel choices, encodings, and other implementation detail to backup\n278\u2192slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279\u2192[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280\u2192\n281\u2192## Closing\n282\u2192\n283\u2192### Review\n284\u2192\n285\u2192*A recap of the body, leading into the conclusion*\n286\u2192\n287\u2192Using real, measured impact data from additively manufactured\n288\u2192tensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\n289\u2192optimization routine \u2014 we rapidly iterate toward better energy absorbers.\n290\u2192This feedback loop makes tensegrity optimization dramatically faster: dozens\n291\u2192of prints instead of the hundreds a traditional design of experiments over\n292\u2192this design space would require.\n293\u2192\n294\u2192### Conclusion\n295\u2192\n296\u2192*What the above means to the audience in the end*\n297\u2192\n298\u2192Because the loop runs on physical measurements, it is useful precisely where\n299\u2192model calibration is not worth its cost \u2014 and the same closed-loop pattern\n300\u2192should transfer (a conditional claim, pending evidence) to other additively\n301\u2192manufactured architectures whose performance is dominated by\n302\u2192hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303\u2192*use this loop when tests are authoritative, each test is costly, the design\n304\u2192space is mixed, and several performance goals conflict.* For tensegrity\n305\u2192specifically, it opens a path from this T3 proxy study toward lattices,\n306\u2192flight-relevant materials, payload protection on other planets, and safety\n307\u2192hardware here on Earth (e.g., the crutch tip, as future work).\n308\u2192\n309\u2192### Close\n310\u2192\n311\u2192*A way to end the presentation clearly and elegantly*\n312\u2192\n313\u2192Return to the opening image: the next planetary lander's energy absorber \u2014 a\n314\u2192structure that has to survive not one impact but many \u2014 doesn't have to take\n315\u2192years of hand-built prototypes; with this loop, candidate designs can be\n316\u2192designed, printed, and tested against real impacts in weeks instead of months\n317\u2192or longer. End on the measured engineering result and its transfer condition,\n318\u2192then the bookend: every method that compresses design time moves our ventures\n319\u2192\u2014 on this planet and beyond \u2014 forward.\n320\u2192\n321\u2192---\n322\u2192\n323\u2192## Changes from Draft 2\n324\u2192\n325\u2192Applied from @sgbaird's inline review comments on PR #84 and the\n326\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327\u2192\n328\u2192**From the inline review comments:**\n329\u2192\n330\u21921. **Reusability in the hook** (line 22): the tensegrity lander survives\n331\u2192 multiple drops, unlike crushable/sacrificial alternatives.\n332\u21922. **Proxy-system framing** (lines 49, 87): PLA\u2013TPU would never fly; it is a\n333\u2192 proxy for prototyping a self-driving-lab-style workflow \u2014 stated in a\n334\u2192 scope note up front and re-stated right after the hook.\n335\u21923. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336\u2192 heavy-duty simulation falls short of the experiments (millisecond-scale\n337\u2192 peak shock), not that it merely costs too much.\n338\u21924. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339\u2192 complex structures and especially lattices are not.\n340\u21925. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341\u2192 (temperature, print speed, \u2026) only; the rest is explicitly future work.\n342\u21926. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343\u2192 literature search (or validation of experiments against simulation), plus\n344\u2192 the millisecond-resolution peak-shock argument.\n345\u21927. **Optional Point 4 added** (line 97): future applications, with a note it\n346\u2192 can fold into the Conclusion if time is short.\n347\u21928. **Steel-materials point** (line 104): simulations fall short for the\n348\u2192 actual engineering materials (steel struts/cables), not just printed\n349\u2192 PLA\u2013TPU.\n350\u21929. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351\u2192 in minutes, pitched so the audience sees they can use it too.\n352\u219210. **\"Thousands\" \u2192 \"hundreds\"** (line 173): baseline is now a traditional\n353\u2192 design of experiments over a high-dimensional space (hundreds), in the\n354\u2192 main message, Need, Point 1, and Review.\n355\u219211. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356\u2192 or longer\" for candidate designs.\n357\u2192\n358\u2192**From the mock-audience report's priority revisions:**\n359\u2192\n360\u219212. **Quantitative three-slide results sequence** (Priority 1): campaign\n361\u2192 ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front, with a\n362\u2192 fallback main-message wording if the campaign can't yet support the\n363\u2192 efficiency claim.\n364\u219213. **Anti-simulation premise replaced with a scoped cost decision**\n365\u2192 (Priority 2): Point 1 retitled to the information-value message;\n366\u2192 \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367\u2192 anticipated with a backup slide.\n368\u219214. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369\u2192 implementation detail moved to backup slides; Q&A prep pointed at the\n370\u2192 report's claims-vs-evidence table.\n371\u219215. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372\u2192 the main talk, machinery in backup slides; the misleading\n373\u2192 \"qNEHVI over standard expected improvement\" comparison replaced with\n374\u2192 \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375\u219216. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376\u2192 sequence in Point 3, so the structure isn't remembered as a generic crush\n377\u2192 structure or lattice.\n378\u219217. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379\u2192 co-fabrication / no joining of parts,\" with a note to list remaining\n380\u2192 manual steps and an automation-status caveat.\n381\u219218. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382\u2192 compaction efficiency, constraint = force cap, deliverable = *feasible*\n383\u2192 Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384\u219219. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385\u2192 transfer claim marked conditional, and the close ends on the measured\n386\u2192 result before the bookend.\n387\u2192\n388\u2192## Changes from Draft 1\n389\u2192\n390\u2192Feedback from the PR #84 review, applied in Draft 2:\n391\u2192\n392\u21921. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n393\u2192 dozens-not-thousands claim); body and message now carry the same story.\n394\u21922. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n395\u2192 manuscript's positioning (#75/#76) is that the physical experiment is the\n396\u2192 source of truth, explicitly avoiding calibrated FE simulation.\n397\u21923. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n398\u2192 parametric design space and avoid an over-claim to a design-automation\n399\u2192 audience.\n400\u21924. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401\u2192 energy absorption and compaction efficiency, cap peak transmitted force.\n402\u21925. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403\u2192 *and* the space is too big to brute-force \u2014 which makes BO + AM the\n404\u2192 inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405\u2192 above.)*\n406\u21926. **Point 1 given its own content** (the *why* behind the inefficiency)\n407\u2192 instead of restating the Need.\n408\u21927. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409\u2192 the specifics (noise-aware acquisition, small batches, multi-objective).\n410\u21928. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411\u2192 (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412\u2192 above.)*\n413\u21929. **Evidence/results slot added** before the Closing, so the Conclusion is\n414\u2192 grounded in shown data rather than three hedged \"may\"s.\n415\u219210. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n416\u2192 Ball Bot visual; verified video links included.\n417\u219211. **Close bookends the opening** (the next lander's absorber, designed in\n418\u2192 weeks) before the broader humankind line.\n419\u219212. **Spelling**: Doumont (was \"Duomont\").", + "name": "read", + "tool_call_id": "call_93haurOMDGiH2mQFFbaMaYKn", + "info": { + "start_ts": 8147.331065886, + "end_ts": 8147.336467845 + } + }, + { + "role": "tool", + "content": " 1\u2192# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n 2\u2192\n 3\u2192Video: (assigned in #83)\n 4\u2192\n 5\u2192Transcribed from @me-madsen's handwritten notes\n 6\u2192([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7\u2192dated 2026-07-15).\n 8\u2192\n 9\u2192## Overview\n10\u2192\n11\u2192A presentation is all about the right content, displayed the right way, at the\n12\u2192right time.\n13\u2192\n14\u2192- Verbal communication is sequential.\n15\u2192- Message is *interpretation* of the information.\n16\u2192 - Ensure the audience can get the most out of what you're doing.\n17\u2192 - Don't just give them information \u2014 help them understand it, interpret it,\n18\u2192 and internalize it.\n19\u2192\n20\u2192## Doumont's suggested rules for a presentation\n21\u2192\n22\u21920. Ensure you have **messages** for your audience, not just words.\n23\u21921. **Adapt to your audience.**\n24\u2192 - Think like the audience, not the speaker.\n25\u21922. **Maximize signal-to-noise ratio.**\n26\u2192 - Reduce noise (animations, laser pointers, anything that pulls attention\n27\u2192 away from your message).\n28\u21923. **Use effective redundancy.**\n29\u2192 - Tell your message in word *and* on slide: someone deaf should understand\n30\u2192 your messages just as well as someone blind.\n31\u2192 - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32\u2192 date).\n33\u2192\n34\u2192## When creating slides\n35\u2192\n36\u2192- If the audience doesn't need it, why put it on the slides?\n37\u2192- People shouldn't have to count the items on a slide to know how many are\n38\u2192 there.\n39\u2192- Don't use your slides as your \"presentation notes.\"\n40\u2192 - Don't show your notes to the audience.\n41\u2192 - **A bad slide is worse than no slide at all.**\n42\u2192- **One message per slide** is a good rule of thumb.\n43\u2192 - Prioritize the *so what* (or *therefore what*) of your message.\n44\u2192- Make it easy to interpret the information on the slide:\n45\u2192 - 3\u20135 items at most.\n46\u2192 - Align the title left.\n47\u2192 - Do not clutter slides with repetitive or unnecessary information: logos,\n48\u2192 affiliation, date, slide numbers.\n49\u2192- Doumont's suggested workflow:\n50\u2192 1. **Start with the title.**\n51\u2192 - Use a full sentence in the title.\n52\u2192 - The title is the main message of the slide.\n53\u2192 - Make it easy to read: left-aligned, with intentional line breaks.\n54\u2192 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55\u2192- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56\u2192\n57\u2192## General preparation\n58\u2192\n59\u2192- Planning for audience, content, and structure matters more than materials\n60\u2192 (i.e., slides).\n61\u2192- Try using one sheet of paper for presentation preparation.\n62\u2192- It's most important that your presentation is:\n63\u2192 - well planned, structured, and delivered;\n64\u2192 - **if you don't have a lot of time to prepare, don't focus on making\n65\u2192 slides.**\n66\u2192- Practice the presentation a few times. Practice at least once *without* your\n67\u2192 slideshow!\n68\u2192- If doing a handout, keep it separate from the slides you're preparing.\n69\u2192- Impress the audience with your body.\n70\u2192 - Use gestures, not laser pointers.\n71\u2192 - \"Control\" the audience with your body \u2014 keep their attention on your\n72\u2192 presentation.\n73\u2192\n74\u2192## Impressions / what to apply to the IDETC presentation\n75\u2192\n76\u2192\n77\u2192\n78\u2192## Questions / things to discuss\n79\u2192\n80\u2192", + "name": "read", + "tool_call_id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "info": { + "start_ts": 8147.331133416, + "end_ts": 8147.338304015 + } + }, + { + "role": "tool", + "content": " 1\u2192\\documentclass[10pt]{article}\n 2\u2192\n 3\u2192% --- Packages ---\n 4\u2192\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5\u2192\\usepackage[hidelinks]{hyperref}\n 6\u2192\\usepackage{parskip}\n 7\u2192\\usepackage{graphicx}\n 8\u2192\\usepackage{caption}\n 9\u2192\\usepackage{titlesec}\n 10\u2192\\usepackage{wrapfig}\n 11\u2192\\usepackage[numbers,sort&compress]{natbib}\n 12\u2192\n 13\u2192% --- Formatting ---\n 14\u2192\\setlength{\\parskip}{0.2em}\n 15\u2192\\setlength{\\parindent}{0em}\n 16\u2192\\titleformat*{\\section}{\\large\\bfseries}\n 17\u2192\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18\u2192\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19\u2192\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20\u2192\\setlength{\\emergencystretch}{2em}\n 21\u2192\\setlength{\\intextsep}{0.2em}\n 22\u2192\\bibliographystyle{abbrvnat}\n 23\u2192\n 24\u2192\\begin{document}\n 25\u2192\n 26\u2192\\begin{center}\n 27\u2192{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28\u21923D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29\u2192\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30\u2192\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31\u2192\\end{center}\n 32\u2192\n 33\u2192\\vspace{0.4em}\n 34\u2192\n 35\u2192\\section*{Abstract}\n 36\u2192Tensegrity-inspired architectures---rigid struts suspended within a\n 37\u2192continuous flexible network---can exhibit tunable nonlinear\n 38\u2192force--displacement responses and favorable energy absorption per unit\n 39\u2192mass, motivating their use in lightweight energy-absorbing and protective\n 40\u2192structures\n 41\u2192\\citep{skelton2009tensegrity,pajunen2019design}.\n 42\u2192Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43\u2192struts and flexible (TPU) elements in a single build\n 44\u2192\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45\u2192space---strut geometry, tension-element cross-section, connectivity\n 46\u2192topology, and unit-cell tiling---is too large to explore by trial and\n 47\u2192error. We present a closed-loop experimental campaign that uses\n 48\u2192Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49\u2192to drive a design--print--test workflow operating directly on physical\n 50\u2192measurements, without relying on calibrated finite-element simulation\n 51\u2192for objective evaluation.\n 52\u2192\n 53\u2192\\section{Introduction and Motivation}\n 54\u2192\n 55\u2192\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56\u2192\\centering\n 57\u2192\\vspace{-0.4em}\n 58\u2192\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59\u2192\\captionsetup{font=small}\n 60\u2192\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61\u2192Candidate tensegrity-inspired unit cells are instantiated from a\n 62\u2192parameter vector, fabricated by multi-material FDM, and tested under\n 63\u2192quasi-static compression and drop-weight impact. Measured peak force,\n 64\u2192specific energy absorption (SEA), and compaction efficiency update a\n 65\u2192Gaussian-process surrogate that proposes the next batch of designs.}\n 66\u2192\\label{fig:overview}\n 67\u2192\\vspace{-0.4em}\n 68\u2192\\end{wrapfigure}\n 69\u2192\n 70\u2192Tensegrity structures combine compression-only members with a\n 71\u2192pre-tensioned tension network to achieve stiffness without rigid\n 72\u2192joinery~\\citep{skelton2009tensegrity}. Their\n 73\u2192nonlinear, often load-limiting force--displacement response makes them\n 74\u2192attractive for impact mitigation, packaging, and wearable protective\n 75\u2192gear~\\citep{pajunen2019design}. Idealized\n 76\u2192cable--strut tensegrities are difficult to manufacture at scale, but\n 77\u2192\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78\u21923D printing reproduce key tensegrity behaviors---including\n 79\u2192post-buckling stability and load-limiting force--displacement\n 80\u2192response~\\citep{pajunen2019design}---motivating our extension to\n 81\u2192multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82\u2192elements. PLA provides rigid struts; TPU provides flexible,\n 83\u2192rate-dependent tension elements whose viscoelasticity is expected to\n 84\u2192contribute hysteretic energy dissipation under impact loading,\n 85\u2192consistent with the energy-absorbing behavior reported for rigid--TPU\n 86\u2192multi-material prints~\\citep{khatri2024energy}.\n 87\u2192\n 88\u2192The challenge is that small geometric and topological changes can\n 89\u2192produce large changes in the measured response, and high-fidelity\n 90\u2192simulation of FDM specimens is computationally expensive and often\n 91\u2192struggles to capture physical defects such as interfacial slip and\n 92\u2192TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93\u2192trial-and-error sweeps over the joint design space are also infeasible:\n 94\u2192even modest parameter resolutions yield thousands of candidates, each\n 95\u2192requiring print and test time. We instead treat the physical\n 96\u2192experiment as the source of truth and use BO to select which specimens\n 97\u2192are most informative to fabricate next---an approach used in related\n 98\u2192closed-loop materials and structures\n 99\u2192campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100\u2192Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101\u2192set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102\u2192designs using parallel, noisy multi-objective BO driven by measured\n103\u2192$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104\u2192\n105\u2192\\section{Approach}\n106\u2192\\subsection{Closed-Loop Workflow}\n107\u2192Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108\u2192proceeds in four steps: (i)~a parameterized unit-cell design is\n109\u2192instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110\u2192is sliced and printed on a multi-material FDM system; (iii)~it is\n111\u2192tested under quasi-static compression and instrumented drop-weight\n112\u2192impact; and (iv)~the resulting performance metrics update a\n113\u2192Gaussian-process (GP) surrogate, which a batch acquisition function\n114\u2192queries to recommend the next set of candidates. Surrogate updating\n115\u2192and experiment selection are automated; specimen handling, slicing,\n116\u2192and test setup remain manual.\n117\u2192\n118\u2192\\subsection{Design Space}\n119\u2192We parameterize a family of unit cells using a core-wrapping\n120\u2192architecture in which rigid PLA struts are enclosed by continuous TPU\n121\u2192skins; the wrapping rationale follows multi-material FDM strategies in\n122\u2192which rigid panels are wrapped by stretchable soft material to prevent\n123\u2192delamination and improve interfacial bonding at the rigid--flexible\n124\u2192interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125\u2192comprises continuous geometric variables (strut diameter and length,\n126\u2192tension-element width and thickness), an integer strut count per unit\n127\u2192cell, and two categorical variables (connectivity topology, unit-cell\n128\u2192tiling pattern). Categorical choices are encoded explicitly rather\n129\u2192than embedded in a continuous metric, and bounds on the continuous\n130\u2192variables are chosen to remain within the printer's resolution and\n131\u2192overhang limits and to keep specimens in a common form factor for the\n132\u2192test fixtures.\n133\u2192\n134\u2192\\subsection{Surrogate and Acquisition}\n135\u2192The GP surrogate is trained on three measured performance metrics:\n136\u2192peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137\u2192(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138\u2192objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139\u2192likelihood with priors to avoid pathological length scales at small $n$.\n140\u2192Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141\u2192interfacial defects motivates our use of q-noisy expected hypervolume\n142\u2192improvement (qNEHVI), which explicitly accounts for observation noise\n143\u2192in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144\u2192expected-improvement variants. Print failures are treated as a\n145\u2192probabilistic feasibility constraint: the acquisition score for each\n146\u2192candidate is its qNEHVI improvement weighted by the GP-estimated\n147\u2192probability of successful fabrication, following recent constrained\n148\u2192multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149\u2192Where applicable, we use numerically stable log-space variants of\n150\u2192improvement-based acquisitions to mitigate vanishing-gradient\n151\u2192pathologies as data and constraints\n152\u2192accumulate~\\citep{ament2023logei}.\n153\u2192\n154\u2192\\section{Methods}\n155\u2192\\subsection{Fabrication}\n156\u2192Specimens are fabricated on a multi-material FDM system capable of\n157\u2192co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158\u2192Print parameters (temperature, line width, infill, retraction) are\n159\u2192held fixed within a batch to isolate the effect of the design\n160\u2192variables. Each specimen is weighed and dimensionally inspected prior\n161\u2192to testing so that mass and geometric deviations are available as\n162\u2192covariates.\n163\u2192\n164\u2192\\subsection{Mechanical Testing}\n165\u2192Quasi-static compression is performed on a screw-driven load frame at\n166\u2192a fixed nominal strain rate, with force--displacement recorded to\n167\u2192densification. Drop-weight impact testing uses an instrumented tup at\n168\u2192a fixed impact energy; transmitted force is captured at high sample\n169\u2192rate, and specimens are imaged before and after impact to document\n170\u2192failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171\u2192unit mass, and compaction efficiency $\\eta$, defined consistently\n172\u2192across the campaign. A control specimen from an earlier batch is\n173\u2192retested periodically to monitor TPU batch-to-batch and printer drift.\n174\u2192\n175\u2192\\section{Expected Outcomes and Discussion}\n176\u2192We will report how BO-selected designs evolve across iterations, how\n177\u2192GP surrogate predictions and uncertainties change as data accumulate,\n178\u2192and which Pareto-efficient trade-offs emerge between SEA and peak\n179\u2192transmitted force within the explored design space. We will also\n180\u2192discuss practical lessons for operating the closed-loop workflow,\n181\u2192including handling of print failures, batch-to-batch variability in\n182\u2192TPU response, the influence of the rigid--flexible interface on\n183\u2192energy absorption, and the exploration--exploitation balance of the\n184\u2192acquisition function under realistic experimental noise. We expect\n185\u2192the campaign to clarify where physical experimentation is genuinely\n186\u2192required versus where simpler analytical or multi-fidelity surrogate\n187\u2192shortcuts~\\citep{mo2023accelerated} could be\n188\u2192substituted in future work.\n189\u2192\n190\u2192\\section{Conclusion}\n191\u2192This work demonstrates a partially automated, experiment-driven\n192\u2192closed-loop pipeline for designing multi-material 3D-printed\n193\u2192tensegrity-inspired energy absorbers, with surrogate updating and\n194\u2192candidate selection automated and fabrication and testing kept manual.\n195\u2192By updating a GP surrogate directly from physical measurements, the\n196\u2192workflow avoids direct dependence on calibrated finite-element\n197\u2192simulation for objective evaluation while still providing principled,\n198\u2192multi-objective design recommendations. The same closed-loop pattern is\n199\u2192expected to transfer to other multi-material additively manufactured\n200\u2192architectures whose performance is dominated by hard-to-simulate\n201\u2192effects.\n202\u2192\n203\u2192{\\tiny\n204\u2192\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205\u2192\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206\u2192% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207\u2192% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208\u2192\\providecommand{\\doi}[1]{}\n209\u2192\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210\u2192\\bibliography{references}\n211\u2192}\n212\u2192\n213\u2192\\end{document}", + "name": "read", + "tool_call_id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "info": { + "start_ts": 8147.332633466, + "end_ts": 8147.339043095 + } + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "time_elapsed_before_transition": 0.9857280759997593, + "time_elapsed_agent_get_asv": 2.217125295000187, + "time_elapsed_after_agent_get_asv": 0.9358747759997641, + "time_elapsed_env_step": 4.482478773999901, + "time_elapsed_after_env_step": 0.9301383860001806 + }, + "action": { + "call_id": { + "run_id": "d134fa21-46a1-4b97-b793-63625703b95d", + "fwd_id": "1dbeb877-a294-4690-8008-386a66c012b6" + }, + "op_name": "IdentityOp-4f030d6055f0", + "op_class_name": "ldp.graph.common_ops.IdentityOp", + "value": { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ] + }, + "logprob": null + } + } + }, + "created_at": "2026-07-26T02:29:21.132573Z" + }, + { + "id": "ab7726c3-78de-4848-9086-a53a010a8c4d", + "trajectory_id": "afade0b1-f49e-4031-a86d-cb5ec4652536", + "trajectory_timestep": 0, + "step": "after_env_step", + "state": { + "transition": { + "timestep": 0, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. 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For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. 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If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + } + ], + "next_observation": [ + { + "role": "tool", + "content": " 1\u2192# IDETC Tensegrity Slides \u2014 Draft 1 (extracted content)\n 2\u2192\n 3\u2192Slide-by-slide extraction of the PowerPoint under review: layout, visibility, all on-slide text, shape/media inventory (placeholders included), and presenter notes.\n 4\u2192\n 5\u2192\n 6\u2192## Slide 1 \u2014 layout: Title Only\n 7\u2192\n 8\u2192**On-slide text:**\n 9\u2192- **Title 1**: Let\u2019s build better tensegrity structures faster\n 10\u2192- **TextBox 4**: Marcus Madsen\n 11\u2192Undergraduate researcher under Sterling Baird, Ph.D.\n 12\u2192- **TextBox 6**: IDETC 2026\n 13\u2192\n 14\u2192**Non-text shapes / media / placeholders:**\n 15\u2192- [PICTURE (13)] Picture 3\n 16\u2192- [PICTURE (13)] Picture 8\n 17\u2192\n 18\u2192**Presenter notes:**\n 19\u2192> Title is a bit of a placeholder. Maybe adjusting it to include/more explicitly state what the presentation is on: i.e. optimizing tensegrity-inspired energy absorbers with real impact data and Bays opt. Probably could use an image in the center or a graphic like in the original slides. Tensegrity structures there as a placeholder mostly.\n 20\u2192> \n 21\u2192> Use this slide to introduce myself and then swing into next slides posing the question of why we might want tensegrity or tensegrity inspired structures at all.\n 22\u2192\n 23\u2192\n 24\u2192## Slide 2 \u2014 layout: Title Only\n 25\u2192\n 26\u2192**On-slide text:**\n 27\u2192- **Title 1**: Tensegrity provides robust, reusable, solutions to issues posed by planetary landings\u2014and to problems closer to home.\n 28\u2192\n 29\u2192**Non-text shapes / media / placeholders:**\n 30\u2192- [MEDIA (16)] Online Media 5\n 31\u2192\n 32\u2192**Presenter notes:**\n 33\u2192> Replace this video with a downloaded and trimmed version of just the fall. \n 34\u2192> \n 35\u2192> Landing a payload on another planet means surviving an impact in conditions where parachutes and retrorockets struggle \u2014 thin atmospheres, rough terrain. NASA's Super Ball Bot concept answers this with a tensegrity structure: the lander\u00a0is\u00a0the energy absorber. And unlike crushable aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the hit\u00a0and get back up\u00a0\u2014 it survives multiple drops and keeps working.\n 36\u2192\n 37\u2192\n 38\u2192## Slide 3 (HIDDEN) \u2014 layout: Title Only\n 39\u2192\n 40\u2192**On-slide text:**\n 41\u2192- **Title 1**: Could use a slide to talk about why tensegrity is useful (i.e. avoiding mechanical ware due to struts isolated in sea of tensile moments)\n 42\u2192- **TextBox 2**: *a gif showing an elbow joint bending back and forth to indicate mechanical grinding friction*\n 43\u2192- **TextBox 4**: *Gif showing tensegrity part being squished to indicate part can stay in elastic region under applied stress*\n 44\u2192\n 45\u2192**Presenter notes:**\n 46\u2192> This might be better explained purely vocally while on the previous slide.\n 47\u2192\n 48\u2192\n 49\u2192## Slide 4 \u2014 layout: Title Only\n 50\u2192\n 51\u2192**On-slide text:**\n 52\u2192- **Title 1**: Currently, tensegrity design is slow and resource intensive.\n 53\u2192- **TextBox 3**: Iteration\n 54\u2192- **TextBox 5**: Building\n 55\u2192- **TextBox 7**: Testing\n 56\u2192\n 57\u2192**Non-text shapes / media / placeholders:**\n 58\u2192- [MEDIA (16)] AdobeStock_207717222\n 59\u2192- [MEDIA (16)] Online Media 2\n 60\u2192- [PICTURE (13)] Picture 14\n 61\u2192- [PICTURE (13)] Picture 16\n 62\u2192- [PICTURE (13)] Picture 18\n 63\u2192\n 64\u2192**Presenter notes:**\n 65\u2192> Change out video with a downloaded and timelapsed version set to play once the slide opens (silent). Maybe only do an image of the loading thing so that it doesn\u2019t become distracting because it\u2019s fun to watch it spin.\n 66\u2192> \n 67\u2192> Use this slide to talk about the current reality and difficulties of optimizing tensegrity structures, how the need is for this process to be significantly faster. Reality: testing is costly in time and resources. Building is costly in time. Iteration can be very costly in time. Quick iteration, building, and testing is very desirable to advance any technology.\n 68\u2192\n 69\u2192\n 70\u2192## Slide 5 (HIDDEN) \u2014 layout: Title Only\n 71\u2192\n 72\u2192**On-slide text:**\n 73\u2192- **Title 1**: Bayesian Optimization makes physical experimentation affordable.\n 74\u2192- **TextBox 14**: Baird et al. Matter (2022)\n 75\u2192\n 76\u2192**Non-text shapes / media / placeholders:**\n 77\u2192- [GROUP (6)] Group 2\n 78\u2192\n 79\u2192**Presenter notes:**\n 80\u2192> Consult Sterling on this slide. I don\u2019t want to rip off of his slides (this is one of his slides); it would be helpful to have his input on what kind of imagery / coverage he foresees being most useful of Bays Opt in this presentation. Maybe it\u2019s well enough known that it\u2019d be simply better to discuss HOW we use Bays Opt and not spend time on WHAT it is.\n 81\u2192\n 82\u2192\n 83\u2192## Slide 6 (HIDDEN) \u2014 layout: Title Only\n 84\u2192\n 85\u2192**On-slide text:**\n 86\u2192- **Title 1**: Model calibration and testing are costly, so we need to be careful about what specimens we choose.\n 87\u2192- **TextBox 2**: *Image over white of two separate T-3 prisms with obviously different characteristics (different size, tendon diameters, etc.)\n 88\u2192\n 89\u2192**Presenter notes:**\n 90\u2192> Personally, I feel like this could simply be stated verbally while talking about the benefits of Bayesian Optimization not needing to map out the entire search space.\n 91\u2192\n 92\u2192\n 93\u2192## Slide 7 \u2014 layout: Title Only\n 94\u2192\n 95\u2192**On-slide text:**\n 96\u2192- **Title 1**: Bayesian optimization makes physical experimentation affordable, even with limited, noisy data.\n 97\u2192- **TextBox 5**: 6 variables\n 98\u2192- **TextBox 7**: 2 types of input data\n 99\u2192- **TextBox 9**: 2 objectives\n100\u2192- **TextBox 17**: *image of blackbox*\n101\u2192\n102\u2192**Non-text shapes / media / placeholders:**\n103\u2192- [LINE (9)] Connector: Curved 11\n104\u2192- [LINE (9)] Connector: Curved 13\n105\u2192- [LINE (9)] Connector: Curved 15\n106\u2192\n107\u2192**Presenter notes:**\n108\u2192> Very rough slide as I\u2019m not entirely sure what graphic or text to put on this, partially because I\u2019m not sure how much I will need to explain about Bayesian Optimization. If we went with something like this\n109\u2192\n110\u2192\n111\u2192## Slide 8 \u2014 layout: Title Only\n112\u2192\n113\u2192**On-slide text:**\n114\u2192- **Title 1**: Multi-material additive manufacturing supplies that data fast \u2014 single-build co-fabrication, with no joining of parts.\n115\u2192\n116\u2192**Non-text shapes / media / placeholders:**\n117\u2192- [MEDIA (16)] TT3_01 Figuring Out TPU Print Parameters (part 1)\n118\u2192- [PICTURE (13)] Picture 4\n119\u2192\n120\u2192**Presenter notes:**\n121\u2192> Notes made in here about the difference between real and these tensegrity inspired structures, including the lack of purely tensioned members.\n122\u2192\n123\u2192\n124\u2192## Slide 9 \u2014 layout: Title Only\n125\u2192\n126\u2192**On-slide text:**\n127\u2192- **Title 1**: We use a drop tower and accelerometers to gather real data on these 3D-printed specimens.\n128\u2192- **TextBox 3**: *placeholder for looped gif of drop test at 60 in*\n129\u2192- **TextBox 5**: *placeholder for slo motion of drop test*\n130\u2192\n131\u2192\n132\u2192## Slide 10 \u2014 layout: Title Only\n133\u2192\n134\u2192**On-slide text:**\n135\u2192- **Title 1**: By connecting these ideas, we significantly accelerate the workflow to optimize tensegrity inspired structures:\n136\u2192- **TextBox 3**: Printing\n137\u2192- **TextBox 5**: Testing\n138\u2192- **TextBox 7**: BO feed\n139\u2192\n140\u2192**Non-text shapes / media / placeholders:**\n141\u2192- [PICTURE (13)] Picture 14\n142\u2192- [PICTURE (13)] Picture 16\n143\u2192- [PICTURE (13)] Picture 18\n144\u2192- [PICTURE (13)] Graphic 8\n145\u2192\n146\u2192**Presenter notes:**\n147\u2192> I think it might be better if we replaced \u201cprinting\u201d with a printed tensegrity image, testing with an image of the drop tower, and BO feed with a blackbox.\n148\u2192\n149\u2192\n150\u2192## Slide 11 (HIDDEN) \u2014 layout: Title Only\n151\u2192\n152\u2192**On-slide text:**\n153\u2192- **Title 1**: Reserved for results\n154\u2192\n155\u2192**Presenter notes:**\n156\u2192> Would be helpful if we could show that this process works in this case.\n157\u2192\n158\u2192\n159\u2192## Slide 12 \u2014 layout: Title Only\n160\u2192\n161\u2192**On-slide text:**\n162\u2192- **Title 1**: We will take this workflow and use it for some of the vast potential applications of tensegrity.\n163\u2192- **TextBox 2**: *image of crutch tip, perhaps from that paper and properly attributed*\n164\u2192- **TextBox 3**: *image to show lattice structure\n165\u2192\n166\u2192\n167\u2192## Slide 13 \u2014 layout: Title Only\n168\u2192\n169\u2192**Non-text shapes / media / placeholders:**\n170\u2192- [PICTURE (13)] Picture 3\n171\u2192\n172\u2192**Presenter notes:**\n173\u2192> Slide for questions at the end\n174\u2192\n175\u2192\n176\u2192## Slide 14 \u2014 layout: Title Slide", + "name": "read", + "tool_call_id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "info": { + "start_ts": 8147.330900226, + "end_ts": 8147.337548875 + } + }, + { + "role": "tool", + "content": " 1\u2192# IDETC Presentation \u2014 Doumont Presentation Structure Template\n 2\u2192\n 3\u2192Transcribed from Jean-luc Doumont's presentation-structure template\n 4\u2192([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\n 5\u2192adapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\n 6\u2192All rights reserved. Can be downloaded from .\n 7\u2192\n 8\u2192> To design your presentation, write down your ideas for each component below.\n 9\u2192> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n 10\u2192\n 11\u2192**Draft 3** \u2014 applies the inline review comments from @sgbaird on PR #84 and the\n 12\u2192priority revisions from the\n 13\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md).\n 14\u2192Summaries of what changed are at the bottom of this file\n 15\u2192([Draft 2 \u2192 3](#changes-from-draft-2), [Draft 1 \u2192 2](#changes-from-draft-1)).\n 16\u2192\n 17\u2192**Scope note (frame everything below with this):** the study presented is the\n 18\u2192optimization of a **T3-prism tensegrity-inspired structure together with FDM\n 19\u2192processing parameters** (e.g., nozzle temperature, print speed). PLA\u2013TPU is a\n 20\u2192**proxy system** \u2014 not flight material \u2014 used to prototype a self-driving-lab\n 21\u2192style, near-autonomous workflow for optimizing tensegrity-inspired structures.\n 22\u2192The planetary lander is *motivation*, not demonstrated readiness. Everything\n 23\u2192beyond the T3 study (lattices, flight materials, crutch tip) is future work.\n 24\u2192\n 25\u2192## Opening\n 26\u2192\n 27\u2192### Attention getter\n 28\u2192\n 29\u2192*A way to lead the audience to the need efficiently*\n 30\u2192\n 31\u2192Tensegrity provides robust, **reusable** solutions to issues posed by planetary\n 32\u2192landings \u2014 and to problems closer to home.\n 33\u2192\n 34\u2192One or two sentences of setup, then let the Super Ball Bot image (or a short\n 35\u2192clip) do the work:\n 36\u2192\n 37\u2192> Landing a payload on another planet means surviving an impact in conditions\n 38\u2192> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n 39\u2192> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n 40\u2192> structure: the lander *is* the energy absorber. And unlike crushable\n 41\u2192> aluminum honeycomb or sacrificial airbags, a tensegrity lander can take the\n 42\u2192> hit **and get back up** \u2014 it survives multiple drops and keeps working.\n 43\u2192\n 44\u2192Candidate Super Ball Bot videos to show (verified links):\n 45\u2192\n 46\u2192- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n 47\u2192 (official channel); the canonical overview.\n 48\u2192- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n 49\u2192 \u2014 Wall Street Journal; prototype drop footage.\n 50\u2192- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n 51\u2192 \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n 52\u2192\n 53\u2192Immediately after the hook, state the present study scope in one sentence\n 54\u2192(per the scope note above): *we study a printed PLA\u2013TPU T3 prism as a proxy\n 55\u2192system for prototyping an autonomous tensegrity-optimization workflow* \u2014 so\n 56\u2192the planetary framing never outruns the demonstrated scope.\n 57\u2192\n 58\u2192\n 60\u2192\n 61\u2192### Need\n 62\u2192\n 63\u2192*A difference between actual and desired situations*\n 64\u2192\n 65\u2192**Actual situation** (a two-sided pincer \u2014 stated as a scoped engineering\n 66\u2192judgment, not an absolute):\n 67\u2192\n 68\u2192- For these structures, obtaining trustworthy objective values from a\n 69\u2192 validated high-fidelity model would require substantial calibration of\n 70\u2192 interface, rate, contact, and defect behavior \u2014 and even heavy-duty\n 71\u2192 simulation falls short of the experiments we are running: peak transmitted\n 72\u2192 shock is a millisecond-scale transient that is notoriously difficult to\n 73\u2192 resolve numerically. This holds even for idealized cable\u2013strut tensegrities\n 74\u2192 in their actual engineering materials (steel or similar), not just for\n 75\u2192 printed PLA\u2013TPU.\n 76\u2192 \n 79\u2192- Exhaustive physical search fails on cost: a traditional design of\n 80\u2192 experiments over this relatively high-dimensional space (geometry + FDM\n 81\u2192 process parameters) would demand hundreds of specimens, each costing print\n 82\u2192 and test time.\n 83\u2192- Assembly does not scale: a single T3 prism can be assembled by hand, but\n 84\u2192 more complex structures \u2014 especially tensegrity *lattices* \u2014 become\n 85\u2192 extremely difficult and ultimately infeasible to hand-build.\n 86\u2192\n 87\u2192**Desired situation:**\n 88\u2192\n 89\u2192- Trustworthy performance data without hand assembly (easy creation of\n 90\u2192 tensegrity structures, scalable beyond a single prism).\n 91\u2192- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n 92\u2192 not the hundreds a traditional design of experiments would require.\n 93\u2192\n 94\u2192### Task\n 95\u2192\n 96\u2192*What I decided/was asked to do to address the need*\n 97\u2192\n 98\u2192Build a closed-loop design\u2013print\u2013test workflow that optimizes a **T3-prism\n 99\u2192tensegrity-inspired energy absorber together with its FDM processing\n100\u2192parameters** (e.g., temperature, print speed) directly from real, measured\n101\u2192impact data \u2014 maximizing energy absorbed per gram (specific energy\n102\u2192absorption) and compaction efficiency, subject to a cap on the peak force\n103\u2192transmitted to the payload. Extensions beyond the T3 prism are future work.\n104\u2192\n105\u2192### Main message\n106\u2192\n107\u2192*The one sentence I want my audience to remember*\n108\u2192\n109\u2192By closing the loop between multi-material 3D printing and Bayesian\n110\u2192optimization, we can optimize tensegrity-inspired energy absorbers directly\n111\u2192from real impact data \u2014 in dozens of prints, not the hundreds a traditional\n112\u2192design of experiments would demand.\n113\u2192\n114\u2192\n118\u2192\n119\u2192### Preview\n120\u2192\n121\u2192*A map of the body (ideally three points, max. five)*\n122\u2192\n123\u21921. When model calibration and exhaustive testing are both costly, each\n124\u2192 physical specimen must be chosen for its information value \u2014 so the\n125\u2192 measured experiment is our source of truth.\n126\u21922. Bayesian optimization makes physical experimentation affordable: it handles\n127\u2192 noisy measurements, small batches, and the constrained multi-objective\n128\u2192 trade-off \u2014 and Honegumi let us scaffold it in minutes.\n129\u21923. Multi-material additive manufacturing closes the loop: single-build PLA\u2013TPU\n130\u2192 co-fabrication turns each proposed design into test data in hours, with no\n131\u2192 joining of parts.\n132\u21924. *(Optional fourth point, if time allows)* The same closed loop points at\n133\u2192 what comes next: tensegrity lattices, flight-relevant materials, and\n134\u2192 safety hardware on Earth.\n135\u2192\n136\u2192## Body\n137\u2192\n138\u2192### Point 1\n139\u2192\n140\u2192When model calibration and exhaustive testing are both costly, each physical\n141\u2192specimen must be chosen for its information value.\n142\u2192\n143\u2192- Scoped, not absolute (do **not** say \"simulation can't be trusted\"): for\n144\u2192 these structures, a sufficiently calibrated multiphysics model is expensive\n145\u2192 to build relative to the design campaign itself \u2014 and the hardest quantity,\n146\u2192 peak transmitted shock, is a millisecond-scale transient that even\n147\u2192 heavy-duty simulation struggles to resolve. The gap persists in the actual\n148\u2192 engineering materials a real lander would use (steel struts and cables),\n149\u2192 so this is not merely an artifact of printed PLA\u2013TPU.\n150\u2192- The alternative \u2014 sweeping the design space physically \u2014 fails on cost: a\n151\u2192 traditional design of experiments over geometry plus FDM process parameters\n152\u2192 would take hundreds of specimens, each needing print + test time.\n153\u2192- Therefore: treat the physical experiment as the authoritative source of\n154\u2192 objective values, and be ruthless about *which* experiments to run.\n155\u2192- Anticipate the FEA-veteran pushback (\"inconvenient \u2260 untrustworthy\"): frame\n156\u2192 it as a cost-and-dependence decision, and keep a backup slide with the\n157\u2192 literature (or our own validation) on simulation\u2013experiment discrepancy for\n158\u2192 impact transients.\n159\u2192\n160\u2192### Transition\n161\u2192\n162\u2192If every data point must be a real experiment, we need a method that extracts\n163\u2192the most from every specimen \u2014 we need to make the process radically more\n164\u2192sample-efficient.\n165\u2192\n166\u2192### Point 2\n167\u2192\n168\u2192Bayesian optimization makes physical experimentation affordable, even with\n169\u2192limited, noisy data.\n170\u2192\n171\u2192- Two-level explanation (per mock-audience feedback): one plain sentence + one\n172\u2192 visual in the main talk; implementation detail goes to backup slides.\n173\u2192 Spoken version:\n174\u2192 > \"After each batch, the model estimates both expected performance and\n175\u2192 > uncertainty across the design space. The acquisition rule selects a small\n176\u2192 > next batch that is most likely to expand the feasible trade-off frontier,\n177\u2192 > while accounting for noisy tests and print failures.\"\n178\u2192- Noisy physical measurements: TPU batch-to-batch variation and print defects\n179\u2192 motivate a noise-aware acquisition function (qNEHVI) rather than\n180\u2192 non-noise-aware alternatives. (Backup slide: how observation noise is\n181\u2192 actually modeled \u2014 replicates, noise identification \u2014 and budget-matched\n182\u2192 baselines such as Sobol/random sampling.)\n183\u2192- Small parallel batches: the surrogate proposes the next batch of specimens\n184\u2192 to print, balancing exploration and exploitation.\n185\u2192- Constrained multi-objective by construction: **objectives** = maximize\n186\u2192 specific energy absorption and compaction efficiency; **constraint** = peak\n187\u2192 transmitted force must stay under a cap; the deliverable is the *feasible*\n188\u2192 Pareto front between the two objectives among force-cap-satisfying designs.\n189\u2192 (Keep this formulation consistent everywhere \u2014 do not present \"SEA vs. peak\n190\u2192 force\" as the trade-off.)\n191\u2192- **Honegumi**: we scaffolded and adapted the Bayesian-optimization script for\n192\u2192 this advanced task (noisy, batched, constrained, multi-objective) in\n193\u2192 minutes using [Honegumi](https://honegumi.readthedocs.io/) \u2014 the audience\n194\u2192 can do the same for their own problems; show the code-template picker\n195\u2192 briefly so they see how low the barrier is.\n196\u2192\n197\u2192### Transition\n198\u2192\n199\u2192The optimizer is no longer the bottleneck \u2014 the limit is now providing real\n200\u2192data quickly enough.\n201\u2192\n202\u2192### Point 3\n203\u2192\n204\u2192Multi-material additive manufacturing supplies that data fast \u2014 single-build\n205\u2192co-fabrication, with no joining of parts.\n206\u2192\n207\u2192- Single-build PLA\u2013TPU co-fabrication: rigid struts and flexible tension\n208\u2192 network in one print \u2014 no joining or hand assembly of members, which closes\n209\u2192 the \"easy creation\" gap from the Need and is what makes future lattices\n210\u2192 feasible at all. (Be precise: if support removal, trimming, or conditioning\n211\u2192 remain, say \"no joining of PLA and TPU members\" rather than \"zero\n212\u2192 assembly.\")\n213\u2192- Show the mechanism, not just the word \"tensegrity-inspired\": a labeled\n214\u2192 as-printed specimen (rigid PLA struts, continuous TPU tension network,\n215\u2192 pre-tension status) plus a short deformation sequence \u2014 so the EDL engineer\n216\u2192 doesn't file it as a \"crush structure\" and the grad student can tell it from\n217\u2192 an ordinary lattice.\n218\u2192- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n219\u2192 tested specimen (quasi-static compression + instrumented drop-weight impact)\n220\u2192 in hours. (Backup slide: representative print-to-result time breakdown and\n221\u2192 which steps remain manual \u2014 candidate selection is automated; slicing,\n222\u2192 handling, and testing are not yet.)\n223\u2192- Now we have the data we need to complete the loop.\n224\u2192\n225\u2192### Point 4 (optional \u2014 future applications)\n226\u2192\n227\u2192*Include if timing allows; otherwise fold into the Conclusion.*\n228\u2192\n229\u2192The same closed loop transfers wherever tests are authoritative, each test is\n230\u2192costly, the design space is mixed, and several performance goals conflict:\n231\u2192\n232\u2192- Tensegrity *lattices* and more complex architectures \u2014 exactly where hand\n233\u2192 assembly becomes infeasible and single-build co-fabrication pays off.\n234\u2192- Migration from the PLA\u2013TPU proxy toward flight-relevant materials and\n235\u2192 impact regimes.\n236\u2192- Safety hardware on Earth (e.g., the tensegrity crutch tip \u2014 future work).\n237\u2192- Toward a self-driving lab: closing the remaining manual gaps (slicing,\n238\u2192 handling, testing) in the loop.\n239\u2192\n240\u2192### Evidence / results\n241\u2192\n242\u2192*What we found \u2014 ground the conclusion in shown data*\n243\u2192\n244\u2192Build the talk around a quantitative three-slide sequence (per the\n245\u2192mock-audience report; insert observed values once the campaign completes):\n246\u2192\n247\u21921. **Campaign ledger** \u2014 \"The campaign used N specimens across B adaptive\n248\u2192 batches under a fixed physical-test budget\": initialization vs. adaptive\n249\u2192 batches, replicates, print failures, cycle time, and the numerical value\n250\u2192 (and rationale) of the force cap.\n251\u21922. **Budget-matched baseline** \u2014 \"Adaptive selection improved the feasible\n252\u2192 design set faster than [predeclared baseline]\": performance vs. number of\n253\u2192 physical tests against Sobol/random sampling at the same budget, with\n254\u2192 uncertainty where available.\n255\u21923. **Measured feasible Pareto front** \u2014 specific energy absorption vs.\n256\u2192 compaction efficiency among designs satisfying the force cap, with\n257\u2192 replicate spread, marked infeasible designs, and specimen photos or\n258\u2192 force\u2013time traces for representative Pareto designs.\n259\u2192\n260\u2192Supporting raw-data candidates: drop-test force\u2013time traces, Sobol /\n261\u2192first-batch results (PR #35 / PR #67), printing and impact-test footage.\n262\u2192\n263\u2192### Suggested 15-minute allocation\n264\u2192\n265\u2192*(From the mock-audience report \u2014 results get the largest block.)*\n266\u2192\n267\u2192| Time | Content |\n268\u2192|---|---|\n269\u2192| 1.5 min | Super Ball Bot hook + specimen + present study scope |\n270\u2192| 2 min | Why exhaustive physical search and high-fidelity calibration are both costly |\n271\u2192| 2.5 min | One visual of the closed loop + the one-sentence BO explanation |\n272\u2192| 2 min | Design variables, objectives, force constraint, failures, noise controls |\n273\u2192| 5 min | Measured results: ledger, baseline, Pareto front, specimens, failure modes |\n274\u2192| 1 min | Limitations and transfer conditions |\n275\u2192| 1 min | Measured conclusion + bookend |\n276\u2192\n277\u2192Move kernel choices, encodings, and other implementation detail to backup\n278\u2192slides. For Q&A preparation, use the 17-row claims-vs-evidence table in the\n279\u2192[mock-audience report](edison-mock-audience/mock-audience-feedback.md#claims-needing-evidence-hedging-or-backup-slides).\n280\u2192\n281\u2192## Closing\n282\u2192\n283\u2192### Review\n284\u2192\n285\u2192*A recap of the body, leading into the conclusion*\n286\u2192\n287\u2192Using real, measured impact data from additively manufactured\n288\u2192tensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\n289\u2192optimization routine \u2014 we rapidly iterate toward better energy absorbers.\n290\u2192This feedback loop makes tensegrity optimization dramatically faster: dozens\n291\u2192of prints instead of the hundreds a traditional design of experiments over\n292\u2192this design space would require.\n293\u2192\n294\u2192### Conclusion\n295\u2192\n296\u2192*What the above means to the audience in the end*\n297\u2192\n298\u2192Because the loop runs on physical measurements, it is useful precisely where\n299\u2192model calibration is not worth its cost \u2014 and the same closed-loop pattern\n300\u2192should transfer (a conditional claim, pending evidence) to other additively\n301\u2192manufactured architectures whose performance is dominated by\n302\u2192hard-to-simulate effects. Give the audience the deployment rule explicitly:\n303\u2192*use this loop when tests are authoritative, each test is costly, the design\n304\u2192space is mixed, and several performance goals conflict.* For tensegrity\n305\u2192specifically, it opens a path from this T3 proxy study toward lattices,\n306\u2192flight-relevant materials, payload protection on other planets, and safety\n307\u2192hardware here on Earth (e.g., the crutch tip, as future work).\n308\u2192\n309\u2192### Close\n310\u2192\n311\u2192*A way to end the presentation clearly and elegantly*\n312\u2192\n313\u2192Return to the opening image: the next planetary lander's energy absorber \u2014 a\n314\u2192structure that has to survive not one impact but many \u2014 doesn't have to take\n315\u2192years of hand-built prototypes; with this loop, candidate designs can be\n316\u2192designed, printed, and tested against real impacts in weeks instead of months\n317\u2192or longer. End on the measured engineering result and its transfer condition,\n318\u2192then the bookend: every method that compresses design time moves our ventures\n319\u2192\u2014 on this planet and beyond \u2014 forward.\n320\u2192\n321\u2192---\n322\u2192\n323\u2192## Changes from Draft 2\n324\u2192\n325\u2192Applied from @sgbaird's inline review comments on PR #84 and the\n326\u2192[Edison mock-audience report](edison-mock-audience/mock-audience-feedback.md):\n327\u2192\n328\u2192**From the inline review comments:**\n329\u2192\n330\u21921. **Reusability in the hook** (line 22): the tensegrity lander survives\n331\u2192 multiple drops, unlike crushable/sacrificial alternatives.\n332\u21922. **Proxy-system framing** (lines 49, 87): PLA\u2013TPU would never fly; it is a\n333\u2192 proxy for prototyping a self-driving-lab-style workflow \u2014 stated in a\n334\u2192 scope note up front and re-stated right after the hook.\n335\u21923. **\"Expensive\" de-emphasized** (line 52): the argument is now that even\n336\u2192 heavy-duty simulation falls short of the experiments (millisecond-scale\n337\u2192 peak shock), not that it merely costs too much.\n338\u21924. **Assembly-scaling argument** (line 60): one T3 prism is hand-assemblable;\n339\u2192 complex structures and especially lattices are not.\n340\u21925. **Task scoped** (line 70): T3 structure + FDM processing parameters\n341\u2192 (temperature, print speed, \u2026) only; the rest is explicitly future work.\n342\u21926. **Simulation-gap justification flagged** (line 87): TODO for an Edison\n343\u2192 literature search (or validation of experiments against simulation), plus\n344\u2192 the millisecond-resolution peak-shock argument.\n345\u21927. **Optional Point 4 added** (line 97): future applications, with a note it\n346\u2192 can fold into the Conclusion if time is short.\n347\u21928. **Steel-materials point** (line 104): simulations fall short for the\n348\u2192 actual engineering materials (steel struts/cables), not just printed\n349\u2192 PLA\u2013TPU.\n350\u21929. **Honegumi featured** (line 130): how it scaffolded the advanced BO script\n351\u2192 in minutes, pitched so the audience sees they can use it too.\n352\u219210. **\"Thousands\" \u2192 \"hundreds\"** (line 173): baseline is now a traditional\n353\u2192 design of experiments over a high-dimensional space (hundreds), in the\n354\u2192 main message, Need, Point 1, and Review.\n355\u219211. **\"Weeks not years\" softened** (line 191): now \"weeks instead of months\n356\u2192 or longer\" for candidate designs.\n357\u2192\n358\u2192**From the mock-audience report's priority revisions:**\n359\u2192\n360\u219212. **Quantitative three-slide results sequence** (Priority 1): campaign\n361\u2192 ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front, with a\n362\u2192 fallback main-message wording if the campaign can't yet support the\n363\u2192 efficiency claim.\n364\u219213. **Anti-simulation premise replaced with a scoped cost decision**\n365\u2192 (Priority 2): Point 1 retitled to the information-value message;\n366\u2192 \"simulation can't be trusted\" removed everywhere; FEA-veteran pushback\n367\u2192 anticipated with a backup slide.\n368\u219214. **15-minute allocation table added** (Priority 3): 5 minutes on results;\n369\u2192 implementation detail moved to backup slides; Q&A prep pointed at the\n370\u2192 report's claims-vs-evidence table.\n371\u219215. **Two-level BO explanation** (theme 3): plain spoken sentence + visual in\n372\u2192 the main talk, machinery in backup slides; the misleading\n373\u2192 \"qNEHVI over standard expected improvement\" comparison replaced with\n374\u2192 \"noise-aware vs. non-noise-aware\" plus proper budget-matched baselines.\n375\u219216. **Tensegrity mechanism visual** (theme 4): labeled specimen + deformation\n376\u2192 sequence in Point 3, so the structure isn't remembered as a generic crush\n377\u2192 structure or lattice.\n378\u219217. **\"Eliminates assembly entirely\" hedged** (theme + P3): now \"single-build\n379\u2192 co-fabrication / no joining of parts,\" with a note to list remaining\n380\u2192 manual steps and an automation-status caveat.\n381\u219218. **Objective formulation made consistent** (theme 7): objectives = SEA +\n382\u2192 compaction efficiency, constraint = force cap, deliverable = *feasible*\n383\u2192 Pareto front; the \"SEA vs. peak force\" evidence bullet corrected.\n384\u219219. **Closing made concrete** (theme 8 + P6): explicit deployment rule, the\n385\u2192 transfer claim marked conditional, and the close ends on the measured\n386\u2192 result before the bookend.\n387\u2192\n388\u2192## Changes from Draft 1\n389\u2192\n390\u2192Feedback from the PR #84 review, applied in Draft 2:\n391\u2192\n392\u21921. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n393\u2192 dozens-not-thousands claim); body and message now carry the same story.\n394\u21922. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n395\u2192 manuscript's positioning (#75/#76) is that the physical experiment is the\n396\u2192 source of truth, explicitly avoiding calibrated FE simulation.\n397\u21923. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n398\u2192 parametric design space and avoid an over-claim to a design-automation\n399\u2192 audience.\n400\u21924. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n401\u2192 energy absorption and compaction efficiency, cap peak transmitted force.\n402\u21925. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n403\u2192 *and* the space is too big to brute-force \u2014 which makes BO + AM the\n404\u2192 inevitable answer. *(Draft 3 re-scopes the first side of this pincer; see\n405\u2192 above.)*\n406\u21926. **Point 1 given its own content** (the *why* behind the inefficiency)\n407\u2192 instead of restating the Need.\n408\u21927. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n409\u2192 the specifics (noise-aware acquisition, small batches, multi-objective).\n410\u21928. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n411\u2192 (monolithic printing = no assembly). *(Draft 3 hedges the wording; see\n412\u2192 above.)*\n413\u21929. **Evidence/results slot added** before the Closing, so the Conclusion is\n414\u2192 grounded in shown data rather than three hedged \"may\"s.\n415\u219210. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n416\u2192 Ball Bot visual; verified video links included.\n417\u219211. **Close bookends the opening** (the next lander's absorber, designed in\n418\u2192 weeks) before the broader humankind line.\n419\u219212. **Spelling**: Doumont (was \"Duomont\").", + "name": "read", + "tool_call_id": "call_93haurOMDGiH2mQFFbaMaYKn", + "info": { + "start_ts": 8147.331065886, + "end_ts": 8147.336467845 + } + }, + { + "role": "tool", + "content": " 1\u2192# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n 2\u2192\n 3\u2192Video: (assigned in #83)\n 4\u2192\n 5\u2192Transcribed from @me-madsen's handwritten notes\n 6\u2192([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\n 7\u2192dated 2026-07-15).\n 8\u2192\n 9\u2192## Overview\n10\u2192\n11\u2192A presentation is all about the right content, displayed the right way, at the\n12\u2192right time.\n13\u2192\n14\u2192- Verbal communication is sequential.\n15\u2192- Message is *interpretation* of the information.\n16\u2192 - Ensure the audience can get the most out of what you're doing.\n17\u2192 - Don't just give them information \u2014 help them understand it, interpret it,\n18\u2192 and internalize it.\n19\u2192\n20\u2192## Doumont's suggested rules for a presentation\n21\u2192\n22\u21920. Ensure you have **messages** for your audience, not just words.\n23\u21921. **Adapt to your audience.**\n24\u2192 - Think like the audience, not the speaker.\n25\u21922. **Maximize signal-to-noise ratio.**\n26\u2192 - Reduce noise (animations, laser pointers, anything that pulls attention\n27\u2192 away from your message).\n28\u21923. **Use effective redundancy.**\n29\u2192 - Tell your message in word *and* on slide: someone deaf should understand\n30\u2192 your messages just as well as someone blind.\n31\u2192 - Ineffective redundancy is clutter appearing on every slide (e.g., the\n32\u2192 date).\n33\u2192\n34\u2192## When creating slides\n35\u2192\n36\u2192- If the audience doesn't need it, why put it on the slides?\n37\u2192- People shouldn't have to count the items on a slide to know how many are\n38\u2192 there.\n39\u2192- Don't use your slides as your \"presentation notes.\"\n40\u2192 - Don't show your notes to the audience.\n41\u2192 - **A bad slide is worse than no slide at all.**\n42\u2192- **One message per slide** is a good rule of thumb.\n43\u2192 - Prioritize the *so what* (or *therefore what*) of your message.\n44\u2192- Make it easy to interpret the information on the slide:\n45\u2192 - 3\u20135 items at most.\n46\u2192 - Align the title left.\n47\u2192 - Do not clutter slides with repetitive or unnecessary information: logos,\n48\u2192 affiliation, date, slide numbers.\n49\u2192- Doumont's suggested workflow:\n50\u2192 1. **Start with the title.**\n51\u2192 - Use a full sentence in the title.\n52\u2192 - The title is the main message of the slide.\n53\u2192 - Make it easy to read: left-aligned, with intentional line breaks.\n54\u2192 2. **Do the visual next.** Optimize for viewing / taking in the message.\n55\u2192- Try printing the slideshow as a handout, 6 per page, to test visibility.\n56\u2192\n57\u2192## General preparation\n58\u2192\n59\u2192- Planning for audience, content, and structure matters more than materials\n60\u2192 (i.e., slides).\n61\u2192- Try using one sheet of paper for presentation preparation.\n62\u2192- It's most important that your presentation is:\n63\u2192 - well planned, structured, and delivered;\n64\u2192 - **if you don't have a lot of time to prepare, don't focus on making\n65\u2192 slides.**\n66\u2192- Practice the presentation a few times. Practice at least once *without* your\n67\u2192 slideshow!\n68\u2192- If doing a handout, keep it separate from the slides you're preparing.\n69\u2192- Impress the audience with your body.\n70\u2192 - Use gestures, not laser pointers.\n71\u2192 - \"Control\" the audience with your body \u2014 keep their attention on your\n72\u2192 presentation.\n73\u2192\n74\u2192## Impressions / what to apply to the IDETC presentation\n75\u2192\n76\u2192\n77\u2192\n78\u2192## Questions / things to discuss\n79\u2192\n80\u2192", + "name": "read", + "tool_call_id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "info": { + "start_ts": 8147.331133416, + "end_ts": 8147.338304015 + } + }, + { + "role": "tool", + "content": " 1\u2192\\documentclass[10pt]{article}\n 2\u2192\n 3\u2192% --- Packages ---\n 4\u2192\\usepackage[letterpaper,margin=0.6in]{geometry}\n 5\u2192\\usepackage[hidelinks]{hyperref}\n 6\u2192\\usepackage{parskip}\n 7\u2192\\usepackage{graphicx}\n 8\u2192\\usepackage{caption}\n 9\u2192\\usepackage{titlesec}\n 10\u2192\\usepackage{wrapfig}\n 11\u2192\\usepackage[numbers,sort&compress]{natbib}\n 12\u2192\n 13\u2192% --- Formatting ---\n 14\u2192\\setlength{\\parskip}{0.2em}\n 15\u2192\\setlength{\\parindent}{0em}\n 16\u2192\\titleformat*{\\section}{\\large\\bfseries}\n 17\u2192\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n 18\u2192\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n 19\u2192\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n 20\u2192\\setlength{\\emergencystretch}{2em}\n 21\u2192\\setlength{\\intextsep}{0.2em}\n 22\u2192\\bibliographystyle{abbrvnat}\n 23\u2192\n 24\u2192\\begin{document}\n 25\u2192\n 26\u2192\\begin{center}\n 27\u2192{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n 28\u21923D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n 29\u2192\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n 30\u2192\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n 31\u2192\\end{center}\n 32\u2192\n 33\u2192\\vspace{0.4em}\n 34\u2192\n 35\u2192\\section*{Abstract}\n 36\u2192Tensegrity-inspired architectures---rigid struts suspended within a\n 37\u2192continuous flexible network---can exhibit tunable nonlinear\n 38\u2192force--displacement responses and favorable energy absorption per unit\n 39\u2192mass, motivating their use in lightweight energy-absorbing and protective\n 40\u2192structures\n 41\u2192\\citep{skelton2009tensegrity,pajunen2019design}.\n 42\u2192Multi-material fused deposition modeling (FDM) can co-print rigid (PLA)\n 43\u2192struts and flexible (TPU) elements in a single build\n 44\u2192\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\n 45\u2192space---strut geometry, tension-element cross-section, connectivity\n 46\u2192topology, and unit-cell tiling---is too large to explore by trial and\n 47\u2192error. We present a closed-loop experimental campaign that uses\n 48\u2192Bayesian optimization (BO) \\citep{shahriari2016taking}\n 49\u2192to drive a design--print--test workflow operating directly on physical\n 50\u2192measurements, without relying on calibrated finite-element simulation\n 51\u2192for objective evaluation.\n 52\u2192\n 53\u2192\\section{Introduction and Motivation}\n 54\u2192\n 55\u2192\\begin{wrapfigure}{r}{0.65\\textwidth}\n 56\u2192\\centering\n 57\u2192\\vspace{-0.4em}\n 58\u2192\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n 59\u2192\\captionsetup{font=small}\n 60\u2192\\caption{Closed-loop, BO-driven design--print--test workflow.\n 61\u2192Candidate tensegrity-inspired unit cells are instantiated from a\n 62\u2192parameter vector, fabricated by multi-material FDM, and tested under\n 63\u2192quasi-static compression and drop-weight impact. Measured peak force,\n 64\u2192specific energy absorption (SEA), and compaction efficiency update a\n 65\u2192Gaussian-process surrogate that proposes the next batch of designs.}\n 66\u2192\\label{fig:overview}\n 67\u2192\\vspace{-0.4em}\n 68\u2192\\end{wrapfigure}\n 69\u2192\n 70\u2192Tensegrity structures combine compression-only members with a\n 71\u2192pre-tensioned tension network to achieve stiffness without rigid\n 72\u2192joinery~\\citep{skelton2009tensegrity}. Their\n 73\u2192nonlinear, often load-limiting force--displacement response makes them\n 74\u2192attractive for impact mitigation, packaging, and wearable protective\n 75\u2192gear~\\citep{pajunen2019design}. Idealized\n 76\u2192cable--strut tensegrities are difficult to manufacture at scale, but\n 77\u2192\\emph{tensegrity-inspired} architectures fabricated by single-material\n 78\u21923D printing reproduce key tensegrity behaviors---including\n 79\u2192post-buckling stability and load-limiting force--displacement\n 80\u2192response~\\citep{pajunen2019design}---motivating our extension to\n 81\u2192multi-material FDM for monolithic co-fabrication of rigid and flexible\n 82\u2192elements. PLA provides rigid struts; TPU provides flexible,\n 83\u2192rate-dependent tension elements whose viscoelasticity is expected to\n 84\u2192contribute hysteretic energy dissipation under impact loading,\n 85\u2192consistent with the energy-absorbing behavior reported for rigid--TPU\n 86\u2192multi-material prints~\\citep{khatri2024energy}.\n 87\u2192\n 88\u2192The challenge is that small geometric and topological changes can\n 89\u2192produce large changes in the measured response, and high-fidelity\n 90\u2192simulation of FDM specimens is computationally expensive and often\n 91\u2192struggles to capture physical defects such as interfacial slip and\n 92\u2192TPU rate-dependence~\\citep{mo2023accelerated}. Pure\n 93\u2192trial-and-error sweeps over the joint design space are also infeasible:\n 94\u2192even modest parameter resolutions yield thousands of candidates, each\n 95\u2192requiring print and test time. We instead treat the physical\n 96\u2192experiment as the source of truth and use BO to select which specimens\n 97\u2192are most informative to fabricate next---an approach used in related\n 98\u2192closed-loop materials and structures\n 99\u2192campaigns~\\citep{mo2023accelerated,low2024evolution}.\n100\u2192Unlike prior tensegrity-inspired studies that evaluate a fixed design\n101\u2192set~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\n102\u2192designs using parallel, noisy multi-objective BO driven by measured\n103\u2192$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n104\u2192\n105\u2192\\section{Approach}\n106\u2192\\subsection{Closed-Loop Workflow}\n107\u2192Figure~\\ref{fig:overview} summarizes the campaign. Each iteration\n108\u2192proceeds in four steps: (i)~a parameterized unit-cell design is\n109\u2192instantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\n110\u2192is sliced and printed on a multi-material FDM system; (iii)~it is\n111\u2192tested under quasi-static compression and instrumented drop-weight\n112\u2192impact; and (iv)~the resulting performance metrics update a\n113\u2192Gaussian-process (GP) surrogate, which a batch acquisition function\n114\u2192queries to recommend the next set of candidates. Surrogate updating\n115\u2192and experiment selection are automated; specimen handling, slicing,\n116\u2192and test setup remain manual.\n117\u2192\n118\u2192\\subsection{Design Space}\n119\u2192We parameterize a family of unit cells using a core-wrapping\n120\u2192architecture in which rigid PLA struts are enclosed by continuous TPU\n121\u2192skins; the wrapping rationale follows multi-material FDM strategies in\n122\u2192which rigid panels are wrapped by stretchable soft material to prevent\n123\u2192delamination and improve interfacial bonding at the rigid--flexible\n124\u2192interface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\n125\u2192comprises continuous geometric variables (strut diameter and length,\n126\u2192tension-element width and thickness), an integer strut count per unit\n127\u2192cell, and two categorical variables (connectivity topology, unit-cell\n128\u2192tiling pattern). Categorical choices are encoded explicitly rather\n129\u2192than embedded in a continuous metric, and bounds on the continuous\n130\u2192variables are chosen to remain within the printer's resolution and\n131\u2192overhang limits and to keep specimens in a common form factor for the\n132\u2192test fixtures.\n133\u2192\n134\u2192\\subsection{Surrogate and Acquisition}\n135\u2192The GP surrogate is trained on three measured performance metrics:\n136\u2192peak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n137\u2192(SEA), and compaction efficiency $\\eta$. We use independent GPs per\n138\u2192objective with Mat\\'ern kernels; hyperparameters are fit by marginal\n139\u2192likelihood with priors to avoid pathological length scales at small $n$.\n140\u2192Heteroscedastic noise from TPU batch-to-batch variation and FDM\n141\u2192interfacial defects motivates our use of q-noisy expected hypervolume\n142\u2192improvement (qNEHVI), which explicitly accounts for observation noise\n143\u2192in parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\n144\u2192expected-improvement variants. Print failures are treated as a\n145\u2192probabilistic feasibility constraint: the acquisition score for each\n146\u2192candidate is its qNEHVI improvement weighted by the GP-estimated\n147\u2192probability of successful fabrication, following recent constrained\n148\u2192multi-objective practice in self-driving labs~\\citep{low2024evolution}.\n149\u2192Where applicable, we use numerically stable log-space variants of\n150\u2192improvement-based acquisitions to mitigate vanishing-gradient\n151\u2192pathologies as data and constraints\n152\u2192accumulate~\\citep{ament2023logei}.\n153\u2192\n154\u2192\\section{Methods}\n155\u2192\\subsection{Fabrication}\n156\u2192Specimens are fabricated on a multi-material FDM system capable of\n157\u2192co-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\n158\u2192Print parameters (temperature, line width, infill, retraction) are\n159\u2192held fixed within a batch to isolate the effect of the design\n160\u2192variables. Each specimen is weighed and dimensionally inspected prior\n161\u2192to testing so that mass and geometric deviations are available as\n162\u2192covariates.\n163\u2192\n164\u2192\\subsection{Mechanical Testing}\n165\u2192Quasi-static compression is performed on a screw-driven load frame at\n166\u2192a fixed nominal strain rate, with force--displacement recorded to\n167\u2192densification. Drop-weight impact testing uses an instrumented tup at\n168\u2192a fixed impact energy; transmitted force is captured at high sample\n169\u2192rate, and specimens are imaged before and after impact to document\n170\u2192failure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\n171\u2192unit mass, and compaction efficiency $\\eta$, defined consistently\n172\u2192across the campaign. A control specimen from an earlier batch is\n173\u2192retested periodically to monitor TPU batch-to-batch and printer drift.\n174\u2192\n175\u2192\\section{Expected Outcomes and Discussion}\n176\u2192We will report how BO-selected designs evolve across iterations, how\n177\u2192GP surrogate predictions and uncertainties change as data accumulate,\n178\u2192and which Pareto-efficient trade-offs emerge between SEA and peak\n179\u2192transmitted force within the explored design space. We will also\n180\u2192discuss practical lessons for operating the closed-loop workflow,\n181\u2192including handling of print failures, batch-to-batch variability in\n182\u2192TPU response, the influence of the rigid--flexible interface on\n183\u2192energy absorption, and the exploration--exploitation balance of the\n184\u2192acquisition function under realistic experimental noise. We expect\n185\u2192the campaign to clarify where physical experimentation is genuinely\n186\u2192required versus where simpler analytical or multi-fidelity surrogate\n187\u2192shortcuts~\\citep{mo2023accelerated} could be\n188\u2192substituted in future work.\n189\u2192\n190\u2192\\section{Conclusion}\n191\u2192This work demonstrates a partially automated, experiment-driven\n192\u2192closed-loop pipeline for designing multi-material 3D-printed\n193\u2192tensegrity-inspired energy absorbers, with surrogate updating and\n194\u2192candidate selection automated and fabrication and testing kept manual.\n195\u2192By updating a GP surrogate directly from physical measurements, the\n196\u2192workflow avoids direct dependence on calibrated finite-element\n197\u2192simulation for objective evaluation while still providing principled,\n198\u2192multi-objective design recommendations. The same closed-loop pattern is\n199\u2192expected to transfer to other multi-material additively manufactured\n200\u2192architectures whose performance is dominated by hard-to-simulate\n201\u2192effects.\n202\u2192\n203\u2192{\\tiny\n204\u2192\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n205\u2192\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n206\u2192% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n207\u2192% keep \\url{} as a clickable hyperlink (default natbib behavior).\n208\u2192\\providecommand{\\doi}[1]{}\n209\u2192\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n210\u2192\\bibliography{references}\n211\u2192}\n212\u2192\n213\u2192\\end{document}", + "name": "read", + "tool_call_id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "info": { + "start_ts": 8147.332633466, + "end_ts": 8147.339043095 + } + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "callback_type": "after_env_step" + }, + "action": { + "call_id": { + "run_id": "d134fa21-46a1-4b97-b793-63625703b95d", + "fwd_id": "1dbeb877-a294-4690-8008-386a66c012b6" + }, + "op_name": "IdentityOp-4f030d6055f0", + "op_class_name": "ldp.graph.common_ops.IdentityOp", + "value": { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ] + }, + "logprob": null + } + } + }, + "created_at": "2026-07-26T02:29:20.212872Z" + }, + { + "id": "9e1eb93b-96dd-414b-b195-f2896a03005f", + "trajectory_id": "afade0b1-f49e-4031-a86d-cb5ec4652536", + "trajectory_timestep": 0, + "step": "after_agent_get_asv", + "state": { + "transition": { + "timestep": 0, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + } + ], + "next_observation": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "callback_type": "after_agent_get_asv" + }, + "action": { + "call_id": { + "run_id": "d134fa21-46a1-4b97-b793-63625703b95d", + "fwd_id": "1dbeb877-a294-4690-8008-386a66c012b6" + }, + "op_name": "IdentityOp-4f030d6055f0", + "op_class_name": "ldp.graph.common_ops.IdentityOp", + "value": { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_8cnLZZRc1qR5WExVt4ZF4hEJ", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/slide-deck-draft1-extracted.md\"}", + "name": "read" + } + }, + { + "id": "call_93haurOMDGiH2mQFFbaMaYKn", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-presentation-template.md\"}", + "name": "read" + } + }, + { + "id": "call_tYXZgr4B8yjH0YEPyPZih9qI", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/doumont-video-notes.md\"}", + "name": "read" + } + }, + { + "id": "call_dlPcqwDfFi7a24fnE1yNh10b", + "type": "function", + "function": { + "arguments": "{\"file_path\": \"/workspace/idetc-abstract.tex\"}", + "name": "read" + } + } + ] + }, + "logprob": null + } + } + }, + "created_at": "2026-07-26T02:29:14.788991Z" + }, + { + "id": "b52b1f12-bb0b-4fe5-a8e7-fa0933befeac", + "trajectory_id": "afade0b1-f49e-4031-a86d-cb5ec4652536", + "trajectory_timestep": 0, + "step": "before_transition", + "state": { + "transition": { + "timestep": 0, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "pwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "You are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + } + ], + "next_observation": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "pwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "You are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "callback_type": "before_transition" + }, + "action": null + } + }, + "created_at": "2026-07-26T02:29:11.556438Z" + }, + { + "id": "a72dfb7b-fbb2-4faa-9b4e-7bf1de1f5da4", + "trajectory_id": "afade0b1-f49e-4031-a86d-cb5ec4652536", + "trajectory_timestep": 0, + "step": "after_agent_init_state", + "state": { + "transition": { + "timestep": 0, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [], + "next_observation": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "pwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "You are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "callback_type": "after_agent_init_state" + }, + "action": null + } + }, + "created_at": "2026-07-26T02:29:10.681318Z" + }, + { + "id": "ff978c77-626e-4540-a1a1-f552ecac3704", + "trajectory_id": "afade0b1-f49e-4031-a86d-cb5ec4652536", + "trajectory_timestep": 0, + "step": "after_env_reset", + "state": { + "transition": { + "timestep": 0, + "agent_state": null, + "next_agent_state": null, + "observation": [], + "next_observation": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "pwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "You are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "info": null + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "callback_type": "after_env_reset", + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ] + }, + "action": null + } + }, + "created_at": "2026-07-26T02:29:09.769206Z" + } + ], + "environment_frame": { + "id": "ff7bb3fa-3fc9-4e3a-b030-24f7ddc2698e", + "agent_state_point_in_time": null, + "trajectory_timestep": 2, + "current_agent_step": "after_transition", + "trajectory_id": "afade0b1-f49e-4031-a86d-cb5ec4652536", + "state": { + "deepcopy": true, + "state": { + "last_action": "Tool request message '' for tool calls: submit_answer(answer='# Mock audience report: IDETC-CIE 2026 slide deck, Draft 1\n\n## Executive read\n\nThe visible deck has a clear visual spine: **motivation \u2192 slow iteration \u2192 Bayesian optimization \u2192 multi-material printing \u2192 impact testing \u2192 closed loop \u2192 future applications**. The strongest section is slides 8\u201310, where fabrication, testing, and adaptive selection finally appear as one workflow.\n\nBut Draft 1 does not yet deliver the technical case promised by the agreed outline. The visible deck omits four things a DAC-10 audience will need:\n\n1. **Honest scope:** no visible slide says that the PLA\u2013TPU T3 prism is a proxy system rather than flight hardware.\n2. **A precise optimization problem:** the six variables, two objectives, force constraint, noise treatment, and fabrication failures are not intelligible on slide 7.\n3. **Quantitative evidence:** the sole results slot is hidden and empty, yet slides 10 and 12 already claim acceleration and transfer.\n4. **A measured conclusion:** the talk jumps from the workflow to speculative applications without showing a campaign ledger, comparison against a budget-matched baseline, or feasible Pareto front.\n\nThe slide titles generally follow Doumont\u2019s full-sentence-message rule, but several messages outrun the evidence. The deck also departs substantially from the Draft 3 timing plan, which reserves **5 of 15 minutes for results**. As built, results receive zero visible slides.\n\n---\n\n# P0. The program manager in the audience\n\n## (a) My reaction in the room\n\nSlide 1 tells me the team wants to work faster, but it does not tell me what is being optimized or what has actually been built. The title sounds aspirational rather than traceable to a project deliverable. I immediately want the formal title from the abstract or a shorter version of it.\n\nThe Super Ball Bot footage on slide 2 is an effective hook. I understand why reusable impact attenuation matters. But the slide creates a scope risk: I am looking at planetary landing hardware while the actual work is a printed PLA\u2013TPU T3 proxy. Because that distinction is not stated immediately, I may spend the rest of the talk evaluating the work against flight-readiness criteria the project never intended to meet.\n\nSlide 4 gives me the business problem, but not a baseline. \u201cSlow and resource intensive\u201d needs a number: hours per print-test cycle, number of factors, or the candidate count under a declared design of experiments. Without that, I cannot assess whether the proposed loop attacks the dominant schedule driver.\n\nSlide 7 is where I expect the project definition. Instead, \u201c6 variables,\u201d \u201c2 types of input data,\u201d and \u201c2 objectives\u201d read like inventory labels. I cannot reconstruct the statement of work from them. I need to see what is automated, what remains manual, what the force cap is, and what constitutes a successful campaign.\n\nSlides 8 and 9 make the project tangible. A labeled specimen and an actual drop test would build confidence. Yet \u201csingle-build\u201d and \u201cno joining\u201d need a process qualification caveat, and I need the measurement chain to be explicit. The abstract refers to an instrumented tup and force\u2013time data; slide 9 says accelerometers. Those may be compatible, but the deck presently leaves the relationship unclear.\n\nSlide 10 is the right management view of the system, but \u201csignificantly accelerate\u201d is a result claim placed on a workflow diagram. At this point I ask, \u201cCompared with what, by how much, and over how many specimens?\u201d Then the deck skips the hidden results slide and goes directly to future applications. That is the point where my confidence drops sharply. The project is asking me to extrapolate before closing the evidence loop.\n\nSlide 12 is too early and too broad. A crutch tip and lattice can be legitimate next phases, but only after the team shows what the present campaign established and what technical risks remain. The blank questions slide gives me no durable take-home message.\n\n### PM stage-risk register\n\n| Risk | Likelihood | Consequence | Mitigation |\n|---|---:|---:|---|\n| Results are incomplete by conference lock | Medium to high | Critical: central claim becomes unsupported | Set a dated campaign freeze; predeclare a fallback message and minimum publishable result package |\n| Embedded Super Ball Bot or drop-test video fails | Medium | Moderate: weak opening or broken pacing | Use local, trimmed files; retain a still-frame fallback on every video slide |\n| Talk overruns because several videos and explanations are improvised | High | High: results or conclusion gets cut | Script videos to 10\u201315 seconds total each; rehearse to 13:30; assign 5 minutes to results |\n| Scope is mistaken for flight-hardware validation | High | High: credibility damage in Q&A | Put \u201cPLA\u2013TPU T3 proxy; not flight hardware\u201d on slide 2 or the next slide |\n| Acceleration claim lacks a budget-matched comparator | High | Critical for DAC review audience | Show Sobol/random or another predeclared baseline at the same physical-test budget |\n| Measurement chain is challenged | Medium | High | Add a methods schematic and backup slide on calibration, sampling, filtering, and metric extraction |\n| \u201cNo joining\u201d or \u201cnear autonomous\u201d is read as full automation | Medium | Moderate | List manual steps explicitly: slicing, handling, setup, and testing |\n| Animations distract or fail to communicate without narration | Medium | Moderate | Prefer one short clip plus static annotated frames; follow Doumont\u2019s signal-to-noise rule |\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey are building a loop that prints PLA\u2013TPU tensegrity-like absorbers, drop-tests them, and lets Bayesian optimization choose the next print.\u201d\n\nThat retains the basic workflow but loses the intended **dozens rather than hundreds** claim because the deck never demonstrates it. I would also probably describe the work as \u201clander research,\u201d which is a distortion caused by the missing proxy-system statement.\n\n## (c) My top three Q&A questions\n\n1. What is the dated plan to complete the campaign, baseline comparison, and repeat testing before the conference slide freeze?\n2. Which exact operations are automated today, which remain manual, and what is the measured end-to-end cycle time per adaptive batch?\n3. What evidence will justify \u201cdozens rather than hundreds,\u201d and what will you claim if the budget-matched baseline does not show an advantage?\n\n## (d) My most pointed objection\n\n**You are presenting a claimed acceleration without showing a completed campaign, a comparator, or even a visible results slide.**\n\nUnanswered, this is **critical**. I would not fund a scale-up phase from this deck. I might fund completion of the present campaign if the team showed a disciplined schedule, predefined success criteria, and a credible fallback.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 11.** It must become a real, scheduled results package, preferably three slides: campaign ledger, budget-matched baseline, and feasible Pareto front.\n- **Hidden-slide decision:** **Unhide slide 11**, but not in its current empty form. The talk cannot make its principal claim without it.\n\n### PM slide-level punch list, ordered by schedule criticality\n\n**Must be fixed first**\n\n1. **Slide 11/results sequence:** assign owners and dates for data freeze, quality control, baseline computation, replicate analysis, and final plots. Define fallback wording now.\n2. **Slide 2:** add the explicit scope line, \u201cThis study uses a printed PLA\u2013TPU T3 prism as a proxy for developing the workflow; it is not flight hardware.\u201d\n3. **Slide 7:** replace category counts with the actual optimization problem, test budget, batch size, objectives, constraint, and noise/failure treatment.\n4. **Slide 10:** remove \u201csignificantly\u201d until slide 11 demonstrates a quantitative improvement; show automation boundaries and the loop\u2019s measured cycle time.\n5. **Slides 2 and 9:** download and trim media, test on the conference laptop, and put a static fallback image on each slide.\n\n**Then fix**\n\n6. **Slide 4:** quantify the current cost or candidate count and identify the bottleneck.\n7. **Slide 8:** label PLA, TPU, interface, and any post-processing; qualify \u201cno joining.\u201d\n8. **Slide 9:** reconcile accelerometers with the instrumented-tup description and show how force, energy absorption, and compaction efficiency are obtained.\n9. **Slide 12:** convert the application collage into a conditional next-phase roadmap with gates.\n10. **Slide 13:** end on the measured result and deployment rule, not a blank Q&A screen.\n\n**Nice to have**\n\n11. Replace decorative or looping media on slide 4 with one static process image unless it communicates a measured delay.\n12. Add backup slides on Bayesian optimization implementation, finite-element-analysis positioning, repeatability, force-cap rationale, and print failures.\n\n---\n\n# P1. The skeptical Bayesian-optimization insider\n\n## (a) My reaction in the room\n\nThe opening is competent but generic for a design-automation conference. Slides 1\u20134 set up an expensive physical experiment, which is a valid application class for Bayesian optimization (BO). I become interested when the presenter reaches slide 7, because that is where methodological seriousness should appear.\n\nInstead, slide 7 says only \u201c6 variables,\u201d \u201c2 types of input data,\u201d and \u201c2 objectives.\u201d I do not learn the variables, their types, how categorical choices are encoded, whether process parameters are optimized, how noisy observations enter the Gaussian processes, how replicates identify noise, how failures are modeled, or how the force cap enters the acquisition. The title says BO handles noisy data, but the visual does not substantiate that message.\n\nThere is also an unresolved specification conflict across the supplied materials. The agreed outline says the study jointly optimizes T3 geometry and FDM processing parameters. The abstract\u2019s design vector lists four continuous geometric variables, one integer, and two categorical variables, apparently **seven variables**, while its fabrication section says print parameters are held fixed within a batch. The slide says **six variables**. A DAC audience will notice if the spoken details do not reconcile these versions.\n\nSlides 8\u201310 explain why physical feedback is useful, but they do not establish why qNEHVI is the right algorithm or whether adaptive selection helped. The visible deck never names qNEHVI, the feasible Pareto front, the peak-force constraint, batch size, initialization, or test budget. Hiding slide 5 is reasonable if it is only a generic \u201cwhat is BO?\u201d slide copied from another talk. But slide 7 must then carry the application-specific methodological content.\n\nWhen slide 10 says the workflow \u201csignificantly accelerate[s]\u201d optimization, I wait for a performance-versus-budget curve. It never arrives. Slide 12 then generalizes to other applications. My default conclusion is exactly the skeptical one posed in the prompt: this appears to be off-the-shelf qNEHVI attached to an interesting physical apparatus, with no demonstrated algorithmic or empirical advantage yet.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cIt was an experimental self-driving-lab-style application of batch multi-objective BO to printed impact absorbers, but the talk did not show whether BO beat space-filling sampling.\u201d\n\nThat differs from the intended message because I would not repeat \u201cdozens, not hundreds\u201d without a budget-matched baseline.\n\n## (c) My top three Q&A questions\n\n1. What are the exact decision variables and encodings, and why do the deck, scope statement, and abstract appear to disagree on whether there are six or seven variables and whether FDM parameters are optimized?\n2. How is observation noise estimated: fixed noise, learned heteroscedastic noise, technical replicates, or repeated control specimens? What is the replicate allocation?\n3. Against what budget-matched baseline do you evaluate qNEHVI, and what metric do you report: feasible hypervolume, best feasible objective values, or attainment probability versus physical tests?\n\n## (d) My most pointed objection\n\n**Nothing visible distinguishes a validated BO contribution from an untested application of standard qNEHVI.**\n\nUnanswered, this is **critical for DAC-10 reception**. Application novelty can be enough, but only if the physical campaign is rigorous and the adaptive method is compared fairly under the same test budget.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 7.** Replace the black-box inventory with a visual of posterior mean plus uncertainty \u2192 constrained batch acquisition \u2192 next physical tests, accompanied by the exact objectives and force constraint.\n- **Hidden-slide decision:** **Keep slide 5 hidden** as a standalone generic BO tutorial. Merge one plain-language BO sentence into slide 7 and put qNEHVI details in backup.\n\n---\n\n# P2. The aerospace practitioner\n\n## (a) My reaction in the room\n\nThe Super Ball Bot hook works because I know the lineage. Reusable landing structures are interesting. But the title\u2019s claim that tensegrity provides \u201crobust, reusable solutions\u201d is broader than what this deck demonstrates. A lab drop of a polymer proxy is not evidence of robustness in thermal extremes, vacuum, dust, radiation, repeated impacts, or off-axis terrain contact.\n\nThe deck never immediately tells me that the actual specimen is **tensegrity-inspired**, lacks ideal compression-only and tension-only membership, and is not intended to fly. That makes slide 2 vulnerable to looking like borrowed aerospace relevance.\n\nSlide 4 is plausible but too broad. Tensegrity design can be difficult, but aerospace development is not made slow only by design-space search. Verification, environmental qualification, uncertainty, packaging, guidance, and system integration matter. A faster specimen loop is useful, but it is one subsystem tool.\n\nI am not helped much by slide 7. I need BO translated into engineering behavior: it chooses the next small batch based on predicted performance and uncertainty while rejecting designs expected to violate a payload-force limit. The slide should name that limit and show the actual trade-off.\n\nSlides 8 and 9 are the first ones that answer \u201cwhat did you build and how did you hit it?\u201d A labeled specimen, deformation sequence, drop mass, speed or energy, orientation, and force measurement would hold my attention. Two simultaneous looping videos may not. I also want to know whether the structure rebounds, survives repeated drops, and retains its response, because the opening emphasizes reuse.\n\nSlide 10 is understandable. Slide 12 loses me because it jumps from a T3 polymer coupon to lattices and crutch tips without stating transfer conditions or technology gates.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey use an optimizer to choose which 3D-printed soft\u2013rigid impact absorber to drop-test next, with the eventual motivation of reusable landing structures.\u201d\n\nThat is fairly close, but I would deliberately downgrade \u201ctensegrity\u201d to \u201ctensegrity-inspired absorber\u201d and treat planetary landing as motivation only.\n\n## (c) My top three Q&A questions\n\n1. What makes the printed T3 specimen mechanically tensegrity-inspired rather than an ordinary compliant crush structure, and is it actually prestressed?\n2. What impact energy, mass, orientation, and transmitted-force cap are used, and how do they relate to a payload-protection requirement?\n3. How does performance change over repeated impacts, and what failure modes or permanent set limit the claimed reusability?\n\n## (d) My most pointed objection\n\n**The opening borrows flight relevance and reusability, but the visible deck never establishes the proxy boundary or shows repeated-impact evidence.**\n\nUnanswered, this is **highly damaging** to credibility with aerospace practitioners, though it does not invalidate the optimization workflow itself.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 2.** Keep the hook, but place a photograph of the actual T3 specimen beside the Super Ball Bot and state \u201cmotivation, not demonstrated flight readiness.\u201d\n- **Hidden-slide decision:** **Keep slide 3 hidden.** A generic elbow-friction GIF would distract and oversimplify. Merge a precise tensegrity-mechanism annotation into slide 8 instead.\n\n---\n\n# P3. The additive-manufacturing/materials researcher\n\n## (a) My reaction in the room\n\nSlides 1\u20134 give me a recognizable motivation for rapid experimental iteration. My interest rises on slide 8 because multi-material fused deposition modeling is where the claimed loop can succeed or fail.\n\nThe message \u201csingle-build co-fabrication, with no joining of parts\u201d is attractive, but it needs exact boundaries. Is there support removal, trimming, conditioning, manual tensioning, or fixture insertion? Does \u201csingle build\u201d mean one tool-change sequence on one machine, or truly co-deposited interfaces throughout the architecture? Are PLA and thermoplastic polyurethane (TPU) mechanically interlocked, chemically bonded, or merely in contact?\n\nThe abstract provides more useful information than the slide: a core-wrapping architecture is intended to resist delamination, specimens are weighed and dimensionally inspected, and a control is retested to monitor drift. Those are exactly the details the talk should surface. Slide 8\u2019s planned printing video will not replace a labeled cross-section or interface image.\n\nSlide 9 raises repeatability questions. TPU is rate-, temperature-, moisture-, and history-dependent. \u201cLimited, noisy data\u201d on slide 7 acknowledges noise but does not show the control plan. I need batch identity, drying/conditioning, build position, print orientation, machine state, interface failures, and replicates.\n\nThe deck also appears inconsistent about process parameters. The agreed scope says FDM processing parameters are optimized with structure, but the abstract says temperature, line width, infill, and retraction are held fixed within a batch. This could mean they vary between batches or are controlled rather than optimized, but the deck must say which.\n\nI like the physical loop on slide 10. I do not yet accept that multi-material printing \u201csupplies data fast\u201d until cycle time and failure rate are shown.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey co-print PLA and TPU impact specimens and use each mechanical test to guide the next design batch.\u201d\n\nI would probably omit \u201cno joining\u201d and \u201cdozens rather than hundreds\u201d until I saw interface qualification, failure accounting, and campaign data.\n\n## (c) My top three Q&A questions\n\n1. How is the PLA\u2013TPU interface designed and qualified, and what fraction of specimens fail by delamination or print defects?\n2. Which material, machine, conditioning, and build-position variables are controlled, measured as covariates, or optimized?\n3. How many true replicates and repeated control specimens are used to separate BO improvement from printer drift and TPU batch variation?\n\n## (d) My most pointed objection\n\n**The deck treats co-fabrication as an enabling fact without showing interface integrity, repeatability, or the remaining manual processing steps.**\n\nUnanswered, this is **highly damaging** because print variation is part of both the physical mechanism and the statistical noise model.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 8.** Replace or supplement the printing clip with an annotated specimen and interface detail: PLA struts, continuous TPU network, deposition/wrapping strategy, prestress status, post-processing, and observed failure modes.\n- **Hidden-slide decision:** **Keep slide 3 hidden.** Its proposed generic GIFs do not answer the materials questions. Put the relevant deformation mechanism on slide 8.\n\n---\n\n# P4. The first-year graduate student\n\n## (a) My reaction in the room\n\nSlide 1 sounds positive, and slide 2 gives me a memorable reason to care. I understand that the lander survives an impact because the structure itself deforms. But I do not yet know what \u201ctensegrity\u201d means, and the hidden slide 3 would not necessarily fix that: an elbow GIF and a squishing GIF could leave me with an inaccurate friction-versus-elasticity story.\n\nSlide 4 is easy to follow. The three words \u201cIteration, Building, Testing\u201d form a simple problem statement, although the images need to make their relationship obvious.\n\nI get lost on slide 7. Six variables of what? What are the two input-data types? Are the two objectives \u201cabsorb energy\u201d and \u201creduce force,\u201d or something else? I do not know what a black box means in this setting. The title says Bayesian optimization works with noisy data, but I have not been shown how it decides anything.\n\nSlide 8 helps because I can see printing. Still, I need labels that distinguish the rigid and flexible parts and one sentence explaining why the object is \u201ctensegrity-inspired.\u201d Slide 9 is intuitive if the clips are synchronized and annotated. I would understand even better if the slide drew the metric extraction directly on a force\u2013time or force\u2013displacement trace.\n\nSlide 10 is the clearest slide in the deck. Printing \u2192 testing \u2192 optimizer \u2192 next print is the idea I will remember. But it arrives after the technical terms rather than before them. I would benefit from seeing a version of this loop before slide 7 and then revisiting it after the methods.\n\nSlide 12 feels like a new presentation. I have not seen whether the current system worked, so the crutch tip and lattice do not feel earned. Then the talk ends on a blank question slide rather than telling me the answer.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey repeatedly print and drop-test flexible 3D structures, and a machine-learning model decides what to print next.\u201d\n\nMy retelling loses the two objectives, the force constraint, and the \u201cdozens rather than hundreds\u201d quantitative point. I might incorrectly call the method generic machine learning rather than Bayesian optimization.\n\n## (c) My top three Q&A questions\n\n1. What exactly is tensegrity, and which parts of your printed specimen are in tension and compression?\n2. What are the six things the optimizer can change, and what are the two scores it is trying to improve?\n3. How does the optimizer know whether to try something uncertain rather than the design it currently thinks is best?\n\n## (d) My most pointed objection\n\n**The deck names the ingredients before giving me a plain-language map of how they work together.**\n\nUnanswered, this is **moderately damaging**. I retain the loop but lose the actual research question and technical contribution.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 7.** It needs one concrete candidate, predicted performance plus uncertainty, and the next selected batch, not abstract counts around a black box.\n- **Hidden-slide decision:** **Unhide neither slide 3 nor slide 5 as written.** If forced to choose one, **keep slide 5 hidden** and integrate its one-sentence concept into a redesigned slide 7. Generic explanation would add time without restoring the missing optimization details.\n\n---\n\n# P5. The finite-element-analysis veteran\n\n## (a) My reaction in the room\n\nThe opening is fine until slide 4 implies that physical iteration is the central route around slow design. I start waiting for the argument about simulation. Because slide 6 is hidden, the deck never states the agreed, defensible position: a sufficiently calibrated model may cost more than it saves for this campaign, particularly when interfaces, defects, rate dependence, contact, and millisecond shock transients dominate.\n\nThat omission avoids an overt attack on finite-element analysis (FEA), which is good, but it also creates a logical hole. Why is physical BO the chosen source of objective values? Why not a hybrid strategy, reduced-order model, analytical screening, or multi-fidelity optimization? The deck merely proceeds from \u201cdesign is slow\u201d to \u201cBO makes experimentation affordable.\u201d\n\nI object to any suggestion that experimental data are automatically \u201creal\u201d and simulations are not. Measurements also involve sensor bandwidth, filtering, fixture dynamics, uncertainty, and model-based transformations. Slide 9\u2019s \u201caccelerometers gather real data\u201d wording is particularly vulnerable. A force estimate derived from acceleration still rests on calibration and assumptions.\n\nSlide 10 is a useful experimental workflow. It should be presented as a scoped engineering choice, not a universal replacement for simulation. Slide 12\u2019s transfer claim needs the condition from the outline: use this pattern where tests are authoritative, costly, the design space is mixed, and goals conflict.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey bypass a calibrated finite-element model and optimize directly against impact tests because the printed interfaces and transient response are difficult to model economically.\u201d\n\nThat is close to the intended experiment-first rationale, but I had to infer it; the visible slides do not say it clearly.\n\n## (c) My top three Q&A questions\n\n1. What evidence shows that calibrating a useful simulation would cost more than it saves for this campaign, rather than merely being inconvenient?\n2. How do you verify the measured peak transmitted force, including sensor bandwidth, fixture dynamics, filtering, and uncertainty?\n3. Why exclude analytical or low-fidelity screening from a multi-fidelity BO strategy, especially for infeasible geometries and gross structural trends?\n\n## (d) My most pointed objection\n\n**The deck uses experiment-first optimization without stating a scoped cost-and-validity argument or acknowledging where simulation could still add value.**\n\nUnanswered, this is **highly damaging in Q&A**. It makes the methodology look ideologically anti-simulation rather than economically reasoned.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 4.** It should become the two-sided decision: exhaustive physical search is too costly, while campaign-grade calibration for the hardest responses is also costly; therefore choose physical tests adaptively.\n- **Hidden-slide decision:** **Merge slide 6 into slide 4**, rather than unhide it as written. Keep a fuller evidence-backed simulation/experiment comparison as backup.\n\n---\n\n# P6. The friendly industry generalist\n\n## (a) My reaction in the room\n\nThe opening video is memorable, and I like the practical goal of shortening build\u2013test iteration. Slide 4 is easy to understand. My attention slips on slide 7 because the counts and black-box symbol do not tell a story. I do not need the name qNEHVI, but I need to know that the software balances trying promising designs with learning from uncertain ones and respects a force limit.\n\nSlides 8\u201310 are the strongest run. If the presenter shows one labeled specimen, one clean drop clip, and one loop diagram, I can see how I might use the pattern in another product-development problem. I want a cycle-time number and an indication of what software or human work is required.\n\nSlide 12 dilutes the message by offering several destinations before proving the present result. The ending is weak. A blank Q&A slide discards the chance to leave the audience with the workflow, the best specimen, and a measured improvement.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cPrint it, break or drop it, feed the measurements back, and let the optimizer pick the next batch instead of running a huge test matrix.\u201d\n\nThat captures the intended practical pattern, but I would probably overgeneralize it beyond the specific conditions under which it is useful.\n\n## (c) My top three Q&A questions\n\n1. How many prints and calendar days did it take to reach a useful design compared with your previous workflow?\n2. What parts of this system could another engineering team reuse without building the same drop tower or writing a custom optimizer?\n3. What happens when a print fails or a test produces an outlier?\n\n## (d) My most pointed objection\n\n**I understand the loop, but the deck never gives me the one number or result that proves it was worth building.**\n\nUnanswered, this is **moderately to highly damaging**. I would remember the concept but not have a reason to adopt or sponsor it.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 13.** Replace the blank Q&A screen with the best measured result, the closed-loop diagram, and one deployment rule; retain a small \u201cQuestions?\u201d label.\n- **Hidden-slide decision:** **Unhide slide 11** once populated. Results are more valuable than another explanatory slide.\n\n---\n\n# Synthesis\n\n## Cross-persona themes\n\n1. **The scope boundary is missing.** The Super Ball Bot is a good hook, but every persona risks interpreting a PLA\u2013TPU proxy as a claim about flight hardware.\n2. **Slide 7 is the weakest visible technical slide.** It serves neither experts nor novices. Counts such as \u201c6 variables\u201d and \u201c2 objectives\u201d are information, not a message.\n3. **The main claim currently lacks evidence.** \u201cSignificantly accelerate,\u201d \u201csupplies data fast,\u201d and \u201cdozens, not hundreds\u201d require a campaign ledger and a budget-matched comparison.\n4. **The results omission breaks the story.** The deck follows the outline through the workflow, then skips the outline\u2019s largest time block and moves directly to future work.\n5. **The physical system needs more definition.** The audience needs a labeled specimen, a clear explanation of \u201ctensegrity-inspired,\u201d interface design, remaining manual steps, and the measurement chain.\n6. **Noise must be operational rather than rhetorical.** Saying the data are noisy is insufficient. Replicates, periodic controls, drift, failures, and uncertainty should be shown.\n7. **Experiment-first needs a scoped rationale.** The deck should not dismiss FEA, but it should explain why adaptive physical testing is the economical source of objective values for this campaign.\n8. **The close is not a conclusion.** Future applications and a blank Q&A slide do not substitute for the measured finding and its conditions of transfer.\n9. **Media are a communication and schedule risk.** Short clips can work, but multiple looping videos violate the signal-to-noise principle and create failure modes.\n10. **The project specification needs reconciliation.** The supplied materials differ on six versus seven variables, whether FDM process parameters are optimized, and whether peak force is a constraint or part of the Pareto trade-off.\n\n## Five highest-priority slide-level revisions\n\n### 1. Slide 11: replace one empty slot with a three-slide quantitative results sequence\n\nUse full-sentence titles tied to observed data:\n\n- **11A:** \u201cThe campaign used **N** specimens in **B** adaptive batches, including **R** replicates and **F** failed prints.\u201d\n- **11B:** \u201cAt the same physical-test budget, adaptive selection reached **[measured feasible-performance result]** faster than **[predeclared Sobol/random baseline]**.\u201d\n- **11C:** \u201cThe measured feasible Pareto front trades specific energy absorption against compaction efficiency while respecting a **[value and unit]** peak-force cap.\u201d\n\nShow uncertainty, replicate spread, infeasible points, and representative specimen photographs or traces. If the campaign cannot support the baseline claim, change the talk\u2019s main message to: **\u201cWe evaluate whether noise-aware multi-objective Bayesian optimization can reduce the physical-test budget.\u201d**\n\n### 2. Slide 7: state the actual constrained optimization problem\n\nReplace the current black-box/count design with:\n\n- the named decision variables and their types;\n- a small batch entering the printer/test loop;\n- objectives: maximize specific energy absorption and compaction efficiency;\n- constraint: peak transmitted force below a stated cap;\n- qNEHVI\u2019s role in one plain sentence: it chooses a small next batch likely to expand the feasible trade-off frontier while accounting for uncertainty and noisy tests;\n- the declared test budget, initialization size, and batch size.\n\nResolve six versus seven variables and whether processing parameters are decision variables before finalizing this slide.\n\n### 3. Slide 2: put the proxy boundary beside the hook\n\nKeep the Super Ball Bot clip, but add an image of the actual specimen and the visible sentence:\n\n> **\u201cHere, a printed PLA\u2013TPU T3 prism is a proxy for developing the workflow, not flight hardware.\u201d**\n\nChange or qualify \u201crobust, reusable solutions\u201d unless the present campaign includes repeat-impact evidence.\n\n### 4. Slide 4: turn the vague need into the two-sided engineering decision\n\nRetitle it:\n\n> **\u201cBecause exhaustive testing and campaign-grade model calibration are both costly, each physical specimen must be chosen for its information value.\u201d**\n\nAdd one quantified physical-search estimate and one scoped calibration challenge. Do not say simulation is untrustworthy. State that FEA and lower-fidelity models remain possible screening or future multi-fidelity tools.\n\n### 5. Slides 10 and 13: separate the demonstrated loop from the conclusion\n\n- **Slide 10:** retitle to **\u201cThe closed loop turns each measured test into the next small batch of specimens.\u201d** Remove \u201csignificantly accelerate\u201d until the results demonstrate it. Mark automated and manual stages.\n- **Slide 13:** retain the closed-loop thumbnail, add the best quantitative result, and state the deployment rule: **use the loop when tests are authoritative, each test is costly, the design space is mixed, and performance goals conflict.** Add \u201cQuestions?\u201d without clearing the conclusion from the screen.\n\n## Hidden-slide adjudication\n\n| Hidden slide | Recommendation | Rationale |\n|---|---|---|\n| **3: tensegrity usefulness/mechanism** | **Merge into slide 8** | The generic elbow and squishing GIFs add noise and risk an inaccurate mechanism; an annotated actual specimen and deformation sequence are stronger. |\n| **5: generic BO explanation** | **Merge into slide 7; keep original as backup or delete** | DAC experts do not need a generic advocacy slide, but novices need one plain-language sentence and a concrete acquisition visual. |\n| **6: specimen information value/experiment-first** | **Merge into slide 4; keep evidence-rich version as backup** | The argument is structurally necessary, but the current title is vague and the visual does not support the scoped calibration-versus-campaign-cost decision. |\n| **11: results** | **Unhide and expand to three slides** | Results are the evidentiary center of the talk and were allocated five minutes in the agreed outline. An empty or hidden results slot makes the main claim untenable. |\n\n## Visible-slide claims needing evidence, hedging, or backup\n\n| Slide | Claim | Required treatment |\n|---|---|---|\n| **1** | \u201cBuild better tensegrity structures faster\u201d | Define \u201cbetter\u201d with the two objectives and force constraint; support \u201cfaster\u201d with the results comparison. |\n| **2** | Tensegrity is robust and reusable for planetary landings | Cite the Super Ball Bot source; distinguish concept evidence from this study; provide repeat-impact evidence or hedge reusability. |\n| **2** | Parachutes and retrorockets struggle in the stated conditions | Cite an authoritative aerospace source if retained in narration; avoid implying universal inadequacy. |\n| **4** | Current tensegrity design is slow and resource intensive | Quantify candidate count, print/test time, hand-assembly burden, or calibration effort. |\n| **7** | BO makes experiments affordable with limited, noisy data | Show campaign budget, noise characterization, replicates, baseline, and performance versus tests. Prepare qNEHVI and Gaussian-process details in backup. |\n| **7** | Six variables, two input-data types, two objectives | Reconcile with the apparent seven-variable abstract specification and identify every quantity. Clarify \u201cinput-data types.\u201d |\n| **8** | Multi-material AM supplies data fast | Report print-to-result cycle time and failure rate. |\n| **8** | Single-build co-fabrication with no joining | Show interface and post-processing; use \u201cno joining of PLA and TPU members\u201d if other manual operations remain. |\n| **9** | Accelerometers gather the relevant impact data | Show sensor locations and the derivation of transmitted force, specific energy absorption, and compaction efficiency. Reconcile this wording with the abstract\u2019s instrumented-tup description. |\n| **10** | The loop significantly accelerates optimization | Remove \u201csignificantly\u201d unless a defined baseline and uncertainty support it. Statistical significance should not be implied casually. |\n| **12** | Workflow will transfer to lattices and other applications | Present as conditional future work with transfer criteria, not an achieved result. Attribute any external crutch-tip image and claim. |\n\n### Prepared backup slides\n\n1. Full design-variable table, bounds, encoding, and fixed versus optimized process parameters.\n2. qNEHVI implementation, Gaussian-process assumptions, constraint treatment, batch size, and acquisition settings.\n3. Noise plan: replicates, periodic control, heteroscedasticity, drift, outlier policy, and uncertainty propagation.\n4. Budget-matched baseline definition and evaluation metric.\n5. Force-cap rationale and full objective definitions.\n6. Sensor chain, calibration, sample rate, filtering, fixture dynamics, and metric extraction.\n7. PLA\u2013TPU interface, conditioning, build layout, dimensional/mass deviations, and print-failure modes.\n8. Scoped FEA rationale with citations or internal validation; possible analytical or multi-fidelity extensions.\n9. Automation map and cycle-time breakdown.\n10. Repeated-impact behavior if reusability remains in the opening.\n\n## Merged, ordered presenter TODO list\n\n### Must fix before the talk\n\n1. **Freeze the scientific specification.** Reconcile the number and type of decision variables, whether FDM parameters are optimized or controlled, the two objectives, the force constraint, treatment of print failures, and the measurement chain. Use one formulation across slides, abstract, paper, and Q&A.\n2. **Set a results completion plan.** Establish dates and owners for campaign completion, repeat tests, data quality review, baseline analysis, and slide freeze. Predeclare the fallback claim if the baseline does not support \u201cdozens rather than hundreds.\u201d\n3. **Build the three-slide results sequence.** Campaign ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front. Allocate approximately five minutes, as the agreed outline requires.\n4. **Add the proxy statement to slide 2.** State plainly that the PLA\u2013TPU T3 specimen is a workflow proxy and not flight hardware.\n5. **Redesign slide 7.** Show the exact constrained multi-objective problem, test budget, small batches, uncertainty, noisy observations, and the role of qNEHVI.\n6. **Remove unsupported result language.** Until supported, delete \u201csignificantly\u201d from slide 10 and avoid \u201cdozens rather than hundreds\u201d in the title, narration, or close.\n7. **Clarify experiment-first positioning.** Merge the information-value argument into slide 4 and prepare an evidence-backed FEA backup slide.\n8. **Make the experiment auditable.** On slide 9 or backup, reconcile accelerometers and the instrumented tup; show how all three performance metrics are calculated and how uncertainty is handled.\n9. **De-risk every video.** Store local trimmed files, disable distracting loops, test them on presentation hardware, and include static fallback frames.\n10. **Rehearse to 13:30\u201314:00.** Protect the results and conclusion from being cut. Practice once without slides, consistent with Doumont\u2019s preparation advice.\n\n### Should fix\n\n11. **Replace slide 8\u2019s generic print emphasis with mechanism and materials evidence.** Label PLA, TPU, interface strategy, prestress status, deformation, post-processing, and failure modes.\n12. **Quantify slide 4.** Give at least one campaign-relevant cost, duration, or candidate-count estimate.\n13. **Show automation boundaries on slide 10.** Candidate selection and surrogate updating may be automated; slicing, handling, test setup, and testing remain manual according to the abstract.\n14. **Convert slide 12 to a gated roadmap.** Separate demonstrated T3-proxy results from future lattices, flight materials, and terrestrial applications.\n15. **Turn slide 13 into the conclusion.** Leave the audience with the best result and transfer rule while taking questions.\n16. **Prepare technical backup slides.** Prioritize baseline fairness, noise, force-cap rationale, print failures, materials repeatability, and FEA positioning.\n\n### Polish\n\n17. Replace the slide 1 title with a specific message, such as **\u201cPhysical Bayesian optimization searches multi-material tensegrity absorbers one small test batch at a time.\u201d**\n18. Left-align titles and use intentional line breaks; check every slide at six-per-page scale.\n19. Use one purposeful visual per slide. Avoid decorative loading or elbow animations.\n20. Standardize terminology: \u201cBayesian optimization,\u201d not \u201cBays Opt\u201d; \u201ctensegrity-inspired\u201d where appropriate; define specific energy absorption once.\n21. Add citations directly beside borrowed footage, images, and quantitative claims.\n\n## Who the deck serves best and worst\n\n- **Best served:** **P6, the friendly industry generalist.** The visible sequence is visually intuitive, practical, and easy to reduce to \u201cprint \u2192 test \u2192 learn \u2192 repeat.\u201d\n- **Worst served:** **P1, the skeptical BO insider.** The current deck omits nearly every item needed to judge a DAC-10 contribution: exact problem formulation, budget, baseline, noise model, constraint handling, and empirical adaptive advantage.\n\nThat is **not the right trade-off for DAC-10**. The deck should remain accessible to P4 and P6, but its technical center must satisfy P1 without turning into a BO lecture. One rigorous optimization slide, three quantitative results slides, and targeted backup material would achieve that balance.\n\n## Predicted reception\n\n- **As is: 4/10.** The physical workflow and application are memorable, but the absent scope statement, empty results section, vague BO slide, and unsupported acceleration claim leave the DAC contribution unproven.\n- **After the must-fixes: 8/10.** A scoped proxy claim, reconciled methods, fair budget-matched comparison, feasible Pareto front, and measured conclusion would turn the same visual story into a credible design-optimization application talk.\n\n## Discretionary analytical decisions\n\n- Evaluated only the visible slide order for the simulated in-room reactions, while using hidden slides and presenter notes to judge revision options.\n- Treated planned placeholder visuals as if implemented according to the presenter\u2019s notes, but judged the selected visual concept and associated stage risk rather than absent graphic quality.\n- Prioritized the Draft 3 outline over conflicting Draft 1 wording because the prompt identifies it as the agreed story arc, scope framing, results plan, and timing plan.\n- Rated objections qualitatively as moderate, high, or critical rather than assigning unsupported numerical probabilities.\n- Recommended three results slides rather than preserving one reserved slot because the agreed outline specifies three distinct evidentiary products and allocates five minutes to results.\n- Recommended merging hidden slides 3, 5, and 6 rather than simply unhiding them because their necessary ideas can be conveyed with higher signal-to-noise on existing slides.\n- Treated the six-versus-seven-variable and optimized-versus-fixed-process-parameter differences as unresolved specification conflicts requiring reconciliation, rather than inferring which document is correct.\n- Assessed claims against the supplied deck extraction, Draft 3 outline, Doumont notes, and submitted abstract only; no independent validation of cited literature or unseen experimental data was attempted.') [id=call_8In1tJq32AD2tTyHX3tmIlLf]", + "answer": "# Mock audience report: IDETC-CIE 2026 slide deck, Draft 1\n\n## Executive read\n\nThe visible deck has a clear visual spine: **motivation \u2192 slow iteration \u2192 Bayesian optimization \u2192 multi-material printing \u2192 impact testing \u2192 closed loop \u2192 future applications**. The strongest section is slides 8\u201310, where fabrication, testing, and adaptive selection finally appear as one workflow.\n\nBut Draft 1 does not yet deliver the technical case promised by the agreed outline. The visible deck omits four things a DAC-10 audience will need:\n\n1. **Honest scope:** no visible slide says that the PLA\u2013TPU T3 prism is a proxy system rather than flight hardware.\n2. **A precise optimization problem:** the six variables, two objectives, force constraint, noise treatment, and fabrication failures are not intelligible on slide 7.\n3. **Quantitative evidence:** the sole results slot is hidden and empty, yet slides 10 and 12 already claim acceleration and transfer.\n4. **A measured conclusion:** the talk jumps from the workflow to speculative applications without showing a campaign ledger, comparison against a budget-matched baseline, or feasible Pareto front.\n\nThe slide titles generally follow Doumont\u2019s full-sentence-message rule, but several messages outrun the evidence. The deck also departs substantially from the Draft 3 timing plan, which reserves **5 of 15 minutes for results**. As built, results receive zero visible slides.\n\n---\n\n# P0. The program manager in the audience\n\n## (a) My reaction in the room\n\nSlide 1 tells me the team wants to work faster, but it does not tell me what is being optimized or what has actually been built. The title sounds aspirational rather than traceable to a project deliverable. I immediately want the formal title from the abstract or a shorter version of it.\n\nThe Super Ball Bot footage on slide 2 is an effective hook. I understand why reusable impact attenuation matters. But the slide creates a scope risk: I am looking at planetary landing hardware while the actual work is a printed PLA\u2013TPU T3 proxy. Because that distinction is not stated immediately, I may spend the rest of the talk evaluating the work against flight-readiness criteria the project never intended to meet.\n\nSlide 4 gives me the business problem, but not a baseline. \u201cSlow and resource intensive\u201d needs a number: hours per print-test cycle, number of factors, or the candidate count under a declared design of experiments. Without that, I cannot assess whether the proposed loop attacks the dominant schedule driver.\n\nSlide 7 is where I expect the project definition. Instead, \u201c6 variables,\u201d \u201c2 types of input data,\u201d and \u201c2 objectives\u201d read like inventory labels. I cannot reconstruct the statement of work from them. I need to see what is automated, what remains manual, what the force cap is, and what constitutes a successful campaign.\n\nSlides 8 and 9 make the project tangible. A labeled specimen and an actual drop test would build confidence. Yet \u201csingle-build\u201d and \u201cno joining\u201d need a process qualification caveat, and I need the measurement chain to be explicit. The abstract refers to an instrumented tup and force\u2013time data; slide 9 says accelerometers. Those may be compatible, but the deck presently leaves the relationship unclear.\n\nSlide 10 is the right management view of the system, but \u201csignificantly accelerate\u201d is a result claim placed on a workflow diagram. At this point I ask, \u201cCompared with what, by how much, and over how many specimens?\u201d Then the deck skips the hidden results slide and goes directly to future applications. That is the point where my confidence drops sharply. The project is asking me to extrapolate before closing the evidence loop.\n\nSlide 12 is too early and too broad. A crutch tip and lattice can be legitimate next phases, but only after the team shows what the present campaign established and what technical risks remain. The blank questions slide gives me no durable take-home message.\n\n### PM stage-risk register\n\n| Risk | Likelihood | Consequence | Mitigation |\n|---|---:|---:|---|\n| Results are incomplete by conference lock | Medium to high | Critical: central claim becomes unsupported | Set a dated campaign freeze; predeclare a fallback message and minimum publishable result package |\n| Embedded Super Ball Bot or drop-test video fails | Medium | Moderate: weak opening or broken pacing | Use local, trimmed files; retain a still-frame fallback on every video slide |\n| Talk overruns because several videos and explanations are improvised | High | High: results or conclusion gets cut | Script videos to 10\u201315 seconds total each; rehearse to 13:30; assign 5 minutes to results |\n| Scope is mistaken for flight-hardware validation | High | High: credibility damage in Q&A | Put \u201cPLA\u2013TPU T3 proxy; not flight hardware\u201d on slide 2 or the next slide |\n| Acceleration claim lacks a budget-matched comparator | High | Critical for DAC review audience | Show Sobol/random or another predeclared baseline at the same physical-test budget |\n| Measurement chain is challenged | Medium | High | Add a methods schematic and backup slide on calibration, sampling, filtering, and metric extraction |\n| \u201cNo joining\u201d or \u201cnear autonomous\u201d is read as full automation | Medium | Moderate | List manual steps explicitly: slicing, handling, setup, and testing |\n| Animations distract or fail to communicate without narration | Medium | Moderate | Prefer one short clip plus static annotated frames; follow Doumont\u2019s signal-to-noise rule |\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey are building a loop that prints PLA\u2013TPU tensegrity-like absorbers, drop-tests them, and lets Bayesian optimization choose the next print.\u201d\n\nThat retains the basic workflow but loses the intended **dozens rather than hundreds** claim because the deck never demonstrates it. I would also probably describe the work as \u201clander research,\u201d which is a distortion caused by the missing proxy-system statement.\n\n## (c) My top three Q&A questions\n\n1. What is the dated plan to complete the campaign, baseline comparison, and repeat testing before the conference slide freeze?\n2. Which exact operations are automated today, which remain manual, and what is the measured end-to-end cycle time per adaptive batch?\n3. What evidence will justify \u201cdozens rather than hundreds,\u201d and what will you claim if the budget-matched baseline does not show an advantage?\n\n## (d) My most pointed objection\n\n**You are presenting a claimed acceleration without showing a completed campaign, a comparator, or even a visible results slide.**\n\nUnanswered, this is **critical**. I would not fund a scale-up phase from this deck. I might fund completion of the present campaign if the team showed a disciplined schedule, predefined success criteria, and a credible fallback.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 11.** It must become a real, scheduled results package, preferably three slides: campaign ledger, budget-matched baseline, and feasible Pareto front.\n- **Hidden-slide decision:** **Unhide slide 11**, but not in its current empty form. The talk cannot make its principal claim without it.\n\n### PM slide-level punch list, ordered by schedule criticality\n\n**Must be fixed first**\n\n1. **Slide 11/results sequence:** assign owners and dates for data freeze, quality control, baseline computation, replicate analysis, and final plots. Define fallback wording now.\n2. **Slide 2:** add the explicit scope line, \u201cThis study uses a printed PLA\u2013TPU T3 prism as a proxy for developing the workflow; it is not flight hardware.\u201d\n3. **Slide 7:** replace category counts with the actual optimization problem, test budget, batch size, objectives, constraint, and noise/failure treatment.\n4. **Slide 10:** remove \u201csignificantly\u201d until slide 11 demonstrates a quantitative improvement; show automation boundaries and the loop\u2019s measured cycle time.\n5. **Slides 2 and 9:** download and trim media, test on the conference laptop, and put a static fallback image on each slide.\n\n**Then fix**\n\n6. **Slide 4:** quantify the current cost or candidate count and identify the bottleneck.\n7. **Slide 8:** label PLA, TPU, interface, and any post-processing; qualify \u201cno joining.\u201d\n8. **Slide 9:** reconcile accelerometers with the instrumented-tup description and show how force, energy absorption, and compaction efficiency are obtained.\n9. **Slide 12:** convert the application collage into a conditional next-phase roadmap with gates.\n10. **Slide 13:** end on the measured result and deployment rule, not a blank Q&A screen.\n\n**Nice to have**\n\n11. Replace decorative or looping media on slide 4 with one static process image unless it communicates a measured delay.\n12. Add backup slides on Bayesian optimization implementation, finite-element-analysis positioning, repeatability, force-cap rationale, and print failures.\n\n---\n\n# P1. The skeptical Bayesian-optimization insider\n\n## (a) My reaction in the room\n\nThe opening is competent but generic for a design-automation conference. Slides 1\u20134 set up an expensive physical experiment, which is a valid application class for Bayesian optimization (BO). I become interested when the presenter reaches slide 7, because that is where methodological seriousness should appear.\n\nInstead, slide 7 says only \u201c6 variables,\u201d \u201c2 types of input data,\u201d and \u201c2 objectives.\u201d I do not learn the variables, their types, how categorical choices are encoded, whether process parameters are optimized, how noisy observations enter the Gaussian processes, how replicates identify noise, how failures are modeled, or how the force cap enters the acquisition. The title says BO handles noisy data, but the visual does not substantiate that message.\n\nThere is also an unresolved specification conflict across the supplied materials. The agreed outline says the study jointly optimizes T3 geometry and FDM processing parameters. The abstract\u2019s design vector lists four continuous geometric variables, one integer, and two categorical variables, apparently **seven variables**, while its fabrication section says print parameters are held fixed within a batch. The slide says **six variables**. A DAC audience will notice if the spoken details do not reconcile these versions.\n\nSlides 8\u201310 explain why physical feedback is useful, but they do not establish why qNEHVI is the right algorithm or whether adaptive selection helped. The visible deck never names qNEHVI, the feasible Pareto front, the peak-force constraint, batch size, initialization, or test budget. Hiding slide 5 is reasonable if it is only a generic \u201cwhat is BO?\u201d slide copied from another talk. But slide 7 must then carry the application-specific methodological content.\n\nWhen slide 10 says the workflow \u201csignificantly accelerate[s]\u201d optimization, I wait for a performance-versus-budget curve. It never arrives. Slide 12 then generalizes to other applications. My default conclusion is exactly the skeptical one posed in the prompt: this appears to be off-the-shelf qNEHVI attached to an interesting physical apparatus, with no demonstrated algorithmic or empirical advantage yet.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cIt was an experimental self-driving-lab-style application of batch multi-objective BO to printed impact absorbers, but the talk did not show whether BO beat space-filling sampling.\u201d\n\nThat differs from the intended message because I would not repeat \u201cdozens, not hundreds\u201d without a budget-matched baseline.\n\n## (c) My top three Q&A questions\n\n1. What are the exact decision variables and encodings, and why do the deck, scope statement, and abstract appear to disagree on whether there are six or seven variables and whether FDM parameters are optimized?\n2. How is observation noise estimated: fixed noise, learned heteroscedastic noise, technical replicates, or repeated control specimens? What is the replicate allocation?\n3. Against what budget-matched baseline do you evaluate qNEHVI, and what metric do you report: feasible hypervolume, best feasible objective values, or attainment probability versus physical tests?\n\n## (d) My most pointed objection\n\n**Nothing visible distinguishes a validated BO contribution from an untested application of standard qNEHVI.**\n\nUnanswered, this is **critical for DAC-10 reception**. Application novelty can be enough, but only if the physical campaign is rigorous and the adaptive method is compared fairly under the same test budget.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 7.** Replace the black-box inventory with a visual of posterior mean plus uncertainty \u2192 constrained batch acquisition \u2192 next physical tests, accompanied by the exact objectives and force constraint.\n- **Hidden-slide decision:** **Keep slide 5 hidden** as a standalone generic BO tutorial. Merge one plain-language BO sentence into slide 7 and put qNEHVI details in backup.\n\n---\n\n# P2. The aerospace practitioner\n\n## (a) My reaction in the room\n\nThe Super Ball Bot hook works because I know the lineage. Reusable landing structures are interesting. But the title\u2019s claim that tensegrity provides \u201crobust, reusable solutions\u201d is broader than what this deck demonstrates. A lab drop of a polymer proxy is not evidence of robustness in thermal extremes, vacuum, dust, radiation, repeated impacts, or off-axis terrain contact.\n\nThe deck never immediately tells me that the actual specimen is **tensegrity-inspired**, lacks ideal compression-only and tension-only membership, and is not intended to fly. That makes slide 2 vulnerable to looking like borrowed aerospace relevance.\n\nSlide 4 is plausible but too broad. Tensegrity design can be difficult, but aerospace development is not made slow only by design-space search. Verification, environmental qualification, uncertainty, packaging, guidance, and system integration matter. A faster specimen loop is useful, but it is one subsystem tool.\n\nI am not helped much by slide 7. I need BO translated into engineering behavior: it chooses the next small batch based on predicted performance and uncertainty while rejecting designs expected to violate a payload-force limit. The slide should name that limit and show the actual trade-off.\n\nSlides 8 and 9 are the first ones that answer \u201cwhat did you build and how did you hit it?\u201d A labeled specimen, deformation sequence, drop mass, speed or energy, orientation, and force measurement would hold my attention. Two simultaneous looping videos may not. I also want to know whether the structure rebounds, survives repeated drops, and retains its response, because the opening emphasizes reuse.\n\nSlide 10 is understandable. Slide 12 loses me because it jumps from a T3 polymer coupon to lattices and crutch tips without stating transfer conditions or technology gates.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey use an optimizer to choose which 3D-printed soft\u2013rigid impact absorber to drop-test next, with the eventual motivation of reusable landing structures.\u201d\n\nThat is fairly close, but I would deliberately downgrade \u201ctensegrity\u201d to \u201ctensegrity-inspired absorber\u201d and treat planetary landing as motivation only.\n\n## (c) My top three Q&A questions\n\n1. What makes the printed T3 specimen mechanically tensegrity-inspired rather than an ordinary compliant crush structure, and is it actually prestressed?\n2. What impact energy, mass, orientation, and transmitted-force cap are used, and how do they relate to a payload-protection requirement?\n3. How does performance change over repeated impacts, and what failure modes or permanent set limit the claimed reusability?\n\n## (d) My most pointed objection\n\n**The opening borrows flight relevance and reusability, but the visible deck never establishes the proxy boundary or shows repeated-impact evidence.**\n\nUnanswered, this is **highly damaging** to credibility with aerospace practitioners, though it does not invalidate the optimization workflow itself.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 2.** Keep the hook, but place a photograph of the actual T3 specimen beside the Super Ball Bot and state \u201cmotivation, not demonstrated flight readiness.\u201d\n- **Hidden-slide decision:** **Keep slide 3 hidden.** A generic elbow-friction GIF would distract and oversimplify. Merge a precise tensegrity-mechanism annotation into slide 8 instead.\n\n---\n\n# P3. The additive-manufacturing/materials researcher\n\n## (a) My reaction in the room\n\nSlides 1\u20134 give me a recognizable motivation for rapid experimental iteration. My interest rises on slide 8 because multi-material fused deposition modeling is where the claimed loop can succeed or fail.\n\nThe message \u201csingle-build co-fabrication, with no joining of parts\u201d is attractive, but it needs exact boundaries. Is there support removal, trimming, conditioning, manual tensioning, or fixture insertion? Does \u201csingle build\u201d mean one tool-change sequence on one machine, or truly co-deposited interfaces throughout the architecture? Are PLA and thermoplastic polyurethane (TPU) mechanically interlocked, chemically bonded, or merely in contact?\n\nThe abstract provides more useful information than the slide: a core-wrapping architecture is intended to resist delamination, specimens are weighed and dimensionally inspected, and a control is retested to monitor drift. Those are exactly the details the talk should surface. Slide 8\u2019s planned printing video will not replace a labeled cross-section or interface image.\n\nSlide 9 raises repeatability questions. TPU is rate-, temperature-, moisture-, and history-dependent. \u201cLimited, noisy data\u201d on slide 7 acknowledges noise but does not show the control plan. I need batch identity, drying/conditioning, build position, print orientation, machine state, interface failures, and replicates.\n\nThe deck also appears inconsistent about process parameters. The agreed scope says FDM processing parameters are optimized with structure, but the abstract says temperature, line width, infill, and retraction are held fixed within a batch. This could mean they vary between batches or are controlled rather than optimized, but the deck must say which.\n\nI like the physical loop on slide 10. I do not yet accept that multi-material printing \u201csupplies data fast\u201d until cycle time and failure rate are shown.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey co-print PLA and TPU impact specimens and use each mechanical test to guide the next design batch.\u201d\n\nI would probably omit \u201cno joining\u201d and \u201cdozens rather than hundreds\u201d until I saw interface qualification, failure accounting, and campaign data.\n\n## (c) My top three Q&A questions\n\n1. How is the PLA\u2013TPU interface designed and qualified, and what fraction of specimens fail by delamination or print defects?\n2. Which material, machine, conditioning, and build-position variables are controlled, measured as covariates, or optimized?\n3. How many true replicates and repeated control specimens are used to separate BO improvement from printer drift and TPU batch variation?\n\n## (d) My most pointed objection\n\n**The deck treats co-fabrication as an enabling fact without showing interface integrity, repeatability, or the remaining manual processing steps.**\n\nUnanswered, this is **highly damaging** because print variation is part of both the physical mechanism and the statistical noise model.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 8.** Replace or supplement the printing clip with an annotated specimen and interface detail: PLA struts, continuous TPU network, deposition/wrapping strategy, prestress status, post-processing, and observed failure modes.\n- **Hidden-slide decision:** **Keep slide 3 hidden.** Its proposed generic GIFs do not answer the materials questions. Put the relevant deformation mechanism on slide 8.\n\n---\n\n# P4. The first-year graduate student\n\n## (a) My reaction in the room\n\nSlide 1 sounds positive, and slide 2 gives me a memorable reason to care. I understand that the lander survives an impact because the structure itself deforms. But I do not yet know what \u201ctensegrity\u201d means, and the hidden slide 3 would not necessarily fix that: an elbow GIF and a squishing GIF could leave me with an inaccurate friction-versus-elasticity story.\n\nSlide 4 is easy to follow. The three words \u201cIteration, Building, Testing\u201d form a simple problem statement, although the images need to make their relationship obvious.\n\nI get lost on slide 7. Six variables of what? What are the two input-data types? Are the two objectives \u201cabsorb energy\u201d and \u201creduce force,\u201d or something else? I do not know what a black box means in this setting. The title says Bayesian optimization works with noisy data, but I have not been shown how it decides anything.\n\nSlide 8 helps because I can see printing. Still, I need labels that distinguish the rigid and flexible parts and one sentence explaining why the object is \u201ctensegrity-inspired.\u201d Slide 9 is intuitive if the clips are synchronized and annotated. I would understand even better if the slide drew the metric extraction directly on a force\u2013time or force\u2013displacement trace.\n\nSlide 10 is the clearest slide in the deck. Printing \u2192 testing \u2192 optimizer \u2192 next print is the idea I will remember. But it arrives after the technical terms rather than before them. I would benefit from seeing a version of this loop before slide 7 and then revisiting it after the methods.\n\nSlide 12 feels like a new presentation. I have not seen whether the current system worked, so the crutch tip and lattice do not feel earned. Then the talk ends on a blank question slide rather than telling me the answer.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey repeatedly print and drop-test flexible 3D structures, and a machine-learning model decides what to print next.\u201d\n\nMy retelling loses the two objectives, the force constraint, and the \u201cdozens rather than hundreds\u201d quantitative point. I might incorrectly call the method generic machine learning rather than Bayesian optimization.\n\n## (c) My top three Q&A questions\n\n1. What exactly is tensegrity, and which parts of your printed specimen are in tension and compression?\n2. What are the six things the optimizer can change, and what are the two scores it is trying to improve?\n3. How does the optimizer know whether to try something uncertain rather than the design it currently thinks is best?\n\n## (d) My most pointed objection\n\n**The deck names the ingredients before giving me a plain-language map of how they work together.**\n\nUnanswered, this is **moderately damaging**. I retain the loop but lose the actual research question and technical contribution.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 7.** It needs one concrete candidate, predicted performance plus uncertainty, and the next selected batch, not abstract counts around a black box.\n- **Hidden-slide decision:** **Unhide neither slide 3 nor slide 5 as written.** If forced to choose one, **keep slide 5 hidden** and integrate its one-sentence concept into a redesigned slide 7. Generic explanation would add time without restoring the missing optimization details.\n\n---\n\n# P5. The finite-element-analysis veteran\n\n## (a) My reaction in the room\n\nThe opening is fine until slide 4 implies that physical iteration is the central route around slow design. I start waiting for the argument about simulation. Because slide 6 is hidden, the deck never states the agreed, defensible position: a sufficiently calibrated model may cost more than it saves for this campaign, particularly when interfaces, defects, rate dependence, contact, and millisecond shock transients dominate.\n\nThat omission avoids an overt attack on finite-element analysis (FEA), which is good, but it also creates a logical hole. Why is physical BO the chosen source of objective values? Why not a hybrid strategy, reduced-order model, analytical screening, or multi-fidelity optimization? The deck merely proceeds from \u201cdesign is slow\u201d to \u201cBO makes experimentation affordable.\u201d\n\nI object to any suggestion that experimental data are automatically \u201creal\u201d and simulations are not. Measurements also involve sensor bandwidth, filtering, fixture dynamics, uncertainty, and model-based transformations. Slide 9\u2019s \u201caccelerometers gather real data\u201d wording is particularly vulnerable. A force estimate derived from acceleration still rests on calibration and assumptions.\n\nSlide 10 is a useful experimental workflow. It should be presented as a scoped engineering choice, not a universal replacement for simulation. Slide 12\u2019s transfer claim needs the condition from the outline: use this pattern where tests are authoritative, costly, the design space is mixed, and goals conflict.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cThey bypass a calibrated finite-element model and optimize directly against impact tests because the printed interfaces and transient response are difficult to model economically.\u201d\n\nThat is close to the intended experiment-first rationale, but I had to infer it; the visible slides do not say it clearly.\n\n## (c) My top three Q&A questions\n\n1. What evidence shows that calibrating a useful simulation would cost more than it saves for this campaign, rather than merely being inconvenient?\n2. How do you verify the measured peak transmitted force, including sensor bandwidth, fixture dynamics, filtering, and uncertainty?\n3. Why exclude analytical or low-fidelity screening from a multi-fidelity BO strategy, especially for infeasible geometries and gross structural trends?\n\n## (d) My most pointed objection\n\n**The deck uses experiment-first optimization without stating a scoped cost-and-validity argument or acknowledging where simulation could still add value.**\n\nUnanswered, this is **highly damaging in Q&A**. It makes the methodology look ideologically anti-simulation rather than economically reasoned.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 4.** It should become the two-sided decision: exhaustive physical search is too costly, while campaign-grade calibration for the hardest responses is also costly; therefore choose physical tests adaptively.\n- **Hidden-slide decision:** **Merge slide 6 into slide 4**, rather than unhide it as written. Keep a fuller evidence-backed simulation/experiment comparison as backup.\n\n---\n\n# P6. The friendly industry generalist\n\n## (a) My reaction in the room\n\nThe opening video is memorable, and I like the practical goal of shortening build\u2013test iteration. Slide 4 is easy to understand. My attention slips on slide 7 because the counts and black-box symbol do not tell a story. I do not need the name qNEHVI, but I need to know that the software balances trying promising designs with learning from uncertain ones and respects a force limit.\n\nSlides 8\u201310 are the strongest run. If the presenter shows one labeled specimen, one clean drop clip, and one loop diagram, I can see how I might use the pattern in another product-development problem. I want a cycle-time number and an indication of what software or human work is required.\n\nSlide 12 dilutes the message by offering several destinations before proving the present result. The ending is weak. A blank Q&A slide discards the chance to leave the audience with the workflow, the best specimen, and a measured improvement.\n\n## (b) What I would tell a colleague the next day\n\n> \u201cPrint it, break or drop it, feed the measurements back, and let the optimizer pick the next batch instead of running a huge test matrix.\u201d\n\nThat captures the intended practical pattern, but I would probably overgeneralize it beyond the specific conditions under which it is useful.\n\n## (c) My top three Q&A questions\n\n1. How many prints and calendar days did it take to reach a useful design compared with your previous workflow?\n2. What parts of this system could another engineering team reuse without building the same drop tower or writing a custom optimizer?\n3. What happens when a print fails or a test produces an outlier?\n\n## (d) My most pointed objection\n\n**I understand the loop, but the deck never gives me the one number or result that proves it was worth building.**\n\nUnanswered, this is **moderately to highly damaging**. I would remember the concept but not have a reason to adopt or sponsor it.\n\n## (e) Slide-deck verdict\n\n- **Slide most needing work:** **Slide 13.** Replace the blank Q&A screen with the best measured result, the closed-loop diagram, and one deployment rule; retain a small \u201cQuestions?\u201d label.\n- **Hidden-slide decision:** **Unhide slide 11** once populated. Results are more valuable than another explanatory slide.\n\n---\n\n# Synthesis\n\n## Cross-persona themes\n\n1. **The scope boundary is missing.** The Super Ball Bot is a good hook, but every persona risks interpreting a PLA\u2013TPU proxy as a claim about flight hardware.\n2. **Slide 7 is the weakest visible technical slide.** It serves neither experts nor novices. Counts such as \u201c6 variables\u201d and \u201c2 objectives\u201d are information, not a message.\n3. **The main claim currently lacks evidence.** \u201cSignificantly accelerate,\u201d \u201csupplies data fast,\u201d and \u201cdozens, not hundreds\u201d require a campaign ledger and a budget-matched comparison.\n4. **The results omission breaks the story.** The deck follows the outline through the workflow, then skips the outline\u2019s largest time block and moves directly to future work.\n5. **The physical system needs more definition.** The audience needs a labeled specimen, a clear explanation of \u201ctensegrity-inspired,\u201d interface design, remaining manual steps, and the measurement chain.\n6. **Noise must be operational rather than rhetorical.** Saying the data are noisy is insufficient. Replicates, periodic controls, drift, failures, and uncertainty should be shown.\n7. **Experiment-first needs a scoped rationale.** The deck should not dismiss FEA, but it should explain why adaptive physical testing is the economical source of objective values for this campaign.\n8. **The close is not a conclusion.** Future applications and a blank Q&A slide do not substitute for the measured finding and its conditions of transfer.\n9. **Media are a communication and schedule risk.** Short clips can work, but multiple looping videos violate the signal-to-noise principle and create failure modes.\n10. **The project specification needs reconciliation.** The supplied materials differ on six versus seven variables, whether FDM process parameters are optimized, and whether peak force is a constraint or part of the Pareto trade-off.\n\n## Five highest-priority slide-level revisions\n\n### 1. Slide 11: replace one empty slot with a three-slide quantitative results sequence\n\nUse full-sentence titles tied to observed data:\n\n- **11A:** \u201cThe campaign used **N** specimens in **B** adaptive batches, including **R** replicates and **F** failed prints.\u201d\n- **11B:** \u201cAt the same physical-test budget, adaptive selection reached **[measured feasible-performance result]** faster than **[predeclared Sobol/random baseline]**.\u201d\n- **11C:** \u201cThe measured feasible Pareto front trades specific energy absorption against compaction efficiency while respecting a **[value and unit]** peak-force cap.\u201d\n\nShow uncertainty, replicate spread, infeasible points, and representative specimen photographs or traces. If the campaign cannot support the baseline claim, change the talk\u2019s main message to: **\u201cWe evaluate whether noise-aware multi-objective Bayesian optimization can reduce the physical-test budget.\u201d**\n\n### 2. Slide 7: state the actual constrained optimization problem\n\nReplace the current black-box/count design with:\n\n- the named decision variables and their types;\n- a small batch entering the printer/test loop;\n- objectives: maximize specific energy absorption and compaction efficiency;\n- constraint: peak transmitted force below a stated cap;\n- qNEHVI\u2019s role in one plain sentence: it chooses a small next batch likely to expand the feasible trade-off frontier while accounting for uncertainty and noisy tests;\n- the declared test budget, initialization size, and batch size.\n\nResolve six versus seven variables and whether processing parameters are decision variables before finalizing this slide.\n\n### 3. Slide 2: put the proxy boundary beside the hook\n\nKeep the Super Ball Bot clip, but add an image of the actual specimen and the visible sentence:\n\n> **\u201cHere, a printed PLA\u2013TPU T3 prism is a proxy for developing the workflow, not flight hardware.\u201d**\n\nChange or qualify \u201crobust, reusable solutions\u201d unless the present campaign includes repeat-impact evidence.\n\n### 4. Slide 4: turn the vague need into the two-sided engineering decision\n\nRetitle it:\n\n> **\u201cBecause exhaustive testing and campaign-grade model calibration are both costly, each physical specimen must be chosen for its information value.\u201d**\n\nAdd one quantified physical-search estimate and one scoped calibration challenge. Do not say simulation is untrustworthy. State that FEA and lower-fidelity models remain possible screening or future multi-fidelity tools.\n\n### 5. Slides 10 and 13: separate the demonstrated loop from the conclusion\n\n- **Slide 10:** retitle to **\u201cThe closed loop turns each measured test into the next small batch of specimens.\u201d** Remove \u201csignificantly accelerate\u201d until the results demonstrate it. Mark automated and manual stages.\n- **Slide 13:** retain the closed-loop thumbnail, add the best quantitative result, and state the deployment rule: **use the loop when tests are authoritative, each test is costly, the design space is mixed, and performance goals conflict.** Add \u201cQuestions?\u201d without clearing the conclusion from the screen.\n\n## Hidden-slide adjudication\n\n| Hidden slide | Recommendation | Rationale |\n|---|---|---|\n| **3: tensegrity usefulness/mechanism** | **Merge into slide 8** | The generic elbow and squishing GIFs add noise and risk an inaccurate mechanism; an annotated actual specimen and deformation sequence are stronger. |\n| **5: generic BO explanation** | **Merge into slide 7; keep original as backup or delete** | DAC experts do not need a generic advocacy slide, but novices need one plain-language sentence and a concrete acquisition visual. |\n| **6: specimen information value/experiment-first** | **Merge into slide 4; keep evidence-rich version as backup** | The argument is structurally necessary, but the current title is vague and the visual does not support the scoped calibration-versus-campaign-cost decision. |\n| **11: results** | **Unhide and expand to three slides** | Results are the evidentiary center of the talk and were allocated five minutes in the agreed outline. An empty or hidden results slot makes the main claim untenable. |\n\n## Visible-slide claims needing evidence, hedging, or backup\n\n| Slide | Claim | Required treatment |\n|---|---|---|\n| **1** | \u201cBuild better tensegrity structures faster\u201d | Define \u201cbetter\u201d with the two objectives and force constraint; support \u201cfaster\u201d with the results comparison. |\n| **2** | Tensegrity is robust and reusable for planetary landings | Cite the Super Ball Bot source; distinguish concept evidence from this study; provide repeat-impact evidence or hedge reusability. |\n| **2** | Parachutes and retrorockets struggle in the stated conditions | Cite an authoritative aerospace source if retained in narration; avoid implying universal inadequacy. |\n| **4** | Current tensegrity design is slow and resource intensive | Quantify candidate count, print/test time, hand-assembly burden, or calibration effort. |\n| **7** | BO makes experiments affordable with limited, noisy data | Show campaign budget, noise characterization, replicates, baseline, and performance versus tests. Prepare qNEHVI and Gaussian-process details in backup. |\n| **7** | Six variables, two input-data types, two objectives | Reconcile with the apparent seven-variable abstract specification and identify every quantity. Clarify \u201cinput-data types.\u201d |\n| **8** | Multi-material AM supplies data fast | Report print-to-result cycle time and failure rate. |\n| **8** | Single-build co-fabrication with no joining | Show interface and post-processing; use \u201cno joining of PLA and TPU members\u201d if other manual operations remain. |\n| **9** | Accelerometers gather the relevant impact data | Show sensor locations and the derivation of transmitted force, specific energy absorption, and compaction efficiency. Reconcile this wording with the abstract\u2019s instrumented-tup description. |\n| **10** | The loop significantly accelerates optimization | Remove \u201csignificantly\u201d unless a defined baseline and uncertainty support it. Statistical significance should not be implied casually. |\n| **12** | Workflow will transfer to lattices and other applications | Present as conditional future work with transfer criteria, not an achieved result. Attribute any external crutch-tip image and claim. |\n\n### Prepared backup slides\n\n1. Full design-variable table, bounds, encoding, and fixed versus optimized process parameters.\n2. qNEHVI implementation, Gaussian-process assumptions, constraint treatment, batch size, and acquisition settings.\n3. Noise plan: replicates, periodic control, heteroscedasticity, drift, outlier policy, and uncertainty propagation.\n4. Budget-matched baseline definition and evaluation metric.\n5. Force-cap rationale and full objective definitions.\n6. Sensor chain, calibration, sample rate, filtering, fixture dynamics, and metric extraction.\n7. PLA\u2013TPU interface, conditioning, build layout, dimensional/mass deviations, and print-failure modes.\n8. Scoped FEA rationale with citations or internal validation; possible analytical or multi-fidelity extensions.\n9. Automation map and cycle-time breakdown.\n10. Repeated-impact behavior if reusability remains in the opening.\n\n## Merged, ordered presenter TODO list\n\n### Must fix before the talk\n\n1. **Freeze the scientific specification.** Reconcile the number and type of decision variables, whether FDM parameters are optimized or controlled, the two objectives, the force constraint, treatment of print failures, and the measurement chain. Use one formulation across slides, abstract, paper, and Q&A.\n2. **Set a results completion plan.** Establish dates and owners for campaign completion, repeat tests, data quality review, baseline analysis, and slide freeze. Predeclare the fallback claim if the baseline does not support \u201cdozens rather than hundreds.\u201d\n3. **Build the three-slide results sequence.** Campaign ledger \u2192 budget-matched baseline \u2192 measured feasible Pareto front. Allocate approximately five minutes, as the agreed outline requires.\n4. **Add the proxy statement to slide 2.** State plainly that the PLA\u2013TPU T3 specimen is a workflow proxy and not flight hardware.\n5. **Redesign slide 7.** Show the exact constrained multi-objective problem, test budget, small batches, uncertainty, noisy observations, and the role of qNEHVI.\n6. **Remove unsupported result language.** Until supported, delete \u201csignificantly\u201d from slide 10 and avoid \u201cdozens rather than hundreds\u201d in the title, narration, or close.\n7. **Clarify experiment-first positioning.** Merge the information-value argument into slide 4 and prepare an evidence-backed FEA backup slide.\n8. **Make the experiment auditable.** On slide 9 or backup, reconcile accelerometers and the instrumented tup; show how all three performance metrics are calculated and how uncertainty is handled.\n9. **De-risk every video.** Store local trimmed files, disable distracting loops, test them on presentation hardware, and include static fallback frames.\n10. **Rehearse to 13:30\u201314:00.** Protect the results and conclusion from being cut. Practice once without slides, consistent with Doumont\u2019s preparation advice.\n\n### Should fix\n\n11. **Replace slide 8\u2019s generic print emphasis with mechanism and materials evidence.** Label PLA, TPU, interface strategy, prestress status, deformation, post-processing, and failure modes.\n12. **Quantify slide 4.** Give at least one campaign-relevant cost, duration, or candidate-count estimate.\n13. **Show automation boundaries on slide 10.** Candidate selection and surrogate updating may be automated; slicing, handling, test setup, and testing remain manual according to the abstract.\n14. **Convert slide 12 to a gated roadmap.** Separate demonstrated T3-proxy results from future lattices, flight materials, and terrestrial applications.\n15. **Turn slide 13 into the conclusion.** Leave the audience with the best result and transfer rule while taking questions.\n16. **Prepare technical backup slides.** Prioritize baseline fairness, noise, force-cap rationale, print failures, materials repeatability, and FEA positioning.\n\n### Polish\n\n17. Replace the slide 1 title with a specific message, such as **\u201cPhysical Bayesian optimization searches multi-material tensegrity absorbers one small test batch at a time.\u201d**\n18. Left-align titles and use intentional line breaks; check every slide at six-per-page scale.\n19. Use one purposeful visual per slide. Avoid decorative loading or elbow animations.\n20. Standardize terminology: \u201cBayesian optimization,\u201d not \u201cBays Opt\u201d; \u201ctensegrity-inspired\u201d where appropriate; define specific energy absorption once.\n21. Add citations directly beside borrowed footage, images, and quantitative claims.\n\n## Who the deck serves best and worst\n\n- **Best served:** **P6, the friendly industry generalist.** The visible sequence is visually intuitive, practical, and easy to reduce to \u201cprint \u2192 test \u2192 learn \u2192 repeat.\u201d\n- **Worst served:** **P1, the skeptical BO insider.** The current deck omits nearly every item needed to judge a DAC-10 contribution: exact problem formulation, budget, baseline, noise model, constraint handling, and empirical adaptive advantage.\n\nThat is **not the right trade-off for DAC-10**. The deck should remain accessible to P4 and P6, but its technical center must satisfy P1 without turning into a BO lecture. One rigorous optimization slide, three quantitative results slides, and targeted backup material would achieve that balance.\n\n## Predicted reception\n\n- **As is: 4/10.** The physical workflow and application are memorable, but the absent scope statement, empty results section, vague BO slide, and unsupported acceleration claim leave the DAC contribution unproven.\n- **After the must-fixes: 8/10.** A scoped proxy claim, reconciled methods, fair budget-matched comparison, feasible Pareto front, and measured conclusion would turn the same visual story into a credible design-optimization application talk.\n\n## Discretionary analytical decisions\n\n- Evaluated only the visible slide order for the simulated in-room reactions, while using hidden slides and presenter notes to judge revision options.\n- Treated planned placeholder visuals as if implemented according to the presenter\u2019s notes, but judged the selected visual concept and associated stage risk rather than absent graphic quality.\n- Prioritized the Draft 3 outline over conflicting Draft 1 wording because the prompt identifies it as the agreed story arc, scope framing, results plan, and timing plan.\n- Rated objections qualitatively as moderate, high, or critical rather than assigning unsupported numerical probabilities.\n- Recommended three results slides rather than preserving one reserved slot because the agreed outline specifies three distinct evidentiary products and allocates five minutes to results.\n- Recommended merging hidden slides 3, 5, and 6 rather than simply unhiding them because their necessary ideas can be conveyed with higher signal-to-noise on existing slides.\n- Treated the six-versus-seven-variable and optimized-versus-fixed-process-parameter differences as unresolved specification conflicts requiring reconciliation, rather than inferring which document is correct.\n- Assessed claims against the supplied deck extraction, Draft 3 outline, Doumont notes, and submitted abstract only; no independent validation of cited literature or unseen experimental data was attempted.", + "done": true, + "nb_state": { + "nbformat": 4, + "nbformat_minor": 5, + "metadata": { + "kernelspec": { + "name": "python", + "display_name": "Python 3 (ipykernel)", + "language": "python" + } + }, + "cells": [] + }, + "nb_runtime_errors": [] + }, + "info": { + "language": "python", + "problem": "You are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "workspace_dir": "/workspace", + "work_dir": "/workspace", + "input_data": [ + { + "entry_id": "2a03725b-2a56-4bfe-a6c8-8ac23e2a8186", + "filename": "slide-deck-draft1-extracted.md" + }, + { + "entry_id": "77b29c6d-b9dd-477d-85f4-b9f2aa2abcc3", + "filename": "doumont-video-notes.md" + }, + { + "entry_id": "80397667-b56a-407d-8c4e-cb1c1e9b986c", + "filename": "idetc-abstract.tex" + }, + { + "entry_id": "9cddb197-eeea-4831-8e3d-6dcff2abee97", + "filename": "doumont-presentation-template.md" + } + ], + "output_data": [ + { + "entry_id": "afade0b1-f49e-4031-a86d-cb5ec4652536-ipynb-ts40", + "filename": "afade0b1-f49e-4031-a86d-cb5ec4652536.ipynb", + "file_size": 184 + }, + { + "entry_id": "afade0b1-f49e-4031-a86d-cb5ec4652536-memory-her-z19x", + "filename": "afade0b1-f49e-4031-a86d-cb5ec4652536_memory_heron_c0_20260726T022908996752.jsonl", + "file_size": 144624 + } + ] + } + }, + "supplemental_data": null, + "created_at": "2026-07-26T02:32:02.064656Z" + }, + "metadata": null, + "deployment_config": null, + "failure_reason": null, + "task": "You are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nThis time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just\nan outline. Attached files:\n1. slide-deck-draft1-extracted.md \u2014 a faithful slide-by-slide extraction of the\n PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide\n text (slide titles are full-sentence messages per Doumont), an inventory of\n non-text shapes/media/placeholders, and the presenter's own notes (which\n include the presenter's candid comments about intended changes and open\n questions \u2014 treat those as the presenter's current thinking). Many visuals\n are placeholders the presenter plans to fill; judge the plan, not the\n missing pixels, but DO flag placeholder choices you think are wrong.\n2. doumont-presentation-template.md \u2014 the Draft 3 outline the deck is built\n from (the agreed story arc, scope/proxy framing, results plan, timing plan).\n Use it to spot where the deck diverges from the plan.\n3. doumont-video-notes.md \u2014 the presenter's notes on Jean-luc Doumont's\n presentation principles (messages not words, adapt to audience, one message\n per slide, signal-to-noise). Part of the evaluation rubric.\n4. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding (closed-loop Bayesian optimization of multi-material 3D-printed\n tensegrity-inspired energy absorbers, optimized directly from physical\n impact tests; qNEHVI; objectives = specific energy absorption and\n compaction efficiency subject to a peak transmitted-force cap).\n\nContext: the presenter is an undergraduate researcher; the PI is the project\nlead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes\nthe \"specimen information value / experiment as source of truth\" argument and\nslide 11 is the reserved results slot.\n\nSimulate SEVEN personas. NEW this round, and to be treated as first among\nequals:\n\nP0. \"The program manager in the audience\" \u2014 a mock PM/project manager (think:\n research program manager at a national lab or aerospace prime who funds\n projects like this one; also plays the internal-stakeholder role of the\n project's own PM sitting in the audience). Moderate technical literacy in\n all three base techniques, expert in schedule/scope/risk. Watches for:\n Does the talk state scope honestly (proxy system vs. flight hardware)?\n Is there a credible plan for the empty results slot given the conference\n date? Are claims traceable to evidence? What is the risk register for this\n talk (what could go wrong on stage: missing results, video failures,\n overtime)? Would they fund the next phase after seeing it? Their feedback\n should include a concrete slide-level punch list ordered by\n schedule-criticality (what MUST be fixed before the talk vs. nice-to-have).\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY\n SKEPTICAL: default assumption is \"off-the-shelf qNEHVI applied to yet\n another application.\" Probes methodological novelty, budget, baselines,\n noise handling \u2014 and now also whether the deck's BO slides (5 hidden, 7\n visible) say anything an expert respects.\nP2. \"The aerospace practitioner\" \u2014 senior EDL engineer; deep in impact\n attenuation and lander hardware; no BO/ML background; knows tensegrity\n from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused.\nP3. \"The AM/materials researcher\" \u2014 multi-material FDM and elastomer printing\n expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU\n bonding, repeatability, and whether \"single-build co-fabrication\" holds.\nP4. \"The first-year grad student\" \u2014 new to all three techniques. ENTHUSIASTIC.\n Tests followability slide by slide: where does the deck lose a novice,\n which jargon lands unexplained, do the hidden slides' absence break the\n story?\nP5. \"The FEA veteran\" \u2014 25 years of finite-element work. DEFENSIVE AND\n SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide\n 6 hidden) still makes a defensible case for experiment-first.\nP6. \"The friendly industry generalist\" \u2014 design engineer scouting methods.\n SUPPORTIVE, limited attention; tests memorability and the practical\n so-what.\n\nFor EACH persona produce:\n(a) a first-person reaction to sitting through the deck as it stands (visible\n slides in order, placeholders imagined as the presenter's notes describe\n them): what landed, what confused, where attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting distortion from the intended message (\"By closing\n the loop between multi-material 3D printing and Bayesian optimization, we\n can optimize tensegrity energy absorbers directly from real impact data \u2014\n in dozens of prints, not hundreds\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection, and how damaging it is unanswered;\n(e) NEW: their verdict on the slide deck specifically (vs. the outline):\n which single slide most needs work, and which hidden slide (3, 5, 6, 11)\n they would unhide or keep hidden, and why.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes;\n- the 5 highest-priority slide-level revisions, concretely worded (slide\n number + exact change);\n- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's\n recommendation (unhide / merge into another slide / keep as backup) with\n one-line rationale;\n- claims on visible slides needing evidence, hedging, or a prepared backup\n slide for Q&A;\n- the PM's punch list reconciled with the technical personas' asks: one merged,\n ordered TODO list for the presenter (must-fix before talk / should-fix /\n polish);\n- which persona the deck currently serves best and worst, and whether that is\n the right trade-off for DAC-10;\n- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes,\n with one-sentence rationale each.\n\nWrite the whole result as a well-structured markdown report.\n", + "started_at": "2026-07-26T02:29:05.582160Z", + "crow": "job-futurehouse-data-analysis-crow-high", + "notification_enabled": false, + "notification_type": null, + "continued_trajectory_id": null, + "is_trajectory_owner": true, + "gcloud_operation_name": "sandbox://afade0b1-f49e-4031-a86d-cb5ec4652536/sandbox-claim-ee3bc216/job-futurehouse-data-analysis-crow-high-agent-warmpool-t497h", + "task_summary": "Simulate Seven Persona Critiques Of Draft Conference Slide Deck For Bayesian Optimization Of Multi\u2011Material Energy Absorbers", + "id": "afade0b1-f49e-4031-a86d-cb5ec4652536" +} \ No newline at end of file diff --git a/presentation/edison-mock-audience/slide-review-trajectory-files.json b/presentation/edison-mock-audience/slide-review-trajectory-files.json new file mode 100644 index 00000000..48e3078e --- /dev/null +++ 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Requires EDISON_PLATFORM_API_KEY in the environment. +Waiting/fetching is done by wait_fetch_slide_review.py (single blocking call). +""" + +import os +from pathlib import Path + +from edison_client import EdisonClient, JobNames +from edison_client.models import RuntimeConfig, TaskRequest + +REPO = Path(__file__).resolve().parents[2] +OUTDIR = REPO / "presentation" / "edison-mock-audience" +OUTDIR.mkdir(parents=True, exist_ok=True) + +FILES = [ + str(OUTDIR / "slide-deck-draft1-extracted.md"), + str(REPO / "presentation" / "doumont-presentation-template.md"), + str(REPO / "presentation" / "doumont-video-notes.md"), + str(REPO / "idetc-abstract.tex"), +] + +QUERY = """\ +You are simulating a mock conference audience for a ~15-minute technical talk at +IDETC-CIE 2026 (ASME International Design Engineering Technical Conferences), +Design Automation Conference track (DAC-10: design optimization applications). + +This time the artifact under review is an ACTUAL SLIDE DECK (Draft 1), not just +an outline. Attached files: +1. slide-deck-draft1-extracted.md — a faithful slide-by-slide extraction of the + PowerPoint: layout used, whether the slide is currently HIDDEN, all on-slide + text (slide titles are full-sentence messages per Doumont), an inventory of + non-text shapes/media/placeholders, and the presenter's own notes (which + include the presenter's candid comments about intended changes and open + questions — treat those as the presenter's current thinking). Many visuals + are placeholders the presenter plans to fill; judge the plan, not the + missing pixels, but DO flag placeholder choices you think are wrong. +2. doumont-presentation-template.md — the Draft 3 outline the deck is built + from (the agreed story arc, scope/proxy framing, results plan, timing plan). + Use it to spot where the deck diverges from the plan. +3. doumont-video-notes.md — the presenter's notes on Jean-luc Doumont's + presentation principles (messages not words, adapt to audience, one message + per slide, signal-to-noise). Part of the evaluation rubric. +4. idetc-abstract.tex — the submitted conference abstract, for technical + grounding (closed-loop Bayesian optimization of multi-material 3D-printed + tensegrity-inspired energy absorbers, optimized directly from physical + impact tests; qNEHVI; objectives = specific energy absorption and + compaction efficiency subject to a peak transmitted-force cap). + +Context: the presenter is an undergraduate researcher; the PI is the project +lead. Four slides are currently HIDDEN (3, 5, 6, 11); hiding slide 6 removes +the "specimen information value / experiment as source of truth" argument and +slide 11 is the reserved results slot. + +Simulate SEVEN personas. NEW this round, and to be treated as first among +equals: + +P0. "The program manager in the audience" — a mock PM/project manager (think: + research program manager at a national lab or aerospace prime who funds + projects like this one; also plays the internal-stakeholder role of the + project's own PM sitting in the audience). Moderate technical literacy in + all three base techniques, expert in schedule/scope/risk. Watches for: + Does the talk state scope honestly (proxy system vs. flight hardware)? + Is there a credible plan for the empty results slot given the conference + date? Are claims traceable to evidence? What is the risk register for this + talk (what could go wrong on stage: missing results, video failures, + overtime)? Would they fund the next phase after seeing it? Their feedback + should include a concrete slide-level punch list ordered by + schedule-criticality (what MUST be fixed before the talk vs. nice-to-have). + +P1. "The skeptical BO insider" — design-automation professor, 15 years in + surrogate-based and Bayesian optimization; reviews for DAC. HIGHLY + SKEPTICAL: default assumption is "off-the-shelf qNEHVI applied to yet + another application." Probes methodological novelty, budget, baselines, + noise handling — and now also whether the deck's BO slides (5 hidden, 7 + visible) say anything an expert respects. +P2. "The aerospace practitioner" — senior EDL engineer; deep in impact + attenuation and lander hardware; no BO/ML background; knows tensegrity + from the Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused. +P3. "The AM/materials researcher" — multi-material FDM and elastomer printing + expert; novice in BO and tensegrity. NEUTRAL/CURIOUS; probes PLA-TPU + bonding, repeatability, and whether "single-build co-fabrication" holds. +P4. "The first-year grad student" — new to all three techniques. ENTHUSIASTIC. + Tests followability slide by slide: where does the deck lose a novice, + which jargon lands unexplained, do the hidden slides' absence break the + story? +P5. "The FEA veteran" — 25 years of finite-element work. DEFENSIVE AND + SKEPTICAL of any simulation-dismissal; checks whether the deck (with slide + 6 hidden) still makes a defensible case for experiment-first. +P6. "The friendly industry generalist" — design engineer scouting methods. + SUPPORTIVE, limited attention; tests memorability and the practical + so-what. + +For EACH persona produce: +(a) a first-person reaction to sitting through the deck as it stands (visible + slides in order, placeholders imagined as the presenter's notes describe + them): what landed, what confused, where attention drifted; +(b) the main message as they would repeat it to a colleague the next day, in + their own words — noting distortion from the intended message ("By closing + the loop between multi-material 3D printing and Bayesian optimization, we + can optimize tensegrity energy absorbers directly from real impact data — + in dozens of prints, not hundreds"); +(c) their top 3 Q&A questions, in character; +(d) their single most pointed objection, and how damaging it is unanswered; +(e) NEW: their verdict on the slide deck specifically (vs. the outline): + which single slide most needs work, and which hidden slide (3, 5, 6, 11) + they would unhide or keep hidden, and why. + +Then produce a SYNTHESIS section: +- cross-persona themes; +- the 5 highest-priority slide-level revisions, concretely worded (slide + number + exact change); +- a hidden-slide adjudication: for each of slides 3, 5, 6, 11, the panel's + recommendation (unhide / merge into another slide / keep as backup) with + one-line rationale; +- claims on visible slides needing evidence, hedging, or a prepared backup + slide for Q&A; +- the PM's punch list reconciled with the technical personas' asks: one merged, + ordered TODO list for the presenter (must-fix before talk / should-fix / + polish); +- which persona the deck currently serves best and worst, and whether that is + the right trade-off for DAC-10; +- predicted overall reception on a 1-10 scale, as-is vs. after must-fixes, + with one-sentence rationale each. + +Write the whole result as a well-structured markdown report. +""" + + +def main() -> None: + api_key = os.environ.get("EDISON_PLATFORM_API_KEY") or os.environ["EDISON_API_KEY"] + client = EdisonClient(api_key=api_key) + + # Official upload flow per + # https://docs.edisonscientific.com/edison-client/file-management#upload + storage_uris = [] + for path in FILES: + resp = client.store_file_content( + name=Path(path).name, + file_path=path, + description="Input document for IDETC slide-deck mock-audience review", + ) + storage_uris.append(f"data_entry:{resp.data_storage.id}") + print(f"uploaded {Path(path).name} -> {resp.data_storage.id}", flush=True) + + task_data = TaskRequest( + name=JobNames.ANALYSIS, + query=QUERY, + runtime_config=RuntimeConfig( + environment_config={"data_storage_uris": storage_uris}, + ), + ) + task_ids = client.create_task(task_data) + task_id = task_ids[0] if isinstance(task_ids, (list, tuple)) else task_ids + (OUTDIR / "slide-review-task-id.txt").write_text(str(task_id) + "\n") + print(f"submitted task {task_id}", flush=True) + + +if __name__ == "__main__": + main() diff --git a/presentation/edison-mock-audience/task-id.txt b/presentation/edison-mock-audience/task-id.txt new file mode 100644 index 00000000..2f5d71e7 --- /dev/null +++ b/presentation/edison-mock-audience/task-id.txt @@ -0,0 +1 @@ +4949af58-f08a-4541-ad1c-1f0a95c6f07d diff --git a/presentation/edison-mock-audience/task-response.json b/presentation/edison-mock-audience/task-response.json new file mode 100644 index 00000000..81d50ac8 --- /dev/null +++ b/presentation/edison-mock-audience/task-response.json @@ -0,0 +1,5097 @@ +{ + "status": "success", + "query": "You are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "user": null, + "created_at": "2026-07-18T17:54:12.487473Z", + "job_name": "job-futurehouse-data-analysis-crow-high", + "share_status": "private", + "permitted_accessors": { + "users": [], + "organizations": [] + }, + "build_owner": "FutureHouse", + "environment_name": null, + "agent_name": null, + "task_id": "4949af58-f08a-4541-ad1c-1f0a95c6f07d", + "project_id": null, + "agent_state": [ + { + "id": "49144dc1-6858-4cd2-905f-39deaaa5b2f0", + "trajectory_id": "4949af58-f08a-4541-ad1c-1f0a95c6f07d", + "trajectory_timestep": 1, + "step": "after_transition", + "state": { + "transition": { + "timestep": 1, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "info": null + }, + { + "role": "assistant", + "content": "I\u2019ll evaluate the outline as a delivered 15-minute talk, treating the unfinished results section as a substantive gap rather than inventing outcomes. I\u2019ll score each persona against comprehension, credibility, relevance, and recall, then synthesize revisions for DAC-10.", + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "info": null + }, + { + "role": "assistant", + "content": "I\u2019ll evaluate the outline as a delivered 15-minute talk, treating the unfinished results section as a substantive gap rather than inventing outcomes. I\u2019ll score each persona against comprehension, credibility, relevance, and recall, then synthesize revisions for DAC-10.", + "info": null + }, + { + "role": "user", + "content": "\nMessage was NOT sent to user. Please call the send_message tool to communicate with the user. Use wait=True if you need a response before proceeding, or wait=False if you are providing a status update to the user.", + "info": { + "is_human": false + } + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_zSHcqZDq4Rrb984ui6lpQugL", + "type": "function", + "function": { + "arguments": "{\"answer\": \"# Mock audience report: IDETC-CIE 2026 DAC-10 talk\\n\\n## Scope of this review\\n\\nThis evaluates the talk implied by the outline, not finished slides or delivery technique. The experimental campaign is still in progress, so I do **not** infer any favorable results. The empty \\u201cEvidence / results\\u201d section is treated as a major unresolved part of the talk: the outline currently explains why the workflow should be useful, but does not yet demonstrate that it is useful.\\n\\nThe central communication test is whether each listener leaves with the intended message:\\n\\n> By closing the loop between multi-material 3D printing and Bayesian optimization, we can optimize tensegrity energy absorbers directly from real impact data, in dozens of prints rather than thousands.\\n\\n---\\n\\n# P1. The skeptical Bayesian-optimization insider\\n\\n## (a) First-person reaction\\n\\nThe application is potentially interesting, and the design\\u2013print\\u2013test loop is easy to understand. The mixed continuous, integer, and categorical design space, physical failures, parallel fabrication, and multiple constrained objectives could make this a worthwhile design-automation case study.\\n\\nBut the outline keeps telling me that Bayesian optimization is sample-efficient without showing that **this implementation** is sample-efficient. \\u201cDozens, not thousands\\u201d sounds like a slogan unless I see the actual experiment count, initialization size, batch size, replication policy, convergence behavior, and a comparison against credible alternatives. q-noisy expected hypervolume improvement, or qNEHVI, is established machinery. Merely deploying it does not establish methodological novelty.\\n\\nI become skeptical at the statement that qNEHVI was selected because of heteroscedastic TPU and print noise. qNEHVI accounts for noisy observations, but the outline does not explain how the surrogate models heteroscedasticity, whether repeated specimens identify noise, or whether noise is being conflated with unmodeled process drift. Independent Gaussian processes with an unspecified observation model do not automatically resolve batch-dependent heteroscedasticity.\\n\\nMy attention drifts during the broad argument against simulation because it delays the information I came to DAC-10 to hear: the design variables, model formulation, constraint handling, acquisition implementation, budget, and benchmark. The Super Ball Bot hook is visually effective, but planetary landing may feel like application theater if the actual specimens and impact regime are far removed from that use case.\\n\\nThe strongest possible section is the results slot. In its current empty state, the talk has a method narrative but no validation.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey used standard batch noisy multi-objective Bayesian optimization to choose which multi-material printed impact specimens to test, instead of running a full physical sweep.\\u201d\\n\\n**Distortion from the intended message:** I would omit the claim that the structures were optimized in dozens of prints because I have not seen evidence for it. I would also describe qNEHVI as standard rather than as the contribution.\\n\\n## (c) Top three Q&A questions\\n\\n1. **What is the actual experimental budget?** How many initial designs, adaptive iterations, specimens per batch, and physical replicates were used, and what stopping rule defined success?\\n2. **What baselines did you beat?** Did you compare qNEHVI with Sobol or Latin-hypercube sampling, random search, scalarized Bayesian optimization, or a domain-informed sequential design under the same number of physical tests?\\n3. **How exactly is noise modeled?** Are replicate-dependent variances supplied to the Gaussian processes, is a heteroscedastic likelihood fitted, and how are TPU lot, print batch, machine drift, and specimen-level defects distinguished?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhat did you contribute beyond applying off-the-shelf qNEHVI to a new specimen family?\\u201d\\n\\n**Damage if unanswered: severe.** For a DAC review audience, this can reduce the work from a design-automation contribution to an application demonstration. A strong answer could rest on the experimental formulation, mixed-variable and failure-aware implementation, quantitative sample-efficiency evidence, and transferable operating lessons. Those contributions must be stated explicitly and tested.\\n\\n---\\n\\n# P2. The aerospace entry, descent, and landing practitioner\\n\\n## (a) First-person reaction\\n\\nThe opening lands immediately. I recognize the Super Ball Bot concept, and \\u201cthe lander is the energy absorber\\u201d is a useful framing. The three performance quantities also sound relevant: energy absorbed per mass, usable stroke, and force transmitted to the payload.\\n\\nThen the talk moves into language I do not naturally use: Gaussian-process surrogate, qNEHVI, acquisition function, hypervolume, exploration versus exploitation, and Pareto front. I can accept that the algorithm chooses the next tests, but I need a plain explanation of what information it uses and why I should trust the recommendations. If the presenter spends several minutes naming algorithms without connecting them to engineering decisions, I will lose the thread.\\n\\nMy main concern is application fidelity. A desktop drop-weight test at one fixed impact energy is not automatically evidence for entry, descent, and landing hardware. I need specimen dimensions, impact mass, velocity, energy, strain rate, boundary conditions, number of impacts, rebound behavior, temperature, vacuum sensitivity, and failure containment. PLA and TPU raise immediate environmental and qualification questions. I also want to know whether the absorber is reusable or sacrificial.\\n\\nMy attention returns when I see force\\u2013time traces, high-speed footage, and damaged specimens. It drifts if the talk stays on the optimizer rather than connecting each output to payload protection.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey have an algorithm that learns from successive drop tests and tells them which 3D-printed crush structure to make next, so they can find a lightweight low-force design with fewer prototypes.\\u201d\\n\\n**Distortion from the intended message:** I would probably call the structures \\u201ccrush structures,\\u201d losing the tensegrity distinction. I might also overgeneralize the method to planetary landers even though the current tests appear to be laboratory-scale.\\n\\n## (c) Top three Q&A questions\\n\\n1. What impact mass, velocity, energy, and strain-rate regime do the tests cover, and how do those values compare with a credible lander load case?\\n2. How do the selected designs scale with size and impact energy? Are the controlling mechanisms material-rate effects, geometric buckling, contact, or all three?\\n3. What happens under off-axis impact, repeated impacts, temperature extremes, aging, vacuum, and manufacturing defects?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhy should I believe that a PLA\\u2013TPU specimen optimized at one laboratory impact condition tells me anything about flight-scale landing protection?\\u201d\\n\\n**Damage if unanswered: severe for the planetary-landing framing, moderate for the underlying optimization study.** The presenter should clearly label planetary landing as motivation, not demonstrated readiness, and state the present technology scope. Otherwise the opening and closing overpromise relative to the reported experiment.\\n\\n---\\n\\n# P3. The additive-manufacturing and materials researcher\\n\\n## (a) First-person reaction\\n\\nThe monolithic PLA\\u2013TPU architecture is the part I care about. The core-wrapping strategy sounds more credible than simply depositing TPU against exposed PLA and assuming durable adhesion. Holding print settings fixed within a batch, weighing specimens, checking dimensions, and periodically testing a control are all sensible.\\n\\nI still need much more fabrication detail before I accept that design geometry is the main source of performance variation. PLA\\u2013TPU interfaces are sensitive to temperature, surface contact, moisture, residence time, toolpath order, contamination, and local geometry. \\u201cPrint defects\\u201d is too broad. I want failure-mode evidence showing whether energy is absorbed through intended tension-network deformation, TPU hysteresis, strut buckling, interface peeling, or accidental damage.\\n\\nThe phrase \\u201cno assembly\\u201d catches my attention, but \\u201celiminates assembly entirely\\u201d may be too strong. Does the build require support removal, trimming, conditioning, fixture insertion, post-tensioning, or manual alignment? If so, the accurate claim is \\u201csingle-build co-fabrication\\u201d or \\u201cno joining of PLA and TPU members,\\u201d not zero assembly.\\n\\nI am a Bayesian-optimization novice. I can follow the loop if the presenter says, \\u201cThe model proposes the next small batch where improvement is plausible or uncertainty is high.\\u201d I will disengage if the slide foregrounds qNEHVI notation rather than the physical decisions it enables.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey co-print rigid PLA and flexible TPU energy absorbers, test them, and use the results to choose the next geometries instead of printing a large design-of-experiments grid.\\u201d\\n\\n**Distortion from the intended message:** I would remember the fabrication loop more strongly than tensegrity or Bayesian optimization. I might interpret the improvement as mostly a material-interface result unless the structural mechanisms are shown.\\n\\n## (c) Top three Q&A questions\\n\\n1. How was PLA\\u2013TPU bonding quantified, and where did failed specimens actually fail: within TPU, within PLA, or at the interface?\\n2. How many nominally identical replicates were printed and tested, and how large were within-batch, between-batch, and printer-drift effects relative to design effects?\\n3. What manual operations occur after printing, and what exactly does \\u201cno assembly\\u201d exclude?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cHow do you know the optimizer is learning architecture rather than uncontrolled interface quality and process drift?\\u201d\\n\\n**Damage if unanswered: severe.** If fabrication variation is not measured and separated from design effects, both the surrogate and the claimed Pareto front may be unstable. Replicates, blocking or covariates, control specimens, dimensional measurements, and failure-mode documentation are needed.\\n\\n---\\n\\n# P4. The first-year graduate student\\n\\n## (a) First-person reaction\\n\\nThe opening video and the phrase \\u201cthe lander is the energy absorber\\u201d give me a concrete picture. I understand the basic problem: too many designs to print, and computer models may miss messy material behavior. I also understand the loop when it is described as \\u201cdesign, print, drop, learn, repeat.\\u201d That story carries me.\\n\\nI start getting lost when several unfamiliar terms arrive together: tensegrity-inspired, compaction efficiency, Gaussian process, surrogate, acquisition function, qNEHVI, heteroscedastic noise, Pareto front, probabilistic feasibility constraint, and exploration versus exploitation. The preview says the audience already believes optimization works, but I still need one intuitive sentence explaining what Bayesian optimization does. Skipping \\u201cBayesian optimization 101\\u201d should not mean skipping the conceptual bridge.\\n\\nI am also unsure how the structure works. Are the TPU parts stretched before impact? Are the PLA struts touching? What makes this \\u201ctensegrity-inspired\\u201d rather than a multi-material lattice? A labeled specimen image and a short deformation sequence would help much more than a verbal definition.\\n\\nMy attention drifts during the repeated case against simulation because I accepted that point the first time. It returns for printing footage, the drop test, and a clear plot with a marked \\u201cbetter\\u201d direction.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey let an artificial-intelligence program learn from 3D-printed drop tests and keep suggesting better shock absorbers, so they do not have to test every design.\\u201d\\n\\n**Distortion from the intended message:** I would probably call Bayesian optimization \\u201cAI,\\u201d omit the two-objective trade-off and force constraint, and assume each new design is simply better than the last. I might also miss what tensegrity contributes.\\n\\n## (c) Top three Q&A questions\\n\\n1. What makes one of these specimens a tensegrity structure rather than an ordinary 3D-printed lattice?\\n2. How does the computer decide which design to print next, in plain language?\\n3. Can one design maximize both energy absorption and compaction efficiency while also lowering peak force, or must those goals trade off?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cI followed the loop, but I still do not understand what is physically special about the structure or what the optimizer actually learned.\\u201d\\n\\n**Damage if unanswered: moderate.** Experts can fill in some gaps, but a talk that leaves novices with only \\u201cAI plus 3D printing\\u201d has failed to communicate its engineering mechanism. One specimen-mechanics visual and one plain-language optimization visual would largely solve this.\\n\\n---\\n\\n# P5. The finite-element-analysis veteran\\n\\n## (a) First-person reaction\\n\\nThe statement \\u201csimulation can\\u2019t be trusted for these structures\\u201d puts me on the defensive before I have seen any evidence. Interfacial slip, viscoelasticity, large deformation, buckling, contact, strain-rate dependence, and manufacturing variability are difficult, but they are not intrinsically beyond finite-element analysis. Models can be calibrated, uncertainty can be propagated, and simulation can still screen designs even if it is imperfect.\\n\\nThe abstract is more defensible than the talk outline. It says the workflow avoids **direct dependence on calibrated finite-element simulation for objective evaluation** and acknowledges future analytical or multifidelity shortcuts. The presentation should use that language. The current outline turns a project-scope choice into a universal technical claim.\\n\\nI accept the economic argument that calibrating a high-fidelity model may not be worthwhile for an early design campaign. That is a stronger position: measured tests provide authoritative objective values, while the study asks whether sequential design can use a small physical budget efficiently. I would also welcome evidence that simulation errors change design rankings or miss observed failure modes. Without such evidence, the repeated attack on simulation sounds like a straw man.\\n\\nMy attention drifts when the talk says simulation is unreliable several times without showing a simulation\\u2013experiment discrepancy. It returns when the presenter discusses physical failure modes and admits where lower-fidelity physics could later improve the loop.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey skipped finite-element modeling and used Bayesian optimization directly on physical tests because they judged model calibration too expensive for these multi-material specimens.\\u201d\\n\\n**Distortion from the intended message:** I would frame the work as bypassing simulation rather than as enabling rapid experimental optimization. If the presenter is combative about simulation, that dispute may become the only thing I remember.\\n\\n## (c) Top three Q&A questions\\n\\n1. What evidence shows that a calibrated finite-element model is insufficient for design ranking, rather than merely costly to construct?\\n2. Did you compare the physical-test-only workflow with a simple mechanics model or multifidelity surrogate that could reduce the experimental budget further?\\n3. Which observed behaviors dominate the model discrepancy: interface failure, TPU viscoelasticity, contact, buckling imperfections, or geometry errors from printing?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cYou have shown that simulation is inconvenient, not that it cannot be trusted. Why is abandoning it scientifically preferable to calibrating and validating it?\\u201d\\n\\n**Damage if unanswered: severe and avoidable.** The current absolute wording invites a technical fight that is unnecessary to the contribution. Reframing the claim as a cost-and-dependence decision would preserve the motivation without asserting that finite-element modeling is incapable.\\n\\n---\\n\\n# P6. The friendly industry generalist\\n\\n## (a) First-person reaction\\n\\nThe loop is memorable: print a candidate, test it, update the model, and print the next informative candidate. I can imagine using that pattern for brackets, seals, lattice pads, or process settings when simulation is weak and tests are expensive. \\u201cDozens rather than thousands\\u201d is exactly the kind of value proposition I remember.\\n\\nI do not need the mathematical details of qNEHVI, but I need an operational recipe. What software is used? How many initial samples are required? How automated is the handoff from optimizer to computer-aided design and slicing? How long does one cycle take? What expertise does a team need? The abstract says candidate selection is automated but slicing, specimen handling, and testing remain manual. That qualification should appear in the talk before I infer a robotic self-driving laboratory.\\n\\nThe tensegrity and planetary material is interesting, but my attention will drift if it occupies too much of a 15-minute slot. The method becomes useful to me only when the presenter explicitly generalizes it and defines when it is worth using.\\n\\nThe ending about moving humankind forward is less memorable than a concrete deployment rule. I would prefer a final slide saying: \\u201cUse this loop when tests are authoritative, each test is costly, the design space is mixed, and several performance goals conflict.\\u201d\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey showed a practical test-driven optimization loop that can find good 3D-printed designs with a few dozen prototypes instead of a giant test matrix.\\u201d\\n\\n**Distortion from the intended message:** I may overstate automation and assume the \\u201cfew dozen\\u201d claim is established across applications. I will probably forget the names qNEHVI and tensegrity, but retain the workflow.\\n\\n## (c) Top three Q&A questions\\n\\n1. What parts of the workflow are automated today, and what does an engineer still do manually between recommendation and test result?\\n2. What minimum experiment budget and software stack would my team need to try this on a different component?\\n3. When is this approach better than a conventional design of experiments, response-surface method, or a calibrated simulation?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhat concrete evidence tells me this saved enough prototypes and engineering time to justify the added optimization infrastructure?\\u201d\\n\\n**Damage if unanswered: moderate to severe.** I will still like the idea, but I will not take it back to my team. A budget-matched baseline and a simple timeline or cost comparison would convert interest into action.\\n\\n---\\n\\n# Synthesis\\n\\n## Cross-persona themes\\n\\n### 1. The causal chain is understandable, but the claimed payoff is not yet demonstrated\\n\\nAll six personas can understand the basic design\\u2013print\\u2013test\\u2013learn loop. The empty results section prevents them from deciding whether the loop actually found better designs, found them faster, or handled noise reliably. The phrase \\u201cdozens, not thousands\\u201d is currently an unsupported quantitative claim.\\n\\nAt minimum, the results need to show:\\n\\n- total number of unique designs and physical specimens;\\n- initial versus adaptively selected designs;\\n- batch size and number of iterations;\\n- replicate policy and failure count;\\n- measured uncertainty or repeatability;\\n- progress under the actual experimental budget;\\n- the final feasible Pareto set for **specific energy absorption and compaction efficiency subject to the force cap**;\\n- a budget-matched nonadaptive or simpler optimization baseline.\\n\\n### 2. \\u201cSimulation can\\u2019t be trusted\\u201d is too absolute\\n\\nP1 questions the evidence, P2 cares about application validity, and P5 directly rejects the premise. Even P6 needs to know when the loop is preferable to simulation. The abstract already contains better wording: physical measurements are used for objective evaluation \\u201cwithout relying on calibrated finite-element simulation.\\u201d\\n\\nThe defensible claim is not that simulation fails universally. It is that a sufficiently calibrated multiphysics model may be expensive relative to the available design campaign, and the authors therefore test whether physical-data-driven sequential optimization is useful without depending on such a model.\\n\\n### 3. The talk needs a two-level explanation of Bayesian optimization\\n\\nP1 does not want introductory Bayesian-optimization material; P2, P3, P4, and P5 need a plain conceptual bridge. These needs are compatible. Give the intuition in one sentence and one visual, then put the implementation specifics in a compact technical panel or backup slide.\\n\\nSuggested spoken explanation:\\n\\n> \\u201cAfter each batch, the model estimates both expected performance and uncertainty across the design space. The acquisition rule selects a small next batch that is most likely to expand the feasible trade-off frontier, while accounting for noisy tests and print failures.\\u201d\\n\\nThis is not \\u201cBayesian optimization 101.\\u201d It is the minimum explanation needed to interpret the workflow.\\n\\n### 4. The physical mechanism and the word \\u201ctensegrity\\u201d need clarification\\n\\nP2 may reduce the objects to crush structures, P3 may interpret the result as an interface study, and P4 may not distinguish the specimens from ordinary lattices. A labeled as-printed specimen plus a four-frame deformation sequence should show:\\n\\n- rigid PLA struts;\\n- continuous flexible TPU network;\\n- whether and how pre-tension exists;\\n- load path before and during impact;\\n- intended energy-dissipation mechanisms;\\n- why \\u201ctensegrity-inspired\\u201d is the precise term.\\n\\n### 5. Manufacturing variability is part of the model, not background noise\\n\\nP1 and P3 will both press this point. Periodically retesting one control is useful but may not identify design-specific heteroscedasticity, lot effects, or confounding between iteration and material batch. The talk needs to state the replication, randomization, blocking, covariate, and drift-monitoring plan. If those steps were not used, the limitation must be explicit.\\n\\n### 6. The application framing outruns the demonstrated scope\\n\\nThe planetary hook is strong, but P2 will not equate fixed-energy laboratory tests on PLA\\u2013TPU specimens with flight hardware. The closing escalates further by claiming that the next lander absorber can be \\u201cproven\\u201d in weeks. That wording should be narrowed unless the campaign includes relevant scaling and environmental evidence.\\n\\n### 7. The objective statement is not fully consistent across the outline\\n\\nThe task and Point 2 correctly describe two objectives, specific energy absorption and compaction efficiency, with a peak-force constraint. The candidate evidence list instead proposes \\u201cSEA vs. peak transmitted force,\\u201d and the abstract\\u2019s expected-outcomes section similarly describes trade-offs between SEA and peak force. Those are different optimization formulations.\\n\\nThe talk must consistently distinguish:\\n\\n- **objectives:** maximize specific energy absorption and compaction efficiency;\\n- **constraint:** peak transmitted force must not exceed a specified cap;\\n- **feasible Pareto front:** trade-offs between the two objectives among designs satisfying that cap.\\n\\nIf peak force is instead treated as a third objective, the task statement and optimization formulation must be revised accordingly.\\n\\n### 8. The closing favors inspiration over technical precision\\n\\nP6 is likely to remember \\u201cdozens, not thousands,\\u201d but P1, P2, and P5 may hear overreach in \\u201cproven,\\u201d \\u201cin weeks,\\u201d and \\u201cmoves humankind forward.\\u201d A DAC-10 close should end on the measured engineering result and transfer condition. The Super Ball Bot bookend can remain, but it should not imply flight qualification.\\n\\n---\\n\\n# Three highest-priority revisions\\n\\n## Priority 1: Build the talk around a quantitative result, not around the availability of a workflow\\n\\nReplace the placeholder with a required three-slide evidence sequence:\\n\\n1. **\\u201cThe campaign used N specimens across B adaptive batches under a fixed physical-test budget.\\u201d** \\n Show initialization, adaptive batches, replicates, failures, elapsed cycle time, and the force threshold. Do not use `N` or `B` in the actual talk; insert the observed values.\\n\\n2. **\\u201cAdaptive selection improved the feasible design set faster than [predeclared baseline].\\u201d** \\n Plot a budget-matched performance measure across physical tests, with uncertainty across repeated runs if available. If only one physical campaign exists, avoid inferential claims that require repeated campaigns and supplement with clearly labeled retrospective resampling or simulation-based algorithm checks.\\n\\n3. **\\u201cThese measured designs form the final feasible trade-off between specific energy absorption and compaction efficiency.\\u201d** \\n Show uncertainty or replicate spread, mark infeasible force-cap violations, and include specimen images or force\\u2013time traces for representative Pareto designs.\\n\\nIf the campaign cannot support those statements, revise the main message from \\u201cwe can optimize \\u2026 in dozens\\u201d to \\u201cwe are evaluating whether noisy multi-objective Bayesian optimization can reduce the physical test budget.\\u201d\\n\\n## Priority 2: Replace the anti-simulation premise with a scoped engineering decision\\n\\nReplace:\\n\\n> \\u201cSimulation can\\u2019t be trusted for these structures.\\u201d\\n\\nwith:\\n\\n> \\u201cFor these multi-material prints, obtaining objective values from a validated high-fidelity model would require substantial calibration of interface, rate, contact, and defect behavior. We therefore ask how far a limited budget of direct physical tests can take us without depending on that model.\\u201d\\n\\nThen title Point 1 with a message rather than a topic:\\n\\n> **\\u201cWhen model calibration and exhaustive testing are both costly, each physical specimen must be chosen for information value.\\u201d**\\n\\nThis keeps the pincer structure while avoiding an unnecessary universal claim.\\n\\n## Priority 3: Reallocate the 15 minutes around audience decisions\\n\\nA concrete timing plan:\\n\\n- **1.5 min:** Super Ball Bot hook, specimen, and present study scope.\\n- **2 min:** Why exhaustive physical search and high-fidelity calibration are costly.\\n- **2.5 min:** One visual of the closed loop, including a one-sentence Bayesian-optimization explanation.\\n- **2 min:** Actual design variables, objectives, force constraint, fabrication failures, and noise controls.\\n- **5 min:** measured results, baseline, uncertainty, representative specimens, and failure modes.\\n- **1 min:** limitations and transfer conditions.\\n- **1 min:** measured conclusion and bookend.\\n\\nMove kernel choices, log-space acquisitions, detailed encoding, and other implementation material to backup slides unless a result depends directly on them. This follows Doumont\\u2019s principle that each slide should communicate one interpreted message rather than expose the speaker\\u2019s notes.\\n\\n---\\n\\n# Claims needing evidence, hedging, or backup slides\\n\\n| Claim or topic | What is needed in the main talk | Prepared backup material |\\n|---|---|---|\\n| \\u201cDozens of prints, not thousands\\u201d | Actual specimen count, definition of success, and budget-matched comparator | Full campaign ledger; sensitivity to initialization and stopping rule |\\n| \\u201cSimulation can\\u2019t be trusted\\u201d | Replace with scoped calibration-cost language unless direct validation evidence exists | Simulation\\u2013experiment traces, ranking errors, or documented failure modes a tested model missed |\\n| \\u201cqNEHVI handles heteroscedastic noise\\u201d | Specify the actual observation-noise model and replication strategy | Likelihood formulation, fixed versus inferred noise, residual diagnostics, replicate variance by design or batch |\\n| qNEHVI \\u201cover standard expected improvement\\u201d | Use a technically appropriate comparison; ordinary expected improvement is not a like-for-like multi-objective batch baseline | Comparisons with random/Sobol, scalarization, qEHVI or another relevant noisy/constrained baseline |\\n| \\u201cThe Pareto front is the design deliverable\\u201d | Show the two stated objectives and force-cap feasibility consistently | Hypervolume reference point, normalization, uncertainty, constraint treatment |\\n| Peak-force cap | State its numerical value and engineering rationale | Sensitivity of selected designs to alternative cap values |\\n| Probabilistic print-feasibility constraint | Report failures and demonstrate that the classifier or constraint model is identifiable at the available sample size | Failure labels, calibration, confusion or reliability metrics, acquisition formula |\\n| \\u201cNo assembly\\u201d / \\u201celiminates assembly entirely\\u201d | List any post-print operations; use \\u201csingle-build co-fabrication\\u201d if manual steps remain | Fabrication workflow photographs and labor-time accounting |\\n| \\u201cCandidate goes to tested specimen in hours\\u201d | Median or representative print-to-result time and what remains manual | Time breakdown for design, slicing, printing, conditioning, inspection, and testing |\\n| \\u201cDramatically faster\\u201d | Define faster relative to a measured or estimated comparator | Cost and wall-clock accounting with assumptions |\\n| Transfer to other architectures | Present as a conditional hypothesis, not a demonstrated result | Applicability checklist: test cost, dimensionality, noise, throughput, and constraint structure |\\n| Planetary-landing relevance | Clearly state that this is motivation unless scale and environment are tested | Impact similitude, energy and velocity regime, environmental gaps, technology-readiness discussion |\\n| PLA\\u2013TPU interface robustness | Failure-mode and repeatability evidence | Microscopy or fracture images, process parameters, conditioning and moisture controls |\\n| \\u201cTensegrity-inspired\\u201d | Show the load path and identify which tensegrity features are retained | Geometric definition, pre-tension status, comparison with a conventional lattice/control |\\n| Independent Gaussian processes | State why cross-objective correlation is ignored and whether that choice matters | Residual correlations and sensitivity to multi-output alternatives |\\n| Mixed categorical variables | Briefly state how candidates are represented and optimized | Kernel or encoding details; treatment of invalid combinations |\\n\\n---\\n\\n# Fit to the six-person audience\\n\\n## Best served: P6, the friendly industry generalist\\n\\nThe outline has a clear problem\\u2013method\\u2013application arc, a memorable loop, and an attractive efficiency promise. P6 can understand the proposed value without needing the mathematics. The danger is that this persona may accept unsupported claims and overestimate the system\\u2019s automation and maturity.\\n\\nP4 also benefits from the story structure, but the unexplained terminology and absent physical-mechanism visual will cause substantial loss of detail.\\n\\n## Worst served: P1, the skeptical Bayesian-optimization insider\\n\\nP1 is the most likely to judge the current talk as an off-the-shelf acquisition function applied to a new artifact. The outline anticipates this concern by saying not to teach Bayesian optimization, but it does not yet supply the evidence P1 needs: novelty positioning, rigorous baseline, budget, noise identification, constraint diagnostics, and measured sample efficiency.\\n\\nP5 is nearly as poorly served because the opening motivation directly challenges finite-element modeling more strongly than the abstract supports.\\n\\n## Is that the right trade-off for DAC-10?\\n\\nNo. A DAC-10 presentation should remain accessible to P2\\u2013P6, but it cannot underserve P1. The design-automation expert is likely to shape the technical discussion and the perceived contribution. Accessibility should come from a plain-language loop and strong visuals, not from omitting methodological validation. The best target is a two-layer talk: every listener can follow the engineering decision, while experts can see the experimental budget, comparator, noise model, and constraint formulation.\\n\\n---\\n\\n# Predicted reception\\n\\n**Current outline: 5/10.** The loop and application are memorable, but without measured results, a budget-matched baseline, and narrower simulation and planetary claims, the talk reads as a polished motivation for an experiment rather than a completed DAC-10 contribution.\\n\\nIf the results establish repeatability and budgeted improvement, and the three priority revisions are made, the same structure could plausibly move into the **7\\u20138/10** range. That conditional estimate is a judgment about presentation readiness, not a prediction of unobserved experimental outcomes.\\n\\n---\\n\\n# Discretionary analytical decisions\\n\\n- Treated the unfinished evidence/results section as missing evidence rather than assuming that the planned campaign succeeds.\\n- Evaluated the artifact as a competent 15-minute delivery from the outline, not as a line edit of the outline or an assessment of slides that do not yet exist.\\n- Weighted technical credibility and evidentiary support more heavily than visual polish because the target is the DAC-10 design-optimization audience.\\n- Used the submitted abstract to resolve technical intent when the outline was ambiguous, while flagging inconsistencies rather than silently harmonizing them.\\n- Interpreted the stated optimization problem as two objectives, specific energy absorption and compaction efficiency, subject to a peak-force constraint; flagged language that instead treats peak force as an objective.\\n- Assigned qualitative damage levels of moderate, moderate-to-severe, or severe based on how directly an unanswered challenge would undermine each persona\\u2019s acceptance of the central claim.\\n- Chose P6 as the best-served persona because the present outline emphasizes an intuitive workflow and practical efficiency promise; chose P1 as the worst-served because the DAC-specific validation and novelty case is not yet developed.\\n- Scored predicted reception on the current incomplete outline, not on a hypothetical final talk with successful results.\"}", + "name": "submit_answer" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "user", + "content": "\nMessage was NOT sent to user. Please call the send_message tool to communicate with the user. Use wait=True if you need a response before proceeding, or wait=False if you are providing a status update to the user.", + "info": { + "is_human": false + } + } + ], + "next_observation": [ + { + "role": "tool", + "content": "# Mock audience report: IDETC-CIE 2026 DAC-10 talk\n\n## Scope of this review\n\nThis evaluates the talk implied by the outline, not finished slides or delivery technique. The experimental campaign is still in progress, so I do **not** infer any favorable results. The empty \u201cEvidence / results\u201d section is treated as a major unresolved part of the talk: the outline currently explains why the workflow should be useful, but does not yet demonstrate that it is useful.\n\nThe central communication test is whether each listener leaves with the intended message:\n\n> By closing the loop between multi-material 3D printing and Bayesian optimization, we can optimize tensegrity energy absorbers directly from real impact data, in dozens of prints rather than thousands.\n\n---\n\n# P1. The skeptical Bayesian-optimization insider\n\n## (a) First-person reaction\n\nThe application is potentially interesting, and the design\u2013print\u2013test loop is easy to understand. The mixed continuous, integer, and categorical design space, physical failures, parallel fabrication, and multiple constrained objectives could make this a worthwhile design-automation case study.\n\nBut the outline keeps telling me that Bayesian optimization is sample-efficient without showing that **this implementation** is sample-efficient. \u201cDozens, not thousands\u201d sounds like a slogan unless I see the actual experiment count, initialization size, batch size, replication policy, convergence behavior, and a comparison against credible alternatives. q-noisy expected hypervolume improvement, or qNEHVI, is established machinery. Merely deploying it does not establish methodological novelty.\n\nI become skeptical at the statement that qNEHVI was selected because of heteroscedastic TPU and print noise. qNEHVI accounts for noisy observations, but the outline does not explain how the surrogate models heteroscedasticity, whether repeated specimens identify noise, or whether noise is being conflated with unmodeled process drift. Independent Gaussian processes with an unspecified observation model do not automatically resolve batch-dependent heteroscedasticity.\n\nMy attention drifts during the broad argument against simulation because it delays the information I came to DAC-10 to hear: the design variables, model formulation, constraint handling, acquisition implementation, budget, and benchmark. The Super Ball Bot hook is visually effective, but planetary landing may feel like application theater if the actual specimens and impact regime are far removed from that use case.\n\nThe strongest possible section is the results slot. In its current empty state, the talk has a method narrative but no validation.\n\n## (b) What I would repeat the next day\n\n> \u201cThey used standard batch noisy multi-objective Bayesian optimization to choose which multi-material printed impact specimens to test, instead of running a full physical sweep.\u201d\n\n**Distortion from the intended message:** I would omit the claim that the structures were optimized in dozens of prints because I have not seen evidence for it. I would also describe qNEHVI as standard rather than as the contribution.\n\n## (c) Top three Q&A questions\n\n1. **What is the actual experimental budget?** How many initial designs, adaptive iterations, specimens per batch, and physical replicates were used, and what stopping rule defined success?\n2. **What baselines did you beat?** Did you compare qNEHVI with Sobol or Latin-hypercube sampling, random search, scalarized Bayesian optimization, or a domain-informed sequential design under the same number of physical tests?\n3. **How exactly is noise modeled?** Are replicate-dependent variances supplied to the Gaussian processes, is a heteroscedastic likelihood fitted, and how are TPU lot, print batch, machine drift, and specimen-level defects distinguished?\n\n## (d) Most pointed challenge\n\n> \u201cWhat did you contribute beyond applying off-the-shelf qNEHVI to a new specimen family?\u201d\n\n**Damage if unanswered: severe.** For a DAC review audience, this can reduce the work from a design-automation contribution to an application demonstration. A strong answer could rest on the experimental formulation, mixed-variable and failure-aware implementation, quantitative sample-efficiency evidence, and transferable operating lessons. Those contributions must be stated explicitly and tested.\n\n---\n\n# P2. The aerospace entry, descent, and landing practitioner\n\n## (a) First-person reaction\n\nThe opening lands immediately. I recognize the Super Ball Bot concept, and \u201cthe lander is the energy absorber\u201d is a useful framing. The three performance quantities also sound relevant: energy absorbed per mass, usable stroke, and force transmitted to the payload.\n\nThen the talk moves into language I do not naturally use: Gaussian-process surrogate, qNEHVI, acquisition function, hypervolume, exploration versus exploitation, and Pareto front. I can accept that the algorithm chooses the next tests, but I need a plain explanation of what information it uses and why I should trust the recommendations. If the presenter spends several minutes naming algorithms without connecting them to engineering decisions, I will lose the thread.\n\nMy main concern is application fidelity. A desktop drop-weight test at one fixed impact energy is not automatically evidence for entry, descent, and landing hardware. I need specimen dimensions, impact mass, velocity, energy, strain rate, boundary conditions, number of impacts, rebound behavior, temperature, vacuum sensitivity, and failure containment. PLA and TPU raise immediate environmental and qualification questions. I also want to know whether the absorber is reusable or sacrificial.\n\nMy attention returns when I see force\u2013time traces, high-speed footage, and damaged specimens. It drifts if the talk stays on the optimizer rather than connecting each output to payload protection.\n\n## (b) What I would repeat the next day\n\n> \u201cThey have an algorithm that learns from successive drop tests and tells them which 3D-printed crush structure to make next, so they can find a lightweight low-force design with fewer prototypes.\u201d\n\n**Distortion from the intended message:** I would probably call the structures \u201ccrush structures,\u201d losing the tensegrity distinction. I might also overgeneralize the method to planetary landers even though the current tests appear to be laboratory-scale.\n\n## (c) Top three Q&A questions\n\n1. What impact mass, velocity, energy, and strain-rate regime do the tests cover, and how do those values compare with a credible lander load case?\n2. How do the selected designs scale with size and impact energy? Are the controlling mechanisms material-rate effects, geometric buckling, contact, or all three?\n3. What happens under off-axis impact, repeated impacts, temperature extremes, aging, vacuum, and manufacturing defects?\n\n## (d) Most pointed challenge\n\n> \u201cWhy should I believe that a PLA\u2013TPU specimen optimized at one laboratory impact condition tells me anything about flight-scale landing protection?\u201d\n\n**Damage if unanswered: severe for the planetary-landing framing, moderate for the underlying optimization study.** The presenter should clearly label planetary landing as motivation, not demonstrated readiness, and state the present technology scope. Otherwise the opening and closing overpromise relative to the reported experiment.\n\n---\n\n# P3. The additive-manufacturing and materials researcher\n\n## (a) First-person reaction\n\nThe monolithic PLA\u2013TPU architecture is the part I care about. The core-wrapping strategy sounds more credible than simply depositing TPU against exposed PLA and assuming durable adhesion. Holding print settings fixed within a batch, weighing specimens, checking dimensions, and periodically testing a control are all sensible.\n\nI still need much more fabrication detail before I accept that design geometry is the main source of performance variation. PLA\u2013TPU interfaces are sensitive to temperature, surface contact, moisture, residence time, toolpath order, contamination, and local geometry. \u201cPrint defects\u201d is too broad. I want failure-mode evidence showing whether energy is absorbed through intended tension-network deformation, TPU hysteresis, strut buckling, interface peeling, or accidental damage.\n\nThe phrase \u201cno assembly\u201d catches my attention, but \u201celiminates assembly entirely\u201d may be too strong. Does the build require support removal, trimming, conditioning, fixture insertion, post-tensioning, or manual alignment? If so, the accurate claim is \u201csingle-build co-fabrication\u201d or \u201cno joining of PLA and TPU members,\u201d not zero assembly.\n\nI am a Bayesian-optimization novice. I can follow the loop if the presenter says, \u201cThe model proposes the next small batch where improvement is plausible or uncertainty is high.\u201d I will disengage if the slide foregrounds qNEHVI notation rather than the physical decisions it enables.\n\n## (b) What I would repeat the next day\n\n> \u201cThey co-print rigid PLA and flexible TPU energy absorbers, test them, and use the results to choose the next geometries instead of printing a large design-of-experiments grid.\u201d\n\n**Distortion from the intended message:** I would remember the fabrication loop more strongly than tensegrity or Bayesian optimization. I might interpret the improvement as mostly a material-interface result unless the structural mechanisms are shown.\n\n## (c) Top three Q&A questions\n\n1. How was PLA\u2013TPU bonding quantified, and where did failed specimens actually fail: within TPU, within PLA, or at the interface?\n2. How many nominally identical replicates were printed and tested, and how large were within-batch, between-batch, and printer-drift effects relative to design effects?\n3. What manual operations occur after printing, and what exactly does \u201cno assembly\u201d exclude?\n\n## (d) Most pointed challenge\n\n> \u201cHow do you know the optimizer is learning architecture rather than uncontrolled interface quality and process drift?\u201d\n\n**Damage if unanswered: severe.** If fabrication variation is not measured and separated from design effects, both the surrogate and the claimed Pareto front may be unstable. Replicates, blocking or covariates, control specimens, dimensional measurements, and failure-mode documentation are needed.\n\n---\n\n# P4. The first-year graduate student\n\n## (a) First-person reaction\n\nThe opening video and the phrase \u201cthe lander is the energy absorber\u201d give me a concrete picture. I understand the basic problem: too many designs to print, and computer models may miss messy material behavior. I also understand the loop when it is described as \u201cdesign, print, drop, learn, repeat.\u201d That story carries me.\n\nI start getting lost when several unfamiliar terms arrive together: tensegrity-inspired, compaction efficiency, Gaussian process, surrogate, acquisition function, qNEHVI, heteroscedastic noise, Pareto front, probabilistic feasibility constraint, and exploration versus exploitation. The preview says the audience already believes optimization works, but I still need one intuitive sentence explaining what Bayesian optimization does. Skipping \u201cBayesian optimization 101\u201d should not mean skipping the conceptual bridge.\n\nI am also unsure how the structure works. Are the TPU parts stretched before impact? Are the PLA struts touching? What makes this \u201ctensegrity-inspired\u201d rather than a multi-material lattice? A labeled specimen image and a short deformation sequence would help much more than a verbal definition.\n\nMy attention drifts during the repeated case against simulation because I accepted that point the first time. It returns for printing footage, the drop test, and a clear plot with a marked \u201cbetter\u201d direction.\n\n## (b) What I would repeat the next day\n\n> \u201cThey let an artificial-intelligence program learn from 3D-printed drop tests and keep suggesting better shock absorbers, so they do not have to test every design.\u201d\n\n**Distortion from the intended message:** I would probably call Bayesian optimization \u201cAI,\u201d omit the two-objective trade-off and force constraint, and assume each new design is simply better than the last. I might also miss what tensegrity contributes.\n\n## (c) Top three Q&A questions\n\n1. What makes one of these specimens a tensegrity structure rather than an ordinary 3D-printed lattice?\n2. How does the computer decide which design to print next, in plain language?\n3. Can one design maximize both energy absorption and compaction efficiency while also lowering peak force, or must those goals trade off?\n\n## (d) Most pointed challenge\n\n> \u201cI followed the loop, but I still do not understand what is physically special about the structure or what the optimizer actually learned.\u201d\n\n**Damage if unanswered: moderate.** Experts can fill in some gaps, but a talk that leaves novices with only \u201cAI plus 3D printing\u201d has failed to communicate its engineering mechanism. One specimen-mechanics visual and one plain-language optimization visual would largely solve this.\n\n---\n\n# P5. The finite-element-analysis veteran\n\n## (a) First-person reaction\n\nThe statement \u201csimulation can\u2019t be trusted for these structures\u201d puts me on the defensive before I have seen any evidence. Interfacial slip, viscoelasticity, large deformation, buckling, contact, strain-rate dependence, and manufacturing variability are difficult, but they are not intrinsically beyond finite-element analysis. Models can be calibrated, uncertainty can be propagated, and simulation can still screen designs even if it is imperfect.\n\nThe abstract is more defensible than the talk outline. It says the workflow avoids **direct dependence on calibrated finite-element simulation for objective evaluation** and acknowledges future analytical or multifidelity shortcuts. The presentation should use that language. The current outline turns a project-scope choice into a universal technical claim.\n\nI accept the economic argument that calibrating a high-fidelity model may not be worthwhile for an early design campaign. That is a stronger position: measured tests provide authoritative objective values, while the study asks whether sequential design can use a small physical budget efficiently. I would also welcome evidence that simulation errors change design rankings or miss observed failure modes. Without such evidence, the repeated attack on simulation sounds like a straw man.\n\nMy attention drifts when the talk says simulation is unreliable several times without showing a simulation\u2013experiment discrepancy. It returns when the presenter discusses physical failure modes and admits where lower-fidelity physics could later improve the loop.\n\n## (b) What I would repeat the next day\n\n> \u201cThey skipped finite-element modeling and used Bayesian optimization directly on physical tests because they judged model calibration too expensive for these multi-material specimens.\u201d\n\n**Distortion from the intended message:** I would frame the work as bypassing simulation rather than as enabling rapid experimental optimization. If the presenter is combative about simulation, that dispute may become the only thing I remember.\n\n## (c) Top three Q&A questions\n\n1. What evidence shows that a calibrated finite-element model is insufficient for design ranking, rather than merely costly to construct?\n2. Did you compare the physical-test-only workflow with a simple mechanics model or multifidelity surrogate that could reduce the experimental budget further?\n3. Which observed behaviors dominate the model discrepancy: interface failure, TPU viscoelasticity, contact, buckling imperfections, or geometry errors from printing?\n\n## (d) Most pointed challenge\n\n> \u201cYou have shown that simulation is inconvenient, not that it cannot be trusted. Why is abandoning it scientifically preferable to calibrating and validating it?\u201d\n\n**Damage if unanswered: severe and avoidable.** The current absolute wording invites a technical fight that is unnecessary to the contribution. Reframing the claim as a cost-and-dependence decision would preserve the motivation without asserting that finite-element modeling is incapable.\n\n---\n\n# P6. The friendly industry generalist\n\n## (a) First-person reaction\n\nThe loop is memorable: print a candidate, test it, update the model, and print the next informative candidate. I can imagine using that pattern for brackets, seals, lattice pads, or process settings when simulation is weak and tests are expensive. \u201cDozens rather than thousands\u201d is exactly the kind of value proposition I remember.\n\nI do not need the mathematical details of qNEHVI, but I need an operational recipe. What software is used? How many initial samples are required? How automated is the handoff from optimizer to computer-aided design and slicing? How long does one cycle take? What expertise does a team need? The abstract says candidate selection is automated but slicing, specimen handling, and testing remain manual. That qualification should appear in the talk before I infer a robotic self-driving laboratory.\n\nThe tensegrity and planetary material is interesting, but my attention will drift if it occupies too much of a 15-minute slot. The method becomes useful to me only when the presenter explicitly generalizes it and defines when it is worth using.\n\nThe ending about moving humankind forward is less memorable than a concrete deployment rule. I would prefer a final slide saying: \u201cUse this loop when tests are authoritative, each test is costly, the design space is mixed, and several performance goals conflict.\u201d\n\n## (b) What I would repeat the next day\n\n> \u201cThey showed a practical test-driven optimization loop that can find good 3D-printed designs with a few dozen prototypes instead of a giant test matrix.\u201d\n\n**Distortion from the intended message:** I may overstate automation and assume the \u201cfew dozen\u201d claim is established across applications. I will probably forget the names qNEHVI and tensegrity, but retain the workflow.\n\n## (c) Top three Q&A questions\n\n1. What parts of the workflow are automated today, and what does an engineer still do manually between recommendation and test result?\n2. What minimum experiment budget and software stack would my team need to try this on a different component?\n3. When is this approach better than a conventional design of experiments, response-surface method, or a calibrated simulation?\n\n## (d) Most pointed challenge\n\n> \u201cWhat concrete evidence tells me this saved enough prototypes and engineering time to justify the added optimization infrastructure?\u201d\n\n**Damage if unanswered: moderate to severe.** I will still like the idea, but I will not take it back to my team. A budget-matched baseline and a simple timeline or cost comparison would convert interest into action.\n\n---\n\n# Synthesis\n\n## Cross-persona themes\n\n### 1. The causal chain is understandable, but the claimed payoff is not yet demonstrated\n\nAll six personas can understand the basic design\u2013print\u2013test\u2013learn loop. The empty results section prevents them from deciding whether the loop actually found better designs, found them faster, or handled noise reliably. The phrase \u201cdozens, not thousands\u201d is currently an unsupported quantitative claim.\n\nAt minimum, the results need to show:\n\n- total number of unique designs and physical specimens;\n- initial versus adaptively selected designs;\n- batch size and number of iterations;\n- replicate policy and failure count;\n- measured uncertainty or repeatability;\n- progress under the actual experimental budget;\n- the final feasible Pareto set for **specific energy absorption and compaction efficiency subject to the force cap**;\n- a budget-matched nonadaptive or simpler optimization baseline.\n\n### 2. \u201cSimulation can\u2019t be trusted\u201d is too absolute\n\nP1 questions the evidence, P2 cares about application validity, and P5 directly rejects the premise. Even P6 needs to know when the loop is preferable to simulation. The abstract already contains better wording: physical measurements are used for objective evaluation \u201cwithout relying on calibrated finite-element simulation.\u201d\n\nThe defensible claim is not that simulation fails universally. It is that a sufficiently calibrated multiphysics model may be expensive relative to the available design campaign, and the authors therefore test whether physical-data-driven sequential optimization is useful without depending on such a model.\n\n### 3. The talk needs a two-level explanation of Bayesian optimization\n\nP1 does not want introductory Bayesian-optimization material; P2, P3, P4, and P5 need a plain conceptual bridge. These needs are compatible. Give the intuition in one sentence and one visual, then put the implementation specifics in a compact technical panel or backup slide.\n\nSuggested spoken explanation:\n\n> \u201cAfter each batch, the model estimates both expected performance and uncertainty across the design space. The acquisition rule selects a small next batch that is most likely to expand the feasible trade-off frontier, while accounting for noisy tests and print failures.\u201d\n\nThis is not \u201cBayesian optimization 101.\u201d It is the minimum explanation needed to interpret the workflow.\n\n### 4. The physical mechanism and the word \u201ctensegrity\u201d need clarification\n\nP2 may reduce the objects to crush structures, P3 may interpret the result as an interface study, and P4 may not distinguish the specimens from ordinary lattices. A labeled as-printed specimen plus a four-frame deformation sequence should show:\n\n- rigid PLA struts;\n- continuous flexible TPU network;\n- whether and how pre-tension exists;\n- load path before and during impact;\n- intended energy-dissipation mechanisms;\n- why \u201ctensegrity-inspired\u201d is the precise term.\n\n### 5. Manufacturing variability is part of the model, not background noise\n\nP1 and P3 will both press this point. Periodically retesting one control is useful but may not identify design-specific heteroscedasticity, lot effects, or confounding between iteration and material batch. The talk needs to state the replication, randomization, blocking, covariate, and drift-monitoring plan. If those steps were not used, the limitation must be explicit.\n\n### 6. The application framing outruns the demonstrated scope\n\nThe planetary hook is strong, but P2 will not equate fixed-energy laboratory tests on PLA\u2013TPU specimens with flight hardware. The closing escalates further by claiming that the next lander absorber can be \u201cproven\u201d in weeks. That wording should be narrowed unless the campaign includes relevant scaling and environmental evidence.\n\n### 7. The objective statement is not fully consistent across the outline\n\nThe task and Point 2 correctly describe two objectives, specific energy absorption and compaction efficiency, with a peak-force constraint. The candidate evidence list instead proposes \u201cSEA vs. peak transmitted force,\u201d and the abstract\u2019s expected-outcomes section similarly describes trade-offs between SEA and peak force. Those are different optimization formulations.\n\nThe talk must consistently distinguish:\n\n- **objectives:** maximize specific energy absorption and compaction efficiency;\n- **constraint:** peak transmitted force must not exceed a specified cap;\n- **feasible Pareto front:** trade-offs between the two objectives among designs satisfying that cap.\n\nIf peak force is instead treated as a third objective, the task statement and optimization formulation must be revised accordingly.\n\n### 8. The closing favors inspiration over technical precision\n\nP6 is likely to remember \u201cdozens, not thousands,\u201d but P1, P2, and P5 may hear overreach in \u201cproven,\u201d \u201cin weeks,\u201d and \u201cmoves humankind forward.\u201d A DAC-10 close should end on the measured engineering result and transfer condition. The Super Ball Bot bookend can remain, but it should not imply flight qualification.\n\n---\n\n# Three highest-priority revisions\n\n## Priority 1: Build the talk around a quantitative result, not around the availability of a workflow\n\nReplace the placeholder with a required three-slide evidence sequence:\n\n1. **\u201cThe campaign used N specimens across B adaptive batches under a fixed physical-test budget.\u201d** \n Show initialization, adaptive batches, replicates, failures, elapsed cycle time, and the force threshold. Do not use `N` or `B` in the actual talk; insert the observed values.\n\n2. **\u201cAdaptive selection improved the feasible design set faster than [predeclared baseline].\u201d** \n Plot a budget-matched performance measure across physical tests, with uncertainty across repeated runs if available. If only one physical campaign exists, avoid inferential claims that require repeated campaigns and supplement with clearly labeled retrospective resampling or simulation-based algorithm checks.\n\n3. **\u201cThese measured designs form the final feasible trade-off between specific energy absorption and compaction efficiency.\u201d** \n Show uncertainty or replicate spread, mark infeasible force-cap violations, and include specimen images or force\u2013time traces for representative Pareto designs.\n\nIf the campaign cannot support those statements, revise the main message from \u201cwe can optimize \u2026 in dozens\u201d to \u201cwe are evaluating whether noisy multi-objective Bayesian optimization can reduce the physical test budget.\u201d\n\n## Priority 2: Replace the anti-simulation premise with a scoped engineering decision\n\nReplace:\n\n> \u201cSimulation can\u2019t be trusted for these structures.\u201d\n\nwith:\n\n> \u201cFor these multi-material prints, obtaining objective values from a validated high-fidelity model would require substantial calibration of interface, rate, contact, and defect behavior. We therefore ask how far a limited budget of direct physical tests can take us without depending on that model.\u201d\n\nThen title Point 1 with a message rather than a topic:\n\n> **\u201cWhen model calibration and exhaustive testing are both costly, each physical specimen must be chosen for information value.\u201d**\n\nThis keeps the pincer structure while avoiding an unnecessary universal claim.\n\n## Priority 3: Reallocate the 15 minutes around audience decisions\n\nA concrete timing plan:\n\n- **1.5 min:** Super Ball Bot hook, specimen, and present study scope.\n- **2 min:** Why exhaustive physical search and high-fidelity calibration are costly.\n- **2.5 min:** One visual of the closed loop, including a one-sentence Bayesian-optimization explanation.\n- **2 min:** Actual design variables, objectives, force constraint, fabrication failures, and noise controls.\n- **5 min:** measured results, baseline, uncertainty, representative specimens, and failure modes.\n- **1 min:** limitations and transfer conditions.\n- **1 min:** measured conclusion and bookend.\n\nMove kernel choices, log-space acquisitions, detailed encoding, and other implementation material to backup slides unless a result depends directly on them. This follows Doumont\u2019s principle that each slide should communicate one interpreted message rather than expose the speaker\u2019s notes.\n\n---\n\n# Claims needing evidence, hedging, or backup slides\n\n| Claim or topic | What is needed in the main talk | Prepared backup material |\n|---|---|---|\n| \u201cDozens of prints, not thousands\u201d | Actual specimen count, definition of success, and budget-matched comparator | Full campaign ledger; sensitivity to initialization and stopping rule |\n| \u201cSimulation can\u2019t be trusted\u201d | Replace with scoped calibration-cost language unless direct validation evidence exists | Simulation\u2013experiment traces, ranking errors, or documented failure modes a tested model missed |\n| \u201cqNEHVI handles heteroscedastic noise\u201d | Specify the actual observation-noise model and replication strategy | Likelihood formulation, fixed versus inferred noise, residual diagnostics, replicate variance by design or batch |\n| qNEHVI \u201cover standard expected improvement\u201d | Use a technically appropriate comparison; ordinary expected improvement is not a like-for-like multi-objective batch baseline | Comparisons with random/Sobol, scalarization, qEHVI or another relevant noisy/constrained baseline |\n| \u201cThe Pareto front is the design deliverable\u201d | Show the two stated objectives and force-cap feasibility consistently | Hypervolume reference point, normalization, uncertainty, constraint treatment |\n| Peak-force cap | State its numerical value and engineering rationale | Sensitivity of selected designs to alternative cap values |\n| Probabilistic print-feasibility constraint | Report failures and demonstrate that the classifier or constraint model is identifiable at the available sample size | Failure labels, calibration, confusion or reliability metrics, acquisition formula |\n| \u201cNo assembly\u201d / \u201celiminates assembly entirely\u201d | List any post-print operations; use \u201csingle-build co-fabrication\u201d if manual steps remain | Fabrication workflow photographs and labor-time accounting |\n| \u201cCandidate goes to tested specimen in hours\u201d | Median or representative print-to-result time and what remains manual | Time breakdown for design, slicing, printing, conditioning, inspection, and testing |\n| \u201cDramatically faster\u201d | Define faster relative to a measured or estimated comparator | Cost and wall-clock accounting with assumptions |\n| Transfer to other architectures | Present as a conditional hypothesis, not a demonstrated result | Applicability checklist: test cost, dimensionality, noise, throughput, and constraint structure |\n| Planetary-landing relevance | Clearly state that this is motivation unless scale and environment are tested | Impact similitude, energy and velocity regime, environmental gaps, technology-readiness discussion |\n| PLA\u2013TPU interface robustness | Failure-mode and repeatability evidence | Microscopy or fracture images, process parameters, conditioning and moisture controls |\n| \u201cTensegrity-inspired\u201d | Show the load path and identify which tensegrity features are retained | Geometric definition, pre-tension status, comparison with a conventional lattice/control |\n| Independent Gaussian processes | State why cross-objective correlation is ignored and whether that choice matters | Residual correlations and sensitivity to multi-output alternatives |\n| Mixed categorical variables | Briefly state how candidates are represented and optimized | Kernel or encoding details; treatment of invalid combinations |\n\n---\n\n# Fit to the six-person audience\n\n## Best served: P6, the friendly industry generalist\n\nThe outline has a clear problem\u2013method\u2013application arc, a memorable loop, and an attractive efficiency promise. P6 can understand the proposed value without needing the mathematics. The danger is that this persona may accept unsupported claims and overestimate the system\u2019s automation and maturity.\n\nP4 also benefits from the story structure, but the unexplained terminology and absent physical-mechanism visual will cause substantial loss of detail.\n\n## Worst served: P1, the skeptical Bayesian-optimization insider\n\nP1 is the most likely to judge the current talk as an off-the-shelf acquisition function applied to a new artifact. The outline anticipates this concern by saying not to teach Bayesian optimization, but it does not yet supply the evidence P1 needs: novelty positioning, rigorous baseline, budget, noise identification, constraint diagnostics, and measured sample efficiency.\n\nP5 is nearly as poorly served because the opening motivation directly challenges finite-element modeling more strongly than the abstract supports.\n\n## Is that the right trade-off for DAC-10?\n\nNo. A DAC-10 presentation should remain accessible to P2\u2013P6, but it cannot underserve P1. The design-automation expert is likely to shape the technical discussion and the perceived contribution. Accessibility should come from a plain-language loop and strong visuals, not from omitting methodological validation. The best target is a two-layer talk: every listener can follow the engineering decision, while experts can see the experimental budget, comparator, noise model, and constraint formulation.\n\n---\n\n# Predicted reception\n\n**Current outline: 5/10.** The loop and application are memorable, but without measured results, a budget-matched baseline, and narrower simulation and planetary claims, the talk reads as a polished motivation for an experiment rather than a completed DAC-10 contribution.\n\nIf the results establish repeatability and budgeted improvement, and the three priority revisions are made, the same structure could plausibly move into the **7\u20138/10** range. That conditional estimate is a judgment about presentation readiness, not a prediction of unobserved experimental outcomes.\n\n---\n\n# Discretionary analytical decisions\n\n- Treated the unfinished evidence/results section as missing evidence rather than assuming that the planned campaign succeeds.\n- Evaluated the artifact as a competent 15-minute delivery from the outline, not as a line edit of the outline or an assessment of slides that do not yet exist.\n- Weighted technical credibility and evidentiary support more heavily than visual polish because the target is the DAC-10 design-optimization audience.\n- Used the submitted abstract to resolve technical intent when the outline was ambiguous, while flagging inconsistencies rather than silently harmonizing them.\n- Interpreted the stated optimization problem as two objectives, specific energy absorption and compaction efficiency, subject to a peak-force constraint; flagged language that instead treats peak force as an objective.\n- Assigned qualitative damage levels of moderate, moderate-to-severe, or severe based on how directly an unanswered challenge would undermine each persona\u2019s acceptance of the central claim.\n- Chose P6 as the best-served persona because the present outline emphasizes an intuitive workflow and practical efficiency promise; chose P1 as the worst-served because the DAC-specific validation and novelty case is not yet developed.\n- Scored predicted reception on the current incomplete outline, not on a hypothetical final talk with successful results.", + "name": "submit_answer", + "tool_call_id": "call_zSHcqZDq4Rrb984ui6lpQugL", + "info": { + "start_ts": 12012.152820776, + "end_ts": 12012.154539476 + } + } + ], + "reward": 0.0, + "truncated": false, + "done": true, + "value": 0.0, + "metadata": { + "time_elapsed_before_transition": 1.11469760899854, + "time_elapsed_agent_get_asv": 164.56053172799875, + "time_elapsed_after_agent_get_asv": 0.8963360329998977, + "time_elapsed_env_step": 0.002234097999462392, + "time_elapsed_after_env_step": 0.8714679189997696 + }, + "action": { + "call_id": { + "run_id": "fc2d1a3a-48db-409f-bfe9-7aaa8fe946b3", + "fwd_id": "6b88bc00-7c53-44b5-a7c0-37329a18fa57" + }, + "op_name": "IdentityOp-3c0dc047595c", + "op_class_name": "ldp.graph.common_ops.IdentityOp", + "value": { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_zSHcqZDq4Rrb984ui6lpQugL", + "type": "function", + "function": { + "arguments": "{\"answer\": \"# Mock audience report: IDETC-CIE 2026 DAC-10 talk\\n\\n## Scope of this review\\n\\nThis evaluates the talk implied by the outline, not finished slides or delivery technique. The experimental campaign is still in progress, so I do **not** infer any favorable results. The empty \\u201cEvidence / results\\u201d section is treated as a major unresolved part of the talk: the outline currently explains why the workflow should be useful, but does not yet demonstrate that it is useful.\\n\\nThe central communication test is whether each listener leaves with the intended message:\\n\\n> By closing the loop between multi-material 3D printing and Bayesian optimization, we can optimize tensegrity energy absorbers directly from real impact data, in dozens of prints rather than thousands.\\n\\n---\\n\\n# P1. The skeptical Bayesian-optimization insider\\n\\n## (a) First-person reaction\\n\\nThe application is potentially interesting, and the design\\u2013print\\u2013test loop is easy to understand. The mixed continuous, integer, and categorical design space, physical failures, parallel fabrication, and multiple constrained objectives could make this a worthwhile design-automation case study.\\n\\nBut the outline keeps telling me that Bayesian optimization is sample-efficient without showing that **this implementation** is sample-efficient. \\u201cDozens, not thousands\\u201d sounds like a slogan unless I see the actual experiment count, initialization size, batch size, replication policy, convergence behavior, and a comparison against credible alternatives. q-noisy expected hypervolume improvement, or qNEHVI, is established machinery. Merely deploying it does not establish methodological novelty.\\n\\nI become skeptical at the statement that qNEHVI was selected because of heteroscedastic TPU and print noise. qNEHVI accounts for noisy observations, but the outline does not explain how the surrogate models heteroscedasticity, whether repeated specimens identify noise, or whether noise is being conflated with unmodeled process drift. Independent Gaussian processes with an unspecified observation model do not automatically resolve batch-dependent heteroscedasticity.\\n\\nMy attention drifts during the broad argument against simulation because it delays the information I came to DAC-10 to hear: the design variables, model formulation, constraint handling, acquisition implementation, budget, and benchmark. The Super Ball Bot hook is visually effective, but planetary landing may feel like application theater if the actual specimens and impact regime are far removed from that use case.\\n\\nThe strongest possible section is the results slot. In its current empty state, the talk has a method narrative but no validation.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey used standard batch noisy multi-objective Bayesian optimization to choose which multi-material printed impact specimens to test, instead of running a full physical sweep.\\u201d\\n\\n**Distortion from the intended message:** I would omit the claim that the structures were optimized in dozens of prints because I have not seen evidence for it. I would also describe qNEHVI as standard rather than as the contribution.\\n\\n## (c) Top three Q&A questions\\n\\n1. **What is the actual experimental budget?** How many initial designs, adaptive iterations, specimens per batch, and physical replicates were used, and what stopping rule defined success?\\n2. **What baselines did you beat?** Did you compare qNEHVI with Sobol or Latin-hypercube sampling, random search, scalarized Bayesian optimization, or a domain-informed sequential design under the same number of physical tests?\\n3. **How exactly is noise modeled?** Are replicate-dependent variances supplied to the Gaussian processes, is a heteroscedastic likelihood fitted, and how are TPU lot, print batch, machine drift, and specimen-level defects distinguished?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhat did you contribute beyond applying off-the-shelf qNEHVI to a new specimen family?\\u201d\\n\\n**Damage if unanswered: severe.** For a DAC review audience, this can reduce the work from a design-automation contribution to an application demonstration. A strong answer could rest on the experimental formulation, mixed-variable and failure-aware implementation, quantitative sample-efficiency evidence, and transferable operating lessons. Those contributions must be stated explicitly and tested.\\n\\n---\\n\\n# P2. The aerospace entry, descent, and landing practitioner\\n\\n## (a) First-person reaction\\n\\nThe opening lands immediately. I recognize the Super Ball Bot concept, and \\u201cthe lander is the energy absorber\\u201d is a useful framing. The three performance quantities also sound relevant: energy absorbed per mass, usable stroke, and force transmitted to the payload.\\n\\nThen the talk moves into language I do not naturally use: Gaussian-process surrogate, qNEHVI, acquisition function, hypervolume, exploration versus exploitation, and Pareto front. I can accept that the algorithm chooses the next tests, but I need a plain explanation of what information it uses and why I should trust the recommendations. If the presenter spends several minutes naming algorithms without connecting them to engineering decisions, I will lose the thread.\\n\\nMy main concern is application fidelity. A desktop drop-weight test at one fixed impact energy is not automatically evidence for entry, descent, and landing hardware. I need specimen dimensions, impact mass, velocity, energy, strain rate, boundary conditions, number of impacts, rebound behavior, temperature, vacuum sensitivity, and failure containment. PLA and TPU raise immediate environmental and qualification questions. I also want to know whether the absorber is reusable or sacrificial.\\n\\nMy attention returns when I see force\\u2013time traces, high-speed footage, and damaged specimens. It drifts if the talk stays on the optimizer rather than connecting each output to payload protection.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey have an algorithm that learns from successive drop tests and tells them which 3D-printed crush structure to make next, so they can find a lightweight low-force design with fewer prototypes.\\u201d\\n\\n**Distortion from the intended message:** I would probably call the structures \\u201ccrush structures,\\u201d losing the tensegrity distinction. I might also overgeneralize the method to planetary landers even though the current tests appear to be laboratory-scale.\\n\\n## (c) Top three Q&A questions\\n\\n1. What impact mass, velocity, energy, and strain-rate regime do the tests cover, and how do those values compare with a credible lander load case?\\n2. How do the selected designs scale with size and impact energy? Are the controlling mechanisms material-rate effects, geometric buckling, contact, or all three?\\n3. What happens under off-axis impact, repeated impacts, temperature extremes, aging, vacuum, and manufacturing defects?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhy should I believe that a PLA\\u2013TPU specimen optimized at one laboratory impact condition tells me anything about flight-scale landing protection?\\u201d\\n\\n**Damage if unanswered: severe for the planetary-landing framing, moderate for the underlying optimization study.** The presenter should clearly label planetary landing as motivation, not demonstrated readiness, and state the present technology scope. Otherwise the opening and closing overpromise relative to the reported experiment.\\n\\n---\\n\\n# P3. The additive-manufacturing and materials researcher\\n\\n## (a) First-person reaction\\n\\nThe monolithic PLA\\u2013TPU architecture is the part I care about. The core-wrapping strategy sounds more credible than simply depositing TPU against exposed PLA and assuming durable adhesion. Holding print settings fixed within a batch, weighing specimens, checking dimensions, and periodically testing a control are all sensible.\\n\\nI still need much more fabrication detail before I accept that design geometry is the main source of performance variation. PLA\\u2013TPU interfaces are sensitive to temperature, surface contact, moisture, residence time, toolpath order, contamination, and local geometry. \\u201cPrint defects\\u201d is too broad. I want failure-mode evidence showing whether energy is absorbed through intended tension-network deformation, TPU hysteresis, strut buckling, interface peeling, or accidental damage.\\n\\nThe phrase \\u201cno assembly\\u201d catches my attention, but \\u201celiminates assembly entirely\\u201d may be too strong. Does the build require support removal, trimming, conditioning, fixture insertion, post-tensioning, or manual alignment? If so, the accurate claim is \\u201csingle-build co-fabrication\\u201d or \\u201cno joining of PLA and TPU members,\\u201d not zero assembly.\\n\\nI am a Bayesian-optimization novice. I can follow the loop if the presenter says, \\u201cThe model proposes the next small batch where improvement is plausible or uncertainty is high.\\u201d I will disengage if the slide foregrounds qNEHVI notation rather than the physical decisions it enables.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey co-print rigid PLA and flexible TPU energy absorbers, test them, and use the results to choose the next geometries instead of printing a large design-of-experiments grid.\\u201d\\n\\n**Distortion from the intended message:** I would remember the fabrication loop more strongly than tensegrity or Bayesian optimization. I might interpret the improvement as mostly a material-interface result unless the structural mechanisms are shown.\\n\\n## (c) Top three Q&A questions\\n\\n1. How was PLA\\u2013TPU bonding quantified, and where did failed specimens actually fail: within TPU, within PLA, or at the interface?\\n2. How many nominally identical replicates were printed and tested, and how large were within-batch, between-batch, and printer-drift effects relative to design effects?\\n3. What manual operations occur after printing, and what exactly does \\u201cno assembly\\u201d exclude?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cHow do you know the optimizer is learning architecture rather than uncontrolled interface quality and process drift?\\u201d\\n\\n**Damage if unanswered: severe.** If fabrication variation is not measured and separated from design effects, both the surrogate and the claimed Pareto front may be unstable. Replicates, blocking or covariates, control specimens, dimensional measurements, and failure-mode documentation are needed.\\n\\n---\\n\\n# P4. The first-year graduate student\\n\\n## (a) First-person reaction\\n\\nThe opening video and the phrase \\u201cthe lander is the energy absorber\\u201d give me a concrete picture. I understand the basic problem: too many designs to print, and computer models may miss messy material behavior. I also understand the loop when it is described as \\u201cdesign, print, drop, learn, repeat.\\u201d That story carries me.\\n\\nI start getting lost when several unfamiliar terms arrive together: tensegrity-inspired, compaction efficiency, Gaussian process, surrogate, acquisition function, qNEHVI, heteroscedastic noise, Pareto front, probabilistic feasibility constraint, and exploration versus exploitation. The preview says the audience already believes optimization works, but I still need one intuitive sentence explaining what Bayesian optimization does. Skipping \\u201cBayesian optimization 101\\u201d should not mean skipping the conceptual bridge.\\n\\nI am also unsure how the structure works. Are the TPU parts stretched before impact? Are the PLA struts touching? What makes this \\u201ctensegrity-inspired\\u201d rather than a multi-material lattice? A labeled specimen image and a short deformation sequence would help much more than a verbal definition.\\n\\nMy attention drifts during the repeated case against simulation because I accepted that point the first time. It returns for printing footage, the drop test, and a clear plot with a marked \\u201cbetter\\u201d direction.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey let an artificial-intelligence program learn from 3D-printed drop tests and keep suggesting better shock absorbers, so they do not have to test every design.\\u201d\\n\\n**Distortion from the intended message:** I would probably call Bayesian optimization \\u201cAI,\\u201d omit the two-objective trade-off and force constraint, and assume each new design is simply better than the last. I might also miss what tensegrity contributes.\\n\\n## (c) Top three Q&A questions\\n\\n1. What makes one of these specimens a tensegrity structure rather than an ordinary 3D-printed lattice?\\n2. How does the computer decide which design to print next, in plain language?\\n3. Can one design maximize both energy absorption and compaction efficiency while also lowering peak force, or must those goals trade off?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cI followed the loop, but I still do not understand what is physically special about the structure or what the optimizer actually learned.\\u201d\\n\\n**Damage if unanswered: moderate.** Experts can fill in some gaps, but a talk that leaves novices with only \\u201cAI plus 3D printing\\u201d has failed to communicate its engineering mechanism. One specimen-mechanics visual and one plain-language optimization visual would largely solve this.\\n\\n---\\n\\n# P5. The finite-element-analysis veteran\\n\\n## (a) First-person reaction\\n\\nThe statement \\u201csimulation can\\u2019t be trusted for these structures\\u201d puts me on the defensive before I have seen any evidence. Interfacial slip, viscoelasticity, large deformation, buckling, contact, strain-rate dependence, and manufacturing variability are difficult, but they are not intrinsically beyond finite-element analysis. Models can be calibrated, uncertainty can be propagated, and simulation can still screen designs even if it is imperfect.\\n\\nThe abstract is more defensible than the talk outline. It says the workflow avoids **direct dependence on calibrated finite-element simulation for objective evaluation** and acknowledges future analytical or multifidelity shortcuts. The presentation should use that language. The current outline turns a project-scope choice into a universal technical claim.\\n\\nI accept the economic argument that calibrating a high-fidelity model may not be worthwhile for an early design campaign. That is a stronger position: measured tests provide authoritative objective values, while the study asks whether sequential design can use a small physical budget efficiently. I would also welcome evidence that simulation errors change design rankings or miss observed failure modes. Without such evidence, the repeated attack on simulation sounds like a straw man.\\n\\nMy attention drifts when the talk says simulation is unreliable several times without showing a simulation\\u2013experiment discrepancy. It returns when the presenter discusses physical failure modes and admits where lower-fidelity physics could later improve the loop.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey skipped finite-element modeling and used Bayesian optimization directly on physical tests because they judged model calibration too expensive for these multi-material specimens.\\u201d\\n\\n**Distortion from the intended message:** I would frame the work as bypassing simulation rather than as enabling rapid experimental optimization. If the presenter is combative about simulation, that dispute may become the only thing I remember.\\n\\n## (c) Top three Q&A questions\\n\\n1. What evidence shows that a calibrated finite-element model is insufficient for design ranking, rather than merely costly to construct?\\n2. Did you compare the physical-test-only workflow with a simple mechanics model or multifidelity surrogate that could reduce the experimental budget further?\\n3. Which observed behaviors dominate the model discrepancy: interface failure, TPU viscoelasticity, contact, buckling imperfections, or geometry errors from printing?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cYou have shown that simulation is inconvenient, not that it cannot be trusted. Why is abandoning it scientifically preferable to calibrating and validating it?\\u201d\\n\\n**Damage if unanswered: severe and avoidable.** The current absolute wording invites a technical fight that is unnecessary to the contribution. Reframing the claim as a cost-and-dependence decision would preserve the motivation without asserting that finite-element modeling is incapable.\\n\\n---\\n\\n# P6. The friendly industry generalist\\n\\n## (a) First-person reaction\\n\\nThe loop is memorable: print a candidate, test it, update the model, and print the next informative candidate. I can imagine using that pattern for brackets, seals, lattice pads, or process settings when simulation is weak and tests are expensive. \\u201cDozens rather than thousands\\u201d is exactly the kind of value proposition I remember.\\n\\nI do not need the mathematical details of qNEHVI, but I need an operational recipe. What software is used? How many initial samples are required? How automated is the handoff from optimizer to computer-aided design and slicing? How long does one cycle take? What expertise does a team need? The abstract says candidate selection is automated but slicing, specimen handling, and testing remain manual. That qualification should appear in the talk before I infer a robotic self-driving laboratory.\\n\\nThe tensegrity and planetary material is interesting, but my attention will drift if it occupies too much of a 15-minute slot. The method becomes useful to me only when the presenter explicitly generalizes it and defines when it is worth using.\\n\\nThe ending about moving humankind forward is less memorable than a concrete deployment rule. I would prefer a final slide saying: \\u201cUse this loop when tests are authoritative, each test is costly, the design space is mixed, and several performance goals conflict.\\u201d\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey showed a practical test-driven optimization loop that can find good 3D-printed designs with a few dozen prototypes instead of a giant test matrix.\\u201d\\n\\n**Distortion from the intended message:** I may overstate automation and assume the \\u201cfew dozen\\u201d claim is established across applications. I will probably forget the names qNEHVI and tensegrity, but retain the workflow.\\n\\n## (c) Top three Q&A questions\\n\\n1. What parts of the workflow are automated today, and what does an engineer still do manually between recommendation and test result?\\n2. What minimum experiment budget and software stack would my team need to try this on a different component?\\n3. When is this approach better than a conventional design of experiments, response-surface method, or a calibrated simulation?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhat concrete evidence tells me this saved enough prototypes and engineering time to justify the added optimization infrastructure?\\u201d\\n\\n**Damage if unanswered: moderate to severe.** I will still like the idea, but I will not take it back to my team. A budget-matched baseline and a simple timeline or cost comparison would convert interest into action.\\n\\n---\\n\\n# Synthesis\\n\\n## Cross-persona themes\\n\\n### 1. The causal chain is understandable, but the claimed payoff is not yet demonstrated\\n\\nAll six personas can understand the basic design\\u2013print\\u2013test\\u2013learn loop. The empty results section prevents them from deciding whether the loop actually found better designs, found them faster, or handled noise reliably. The phrase \\u201cdozens, not thousands\\u201d is currently an unsupported quantitative claim.\\n\\nAt minimum, the results need to show:\\n\\n- total number of unique designs and physical specimens;\\n- initial versus adaptively selected designs;\\n- batch size and number of iterations;\\n- replicate policy and failure count;\\n- measured uncertainty or repeatability;\\n- progress under the actual experimental budget;\\n- the final feasible Pareto set for **specific energy absorption and compaction efficiency subject to the force cap**;\\n- a budget-matched nonadaptive or simpler optimization baseline.\\n\\n### 2. \\u201cSimulation can\\u2019t be trusted\\u201d is too absolute\\n\\nP1 questions the evidence, P2 cares about application validity, and P5 directly rejects the premise. Even P6 needs to know when the loop is preferable to simulation. The abstract already contains better wording: physical measurements are used for objective evaluation \\u201cwithout relying on calibrated finite-element simulation.\\u201d\\n\\nThe defensible claim is not that simulation fails universally. It is that a sufficiently calibrated multiphysics model may be expensive relative to the available design campaign, and the authors therefore test whether physical-data-driven sequential optimization is useful without depending on such a model.\\n\\n### 3. The talk needs a two-level explanation of Bayesian optimization\\n\\nP1 does not want introductory Bayesian-optimization material; P2, P3, P4, and P5 need a plain conceptual bridge. These needs are compatible. Give the intuition in one sentence and one visual, then put the implementation specifics in a compact technical panel or backup slide.\\n\\nSuggested spoken explanation:\\n\\n> \\u201cAfter each batch, the model estimates both expected performance and uncertainty across the design space. The acquisition rule selects a small next batch that is most likely to expand the feasible trade-off frontier, while accounting for noisy tests and print failures.\\u201d\\n\\nThis is not \\u201cBayesian optimization 101.\\u201d It is the minimum explanation needed to interpret the workflow.\\n\\n### 4. The physical mechanism and the word \\u201ctensegrity\\u201d need clarification\\n\\nP2 may reduce the objects to crush structures, P3 may interpret the result as an interface study, and P4 may not distinguish the specimens from ordinary lattices. A labeled as-printed specimen plus a four-frame deformation sequence should show:\\n\\n- rigid PLA struts;\\n- continuous flexible TPU network;\\n- whether and how pre-tension exists;\\n- load path before and during impact;\\n- intended energy-dissipation mechanisms;\\n- why \\u201ctensegrity-inspired\\u201d is the precise term.\\n\\n### 5. Manufacturing variability is part of the model, not background noise\\n\\nP1 and P3 will both press this point. Periodically retesting one control is useful but may not identify design-specific heteroscedasticity, lot effects, or confounding between iteration and material batch. The talk needs to state the replication, randomization, blocking, covariate, and drift-monitoring plan. If those steps were not used, the limitation must be explicit.\\n\\n### 6. The application framing outruns the demonstrated scope\\n\\nThe planetary hook is strong, but P2 will not equate fixed-energy laboratory tests on PLA\\u2013TPU specimens with flight hardware. The closing escalates further by claiming that the next lander absorber can be \\u201cproven\\u201d in weeks. That wording should be narrowed unless the campaign includes relevant scaling and environmental evidence.\\n\\n### 7. The objective statement is not fully consistent across the outline\\n\\nThe task and Point 2 correctly describe two objectives, specific energy absorption and compaction efficiency, with a peak-force constraint. The candidate evidence list instead proposes \\u201cSEA vs. peak transmitted force,\\u201d and the abstract\\u2019s expected-outcomes section similarly describes trade-offs between SEA and peak force. Those are different optimization formulations.\\n\\nThe talk must consistently distinguish:\\n\\n- **objectives:** maximize specific energy absorption and compaction efficiency;\\n- **constraint:** peak transmitted force must not exceed a specified cap;\\n- **feasible Pareto front:** trade-offs between the two objectives among designs satisfying that cap.\\n\\nIf peak force is instead treated as a third objective, the task statement and optimization formulation must be revised accordingly.\\n\\n### 8. The closing favors inspiration over technical precision\\n\\nP6 is likely to remember \\u201cdozens, not thousands,\\u201d but P1, P2, and P5 may hear overreach in \\u201cproven,\\u201d \\u201cin weeks,\\u201d and \\u201cmoves humankind forward.\\u201d A DAC-10 close should end on the measured engineering result and transfer condition. The Super Ball Bot bookend can remain, but it should not imply flight qualification.\\n\\n---\\n\\n# Three highest-priority revisions\\n\\n## Priority 1: Build the talk around a quantitative result, not around the availability of a workflow\\n\\nReplace the placeholder with a required three-slide evidence sequence:\\n\\n1. **\\u201cThe campaign used N specimens across B adaptive batches under a fixed physical-test budget.\\u201d** \\n Show initialization, adaptive batches, replicates, failures, elapsed cycle time, and the force threshold. Do not use `N` or `B` in the actual talk; insert the observed values.\\n\\n2. **\\u201cAdaptive selection improved the feasible design set faster than [predeclared baseline].\\u201d** \\n Plot a budget-matched performance measure across physical tests, with uncertainty across repeated runs if available. If only one physical campaign exists, avoid inferential claims that require repeated campaigns and supplement with clearly labeled retrospective resampling or simulation-based algorithm checks.\\n\\n3. **\\u201cThese measured designs form the final feasible trade-off between specific energy absorption and compaction efficiency.\\u201d** \\n Show uncertainty or replicate spread, mark infeasible force-cap violations, and include specimen images or force\\u2013time traces for representative Pareto designs.\\n\\nIf the campaign cannot support those statements, revise the main message from \\u201cwe can optimize \\u2026 in dozens\\u201d to \\u201cwe are evaluating whether noisy multi-objective Bayesian optimization can reduce the physical test budget.\\u201d\\n\\n## Priority 2: Replace the anti-simulation premise with a scoped engineering decision\\n\\nReplace:\\n\\n> \\u201cSimulation can\\u2019t be trusted for these structures.\\u201d\\n\\nwith:\\n\\n> \\u201cFor these multi-material prints, obtaining objective values from a validated high-fidelity model would require substantial calibration of interface, rate, contact, and defect behavior. We therefore ask how far a limited budget of direct physical tests can take us without depending on that model.\\u201d\\n\\nThen title Point 1 with a message rather than a topic:\\n\\n> **\\u201cWhen model calibration and exhaustive testing are both costly, each physical specimen must be chosen for information value.\\u201d**\\n\\nThis keeps the pincer structure while avoiding an unnecessary universal claim.\\n\\n## Priority 3: Reallocate the 15 minutes around audience decisions\\n\\nA concrete timing plan:\\n\\n- **1.5 min:** Super Ball Bot hook, specimen, and present study scope.\\n- **2 min:** Why exhaustive physical search and high-fidelity calibration are costly.\\n- **2.5 min:** One visual of the closed loop, including a one-sentence Bayesian-optimization explanation.\\n- **2 min:** Actual design variables, objectives, force constraint, fabrication failures, and noise controls.\\n- **5 min:** measured results, baseline, uncertainty, representative specimens, and failure modes.\\n- **1 min:** limitations and transfer conditions.\\n- **1 min:** measured conclusion and bookend.\\n\\nMove kernel choices, log-space acquisitions, detailed encoding, and other implementation material to backup slides unless a result depends directly on them. This follows Doumont\\u2019s principle that each slide should communicate one interpreted message rather than expose the speaker\\u2019s notes.\\n\\n---\\n\\n# Claims needing evidence, hedging, or backup slides\\n\\n| Claim or topic | What is needed in the main talk | Prepared backup material |\\n|---|---|---|\\n| \\u201cDozens of prints, not thousands\\u201d | Actual specimen count, definition of success, and budget-matched comparator | Full campaign ledger; sensitivity to initialization and stopping rule |\\n| \\u201cSimulation can\\u2019t be trusted\\u201d | Replace with scoped calibration-cost language unless direct validation evidence exists | Simulation\\u2013experiment traces, ranking errors, or documented failure modes a tested model missed |\\n| \\u201cqNEHVI handles heteroscedastic noise\\u201d | Specify the actual observation-noise model and replication strategy | Likelihood formulation, fixed versus inferred noise, residual diagnostics, replicate variance by design or batch |\\n| qNEHVI \\u201cover standard expected improvement\\u201d | Use a technically appropriate comparison; ordinary expected improvement is not a like-for-like multi-objective batch baseline | Comparisons with random/Sobol, scalarization, qEHVI or another relevant noisy/constrained baseline |\\n| \\u201cThe Pareto front is the design deliverable\\u201d | Show the two stated objectives and force-cap feasibility consistently | Hypervolume reference point, normalization, uncertainty, constraint treatment |\\n| Peak-force cap | State its numerical value and engineering rationale | Sensitivity of selected designs to alternative cap values |\\n| Probabilistic print-feasibility constraint | Report failures and demonstrate that the classifier or constraint model is identifiable at the available sample size | Failure labels, calibration, confusion or reliability metrics, acquisition formula |\\n| \\u201cNo assembly\\u201d / \\u201celiminates assembly entirely\\u201d | List any post-print operations; use \\u201csingle-build co-fabrication\\u201d if manual steps remain | Fabrication workflow photographs and labor-time accounting |\\n| \\u201cCandidate goes to tested specimen in hours\\u201d | Median or representative print-to-result time and what remains manual | Time breakdown for design, slicing, printing, conditioning, inspection, and testing |\\n| \\u201cDramatically faster\\u201d | Define faster relative to a measured or estimated comparator | Cost and wall-clock accounting with assumptions |\\n| Transfer to other architectures | Present as a conditional hypothesis, not a demonstrated result | Applicability checklist: test cost, dimensionality, noise, throughput, and constraint structure |\\n| Planetary-landing relevance | Clearly state that this is motivation unless scale and environment are tested | Impact similitude, energy and velocity regime, environmental gaps, technology-readiness discussion |\\n| PLA\\u2013TPU interface robustness | Failure-mode and repeatability evidence | Microscopy or fracture images, process parameters, conditioning and moisture controls |\\n| \\u201cTensegrity-inspired\\u201d | Show the load path and identify which tensegrity features are retained | Geometric definition, pre-tension status, comparison with a conventional lattice/control |\\n| Independent Gaussian processes | State why cross-objective correlation is ignored and whether that choice matters | Residual correlations and sensitivity to multi-output alternatives |\\n| Mixed categorical variables | Briefly state how candidates are represented and optimized | Kernel or encoding details; treatment of invalid combinations |\\n\\n---\\n\\n# Fit to the six-person audience\\n\\n## Best served: P6, the friendly industry generalist\\n\\nThe outline has a clear problem\\u2013method\\u2013application arc, a memorable loop, and an attractive efficiency promise. P6 can understand the proposed value without needing the mathematics. The danger is that this persona may accept unsupported claims and overestimate the system\\u2019s automation and maturity.\\n\\nP4 also benefits from the story structure, but the unexplained terminology and absent physical-mechanism visual will cause substantial loss of detail.\\n\\n## Worst served: P1, the skeptical Bayesian-optimization insider\\n\\nP1 is the most likely to judge the current talk as an off-the-shelf acquisition function applied to a new artifact. The outline anticipates this concern by saying not to teach Bayesian optimization, but it does not yet supply the evidence P1 needs: novelty positioning, rigorous baseline, budget, noise identification, constraint diagnostics, and measured sample efficiency.\\n\\nP5 is nearly as poorly served because the opening motivation directly challenges finite-element modeling more strongly than the abstract supports.\\n\\n## Is that the right trade-off for DAC-10?\\n\\nNo. A DAC-10 presentation should remain accessible to P2\\u2013P6, but it cannot underserve P1. The design-automation expert is likely to shape the technical discussion and the perceived contribution. Accessibility should come from a plain-language loop and strong visuals, not from omitting methodological validation. The best target is a two-layer talk: every listener can follow the engineering decision, while experts can see the experimental budget, comparator, noise model, and constraint formulation.\\n\\n---\\n\\n# Predicted reception\\n\\n**Current outline: 5/10.** The loop and application are memorable, but without measured results, a budget-matched baseline, and narrower simulation and planetary claims, the talk reads as a polished motivation for an experiment rather than a completed DAC-10 contribution.\\n\\nIf the results establish repeatability and budgeted improvement, and the three priority revisions are made, the same structure could plausibly move into the **7\\u20138/10** range. That conditional estimate is a judgment about presentation readiness, not a prediction of unobserved experimental outcomes.\\n\\n---\\n\\n# Discretionary analytical decisions\\n\\n- Treated the unfinished evidence/results section as missing evidence rather than assuming that the planned campaign succeeds.\\n- Evaluated the artifact as a competent 15-minute delivery from the outline, not as a line edit of the outline or an assessment of slides that do not yet exist.\\n- Weighted technical credibility and evidentiary support more heavily than visual polish because the target is the DAC-10 design-optimization audience.\\n- Used the submitted abstract to resolve technical intent when the outline was ambiguous, while flagging inconsistencies rather than silently harmonizing them.\\n- Interpreted the stated optimization problem as two objectives, specific energy absorption and compaction efficiency, subject to a peak-force constraint; flagged language that instead treats peak force as an objective.\\n- Assigned qualitative damage levels of moderate, moderate-to-severe, or severe based on how directly an unanswered challenge would undermine each persona\\u2019s acceptance of the central claim.\\n- Chose P6 as the best-served persona because the present outline emphasizes an intuitive workflow and practical efficiency promise; chose P1 as the worst-served because the DAC-specific validation and novelty case is not yet developed.\\n- Scored predicted reception on the current incomplete outline, not on a hypothetical final talk with successful results.\"}", + "name": "submit_answer" + } + } + ] + }, + "logprob": null + } + } + }, + "created_at": "2026-07-18T17:57:23.704939Z" + }, + { + "id": "0a2e832f-d67d-46bb-8b24-f55f0af71cf5", + "trajectory_id": "4949af58-f08a-4541-ad1c-1f0a95c6f07d", + "trajectory_timestep": 0, + "step": "after_env_step", + "state": { + "transition": { + "timestep": 0, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "info": null + }, + { + "role": "assistant", + "content": "I\u2019ll evaluate the outline as a delivered 15-minute talk, treating the unfinished results section as a substantive gap rather than inventing outcomes. I\u2019ll score each persona against comprehension, credibility, relevance, and recall, then synthesize revisions for DAC-10.", + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "info": null + }, + { + "role": "assistant", + "content": "I\u2019ll evaluate the outline as a delivered 15-minute talk, treating the unfinished results section as a substantive gap rather than inventing outcomes. I\u2019ll score each persona against comprehension, credibility, relevance, and recall, then synthesize revisions for DAC-10.", + "info": null + }, + { + "role": "user", + "content": "\nMessage was NOT sent to user. Please call the send_message tool to communicate with the user. Use wait=True if you need a response before proceeding, or wait=False if you are providing a status update to the user.", + "info": { + "is_human": false + } + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_zSHcqZDq4Rrb984ui6lpQugL", + "type": "function", + "function": { + "arguments": "{\"answer\": \"# Mock audience report: IDETC-CIE 2026 DAC-10 talk\\n\\n## Scope of this review\\n\\nThis evaluates the talk implied by the outline, not finished slides or delivery technique. The experimental campaign is still in progress, so I do **not** infer any favorable results. The empty \\u201cEvidence / results\\u201d section is treated as a major unresolved part of the talk: the outline currently explains why the workflow should be useful, but does not yet demonstrate that it is useful.\\n\\nThe central communication test is whether each listener leaves with the intended message:\\n\\n> By closing the loop between multi-material 3D printing and Bayesian optimization, we can optimize tensegrity energy absorbers directly from real impact data, in dozens of prints rather than thousands.\\n\\n---\\n\\n# P1. The skeptical Bayesian-optimization insider\\n\\n## (a) First-person reaction\\n\\nThe application is potentially interesting, and the design\\u2013print\\u2013test loop is easy to understand. The mixed continuous, integer, and categorical design space, physical failures, parallel fabrication, and multiple constrained objectives could make this a worthwhile design-automation case study.\\n\\nBut the outline keeps telling me that Bayesian optimization is sample-efficient without showing that **this implementation** is sample-efficient. \\u201cDozens, not thousands\\u201d sounds like a slogan unless I see the actual experiment count, initialization size, batch size, replication policy, convergence behavior, and a comparison against credible alternatives. q-noisy expected hypervolume improvement, or qNEHVI, is established machinery. Merely deploying it does not establish methodological novelty.\\n\\nI become skeptical at the statement that qNEHVI was selected because of heteroscedastic TPU and print noise. qNEHVI accounts for noisy observations, but the outline does not explain how the surrogate models heteroscedasticity, whether repeated specimens identify noise, or whether noise is being conflated with unmodeled process drift. Independent Gaussian processes with an unspecified observation model do not automatically resolve batch-dependent heteroscedasticity.\\n\\nMy attention drifts during the broad argument against simulation because it delays the information I came to DAC-10 to hear: the design variables, model formulation, constraint handling, acquisition implementation, budget, and benchmark. The Super Ball Bot hook is visually effective, but planetary landing may feel like application theater if the actual specimens and impact regime are far removed from that use case.\\n\\nThe strongest possible section is the results slot. In its current empty state, the talk has a method narrative but no validation.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey used standard batch noisy multi-objective Bayesian optimization to choose which multi-material printed impact specimens to test, instead of running a full physical sweep.\\u201d\\n\\n**Distortion from the intended message:** I would omit the claim that the structures were optimized in dozens of prints because I have not seen evidence for it. I would also describe qNEHVI as standard rather than as the contribution.\\n\\n## (c) Top three Q&A questions\\n\\n1. **What is the actual experimental budget?** How many initial designs, adaptive iterations, specimens per batch, and physical replicates were used, and what stopping rule defined success?\\n2. **What baselines did you beat?** Did you compare qNEHVI with Sobol or Latin-hypercube sampling, random search, scalarized Bayesian optimization, or a domain-informed sequential design under the same number of physical tests?\\n3. **How exactly is noise modeled?** Are replicate-dependent variances supplied to the Gaussian processes, is a heteroscedastic likelihood fitted, and how are TPU lot, print batch, machine drift, and specimen-level defects distinguished?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhat did you contribute beyond applying off-the-shelf qNEHVI to a new specimen family?\\u201d\\n\\n**Damage if unanswered: severe.** For a DAC review audience, this can reduce the work from a design-automation contribution to an application demonstration. A strong answer could rest on the experimental formulation, mixed-variable and failure-aware implementation, quantitative sample-efficiency evidence, and transferable operating lessons. Those contributions must be stated explicitly and tested.\\n\\n---\\n\\n# P2. The aerospace entry, descent, and landing practitioner\\n\\n## (a) First-person reaction\\n\\nThe opening lands immediately. I recognize the Super Ball Bot concept, and \\u201cthe lander is the energy absorber\\u201d is a useful framing. The three performance quantities also sound relevant: energy absorbed per mass, usable stroke, and force transmitted to the payload.\\n\\nThen the talk moves into language I do not naturally use: Gaussian-process surrogate, qNEHVI, acquisition function, hypervolume, exploration versus exploitation, and Pareto front. I can accept that the algorithm chooses the next tests, but I need a plain explanation of what information it uses and why I should trust the recommendations. If the presenter spends several minutes naming algorithms without connecting them to engineering decisions, I will lose the thread.\\n\\nMy main concern is application fidelity. A desktop drop-weight test at one fixed impact energy is not automatically evidence for entry, descent, and landing hardware. I need specimen dimensions, impact mass, velocity, energy, strain rate, boundary conditions, number of impacts, rebound behavior, temperature, vacuum sensitivity, and failure containment. PLA and TPU raise immediate environmental and qualification questions. I also want to know whether the absorber is reusable or sacrificial.\\n\\nMy attention returns when I see force\\u2013time traces, high-speed footage, and damaged specimens. It drifts if the talk stays on the optimizer rather than connecting each output to payload protection.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey have an algorithm that learns from successive drop tests and tells them which 3D-printed crush structure to make next, so they can find a lightweight low-force design with fewer prototypes.\\u201d\\n\\n**Distortion from the intended message:** I would probably call the structures \\u201ccrush structures,\\u201d losing the tensegrity distinction. I might also overgeneralize the method to planetary landers even though the current tests appear to be laboratory-scale.\\n\\n## (c) Top three Q&A questions\\n\\n1. What impact mass, velocity, energy, and strain-rate regime do the tests cover, and how do those values compare with a credible lander load case?\\n2. How do the selected designs scale with size and impact energy? Are the controlling mechanisms material-rate effects, geometric buckling, contact, or all three?\\n3. What happens under off-axis impact, repeated impacts, temperature extremes, aging, vacuum, and manufacturing defects?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhy should I believe that a PLA\\u2013TPU specimen optimized at one laboratory impact condition tells me anything about flight-scale landing protection?\\u201d\\n\\n**Damage if unanswered: severe for the planetary-landing framing, moderate for the underlying optimization study.** The presenter should clearly label planetary landing as motivation, not demonstrated readiness, and state the present technology scope. Otherwise the opening and closing overpromise relative to the reported experiment.\\n\\n---\\n\\n# P3. The additive-manufacturing and materials researcher\\n\\n## (a) First-person reaction\\n\\nThe monolithic PLA\\u2013TPU architecture is the part I care about. The core-wrapping strategy sounds more credible than simply depositing TPU against exposed PLA and assuming durable adhesion. Holding print settings fixed within a batch, weighing specimens, checking dimensions, and periodically testing a control are all sensible.\\n\\nI still need much more fabrication detail before I accept that design geometry is the main source of performance variation. PLA\\u2013TPU interfaces are sensitive to temperature, surface contact, moisture, residence time, toolpath order, contamination, and local geometry. \\u201cPrint defects\\u201d is too broad. I want failure-mode evidence showing whether energy is absorbed through intended tension-network deformation, TPU hysteresis, strut buckling, interface peeling, or accidental damage.\\n\\nThe phrase \\u201cno assembly\\u201d catches my attention, but \\u201celiminates assembly entirely\\u201d may be too strong. Does the build require support removal, trimming, conditioning, fixture insertion, post-tensioning, or manual alignment? If so, the accurate claim is \\u201csingle-build co-fabrication\\u201d or \\u201cno joining of PLA and TPU members,\\u201d not zero assembly.\\n\\nI am a Bayesian-optimization novice. I can follow the loop if the presenter says, \\u201cThe model proposes the next small batch where improvement is plausible or uncertainty is high.\\u201d I will disengage if the slide foregrounds qNEHVI notation rather than the physical decisions it enables.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey co-print rigid PLA and flexible TPU energy absorbers, test them, and use the results to choose the next geometries instead of printing a large design-of-experiments grid.\\u201d\\n\\n**Distortion from the intended message:** I would remember the fabrication loop more strongly than tensegrity or Bayesian optimization. I might interpret the improvement as mostly a material-interface result unless the structural mechanisms are shown.\\n\\n## (c) Top three Q&A questions\\n\\n1. How was PLA\\u2013TPU bonding quantified, and where did failed specimens actually fail: within TPU, within PLA, or at the interface?\\n2. How many nominally identical replicates were printed and tested, and how large were within-batch, between-batch, and printer-drift effects relative to design effects?\\n3. What manual operations occur after printing, and what exactly does \\u201cno assembly\\u201d exclude?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cHow do you know the optimizer is learning architecture rather than uncontrolled interface quality and process drift?\\u201d\\n\\n**Damage if unanswered: severe.** If fabrication variation is not measured and separated from design effects, both the surrogate and the claimed Pareto front may be unstable. Replicates, blocking or covariates, control specimens, dimensional measurements, and failure-mode documentation are needed.\\n\\n---\\n\\n# P4. The first-year graduate student\\n\\n## (a) First-person reaction\\n\\nThe opening video and the phrase \\u201cthe lander is the energy absorber\\u201d give me a concrete picture. I understand the basic problem: too many designs to print, and computer models may miss messy material behavior. I also understand the loop when it is described as \\u201cdesign, print, drop, learn, repeat.\\u201d That story carries me.\\n\\nI start getting lost when several unfamiliar terms arrive together: tensegrity-inspired, compaction efficiency, Gaussian process, surrogate, acquisition function, qNEHVI, heteroscedastic noise, Pareto front, probabilistic feasibility constraint, and exploration versus exploitation. The preview says the audience already believes optimization works, but I still need one intuitive sentence explaining what Bayesian optimization does. Skipping \\u201cBayesian optimization 101\\u201d should not mean skipping the conceptual bridge.\\n\\nI am also unsure how the structure works. Are the TPU parts stretched before impact? Are the PLA struts touching? What makes this \\u201ctensegrity-inspired\\u201d rather than a multi-material lattice? A labeled specimen image and a short deformation sequence would help much more than a verbal definition.\\n\\nMy attention drifts during the repeated case against simulation because I accepted that point the first time. It returns for printing footage, the drop test, and a clear plot with a marked \\u201cbetter\\u201d direction.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey let an artificial-intelligence program learn from 3D-printed drop tests and keep suggesting better shock absorbers, so they do not have to test every design.\\u201d\\n\\n**Distortion from the intended message:** I would probably call Bayesian optimization \\u201cAI,\\u201d omit the two-objective trade-off and force constraint, and assume each new design is simply better than the last. I might also miss what tensegrity contributes.\\n\\n## (c) Top three Q&A questions\\n\\n1. What makes one of these specimens a tensegrity structure rather than an ordinary 3D-printed lattice?\\n2. How does the computer decide which design to print next, in plain language?\\n3. Can one design maximize both energy absorption and compaction efficiency while also lowering peak force, or must those goals trade off?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cI followed the loop, but I still do not understand what is physically special about the structure or what the optimizer actually learned.\\u201d\\n\\n**Damage if unanswered: moderate.** Experts can fill in some gaps, but a talk that leaves novices with only \\u201cAI plus 3D printing\\u201d has failed to communicate its engineering mechanism. One specimen-mechanics visual and one plain-language optimization visual would largely solve this.\\n\\n---\\n\\n# P5. The finite-element-analysis veteran\\n\\n## (a) First-person reaction\\n\\nThe statement \\u201csimulation can\\u2019t be trusted for these structures\\u201d puts me on the defensive before I have seen any evidence. Interfacial slip, viscoelasticity, large deformation, buckling, contact, strain-rate dependence, and manufacturing variability are difficult, but they are not intrinsically beyond finite-element analysis. Models can be calibrated, uncertainty can be propagated, and simulation can still screen designs even if it is imperfect.\\n\\nThe abstract is more defensible than the talk outline. It says the workflow avoids **direct dependence on calibrated finite-element simulation for objective evaluation** and acknowledges future analytical or multifidelity shortcuts. The presentation should use that language. The current outline turns a project-scope choice into a universal technical claim.\\n\\nI accept the economic argument that calibrating a high-fidelity model may not be worthwhile for an early design campaign. That is a stronger position: measured tests provide authoritative objective values, while the study asks whether sequential design can use a small physical budget efficiently. I would also welcome evidence that simulation errors change design rankings or miss observed failure modes. Without such evidence, the repeated attack on simulation sounds like a straw man.\\n\\nMy attention drifts when the talk says simulation is unreliable several times without showing a simulation\\u2013experiment discrepancy. It returns when the presenter discusses physical failure modes and admits where lower-fidelity physics could later improve the loop.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey skipped finite-element modeling and used Bayesian optimization directly on physical tests because they judged model calibration too expensive for these multi-material specimens.\\u201d\\n\\n**Distortion from the intended message:** I would frame the work as bypassing simulation rather than as enabling rapid experimental optimization. If the presenter is combative about simulation, that dispute may become the only thing I remember.\\n\\n## (c) Top three Q&A questions\\n\\n1. What evidence shows that a calibrated finite-element model is insufficient for design ranking, rather than merely costly to construct?\\n2. Did you compare the physical-test-only workflow with a simple mechanics model or multifidelity surrogate that could reduce the experimental budget further?\\n3. Which observed behaviors dominate the model discrepancy: interface failure, TPU viscoelasticity, contact, buckling imperfections, or geometry errors from printing?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cYou have shown that simulation is inconvenient, not that it cannot be trusted. Why is abandoning it scientifically preferable to calibrating and validating it?\\u201d\\n\\n**Damage if unanswered: severe and avoidable.** The current absolute wording invites a technical fight that is unnecessary to the contribution. Reframing the claim as a cost-and-dependence decision would preserve the motivation without asserting that finite-element modeling is incapable.\\n\\n---\\n\\n# P6. The friendly industry generalist\\n\\n## (a) First-person reaction\\n\\nThe loop is memorable: print a candidate, test it, update the model, and print the next informative candidate. I can imagine using that pattern for brackets, seals, lattice pads, or process settings when simulation is weak and tests are expensive. \\u201cDozens rather than thousands\\u201d is exactly the kind of value proposition I remember.\\n\\nI do not need the mathematical details of qNEHVI, but I need an operational recipe. What software is used? How many initial samples are required? How automated is the handoff from optimizer to computer-aided design and slicing? How long does one cycle take? What expertise does a team need? The abstract says candidate selection is automated but slicing, specimen handling, and testing remain manual. That qualification should appear in the talk before I infer a robotic self-driving laboratory.\\n\\nThe tensegrity and planetary material is interesting, but my attention will drift if it occupies too much of a 15-minute slot. The method becomes useful to me only when the presenter explicitly generalizes it and defines when it is worth using.\\n\\nThe ending about moving humankind forward is less memorable than a concrete deployment rule. I would prefer a final slide saying: \\u201cUse this loop when tests are authoritative, each test is costly, the design space is mixed, and several performance goals conflict.\\u201d\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey showed a practical test-driven optimization loop that can find good 3D-printed designs with a few dozen prototypes instead of a giant test matrix.\\u201d\\n\\n**Distortion from the intended message:** I may overstate automation and assume the \\u201cfew dozen\\u201d claim is established across applications. I will probably forget the names qNEHVI and tensegrity, but retain the workflow.\\n\\n## (c) Top three Q&A questions\\n\\n1. What parts of the workflow are automated today, and what does an engineer still do manually between recommendation and test result?\\n2. What minimum experiment budget and software stack would my team need to try this on a different component?\\n3. When is this approach better than a conventional design of experiments, response-surface method, or a calibrated simulation?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhat concrete evidence tells me this saved enough prototypes and engineering time to justify the added optimization infrastructure?\\u201d\\n\\n**Damage if unanswered: moderate to severe.** I will still like the idea, but I will not take it back to my team. A budget-matched baseline and a simple timeline or cost comparison would convert interest into action.\\n\\n---\\n\\n# Synthesis\\n\\n## Cross-persona themes\\n\\n### 1. The causal chain is understandable, but the claimed payoff is not yet demonstrated\\n\\nAll six personas can understand the basic design\\u2013print\\u2013test\\u2013learn loop. The empty results section prevents them from deciding whether the loop actually found better designs, found them faster, or handled noise reliably. The phrase \\u201cdozens, not thousands\\u201d is currently an unsupported quantitative claim.\\n\\nAt minimum, the results need to show:\\n\\n- total number of unique designs and physical specimens;\\n- initial versus adaptively selected designs;\\n- batch size and number of iterations;\\n- replicate policy and failure count;\\n- measured uncertainty or repeatability;\\n- progress under the actual experimental budget;\\n- the final feasible Pareto set for **specific energy absorption and compaction efficiency subject to the force cap**;\\n- a budget-matched nonadaptive or simpler optimization baseline.\\n\\n### 2. \\u201cSimulation can\\u2019t be trusted\\u201d is too absolute\\n\\nP1 questions the evidence, P2 cares about application validity, and P5 directly rejects the premise. Even P6 needs to know when the loop is preferable to simulation. The abstract already contains better wording: physical measurements are used for objective evaluation \\u201cwithout relying on calibrated finite-element simulation.\\u201d\\n\\nThe defensible claim is not that simulation fails universally. It is that a sufficiently calibrated multiphysics model may be expensive relative to the available design campaign, and the authors therefore test whether physical-data-driven sequential optimization is useful without depending on such a model.\\n\\n### 3. The talk needs a two-level explanation of Bayesian optimization\\n\\nP1 does not want introductory Bayesian-optimization material; P2, P3, P4, and P5 need a plain conceptual bridge. These needs are compatible. Give the intuition in one sentence and one visual, then put the implementation specifics in a compact technical panel or backup slide.\\n\\nSuggested spoken explanation:\\n\\n> \\u201cAfter each batch, the model estimates both expected performance and uncertainty across the design space. The acquisition rule selects a small next batch that is most likely to expand the feasible trade-off frontier, while accounting for noisy tests and print failures.\\u201d\\n\\nThis is not \\u201cBayesian optimization 101.\\u201d It is the minimum explanation needed to interpret the workflow.\\n\\n### 4. The physical mechanism and the word \\u201ctensegrity\\u201d need clarification\\n\\nP2 may reduce the objects to crush structures, P3 may interpret the result as an interface study, and P4 may not distinguish the specimens from ordinary lattices. A labeled as-printed specimen plus a four-frame deformation sequence should show:\\n\\n- rigid PLA struts;\\n- continuous flexible TPU network;\\n- whether and how pre-tension exists;\\n- load path before and during impact;\\n- intended energy-dissipation mechanisms;\\n- why \\u201ctensegrity-inspired\\u201d is the precise term.\\n\\n### 5. Manufacturing variability is part of the model, not background noise\\n\\nP1 and P3 will both press this point. Periodically retesting one control is useful but may not identify design-specific heteroscedasticity, lot effects, or confounding between iteration and material batch. The talk needs to state the replication, randomization, blocking, covariate, and drift-monitoring plan. If those steps were not used, the limitation must be explicit.\\n\\n### 6. The application framing outruns the demonstrated scope\\n\\nThe planetary hook is strong, but P2 will not equate fixed-energy laboratory tests on PLA\\u2013TPU specimens with flight hardware. The closing escalates further by claiming that the next lander absorber can be \\u201cproven\\u201d in weeks. That wording should be narrowed unless the campaign includes relevant scaling and environmental evidence.\\n\\n### 7. The objective statement is not fully consistent across the outline\\n\\nThe task and Point 2 correctly describe two objectives, specific energy absorption and compaction efficiency, with a peak-force constraint. The candidate evidence list instead proposes \\u201cSEA vs. peak transmitted force,\\u201d and the abstract\\u2019s expected-outcomes section similarly describes trade-offs between SEA and peak force. Those are different optimization formulations.\\n\\nThe talk must consistently distinguish:\\n\\n- **objectives:** maximize specific energy absorption and compaction efficiency;\\n- **constraint:** peak transmitted force must not exceed a specified cap;\\n- **feasible Pareto front:** trade-offs between the two objectives among designs satisfying that cap.\\n\\nIf peak force is instead treated as a third objective, the task statement and optimization formulation must be revised accordingly.\\n\\n### 8. The closing favors inspiration over technical precision\\n\\nP6 is likely to remember \\u201cdozens, not thousands,\\u201d but P1, P2, and P5 may hear overreach in \\u201cproven,\\u201d \\u201cin weeks,\\u201d and \\u201cmoves humankind forward.\\u201d A DAC-10 close should end on the measured engineering result and transfer condition. The Super Ball Bot bookend can remain, but it should not imply flight qualification.\\n\\n---\\n\\n# Three highest-priority revisions\\n\\n## Priority 1: Build the talk around a quantitative result, not around the availability of a workflow\\n\\nReplace the placeholder with a required three-slide evidence sequence:\\n\\n1. **\\u201cThe campaign used N specimens across B adaptive batches under a fixed physical-test budget.\\u201d** \\n Show initialization, adaptive batches, replicates, failures, elapsed cycle time, and the force threshold. Do not use `N` or `B` in the actual talk; insert the observed values.\\n\\n2. **\\u201cAdaptive selection improved the feasible design set faster than [predeclared baseline].\\u201d** \\n Plot a budget-matched performance measure across physical tests, with uncertainty across repeated runs if available. If only one physical campaign exists, avoid inferential claims that require repeated campaigns and supplement with clearly labeled retrospective resampling or simulation-based algorithm checks.\\n\\n3. **\\u201cThese measured designs form the final feasible trade-off between specific energy absorption and compaction efficiency.\\u201d** \\n Show uncertainty or replicate spread, mark infeasible force-cap violations, and include specimen images or force\\u2013time traces for representative Pareto designs.\\n\\nIf the campaign cannot support those statements, revise the main message from \\u201cwe can optimize \\u2026 in dozens\\u201d to \\u201cwe are evaluating whether noisy multi-objective Bayesian optimization can reduce the physical test budget.\\u201d\\n\\n## Priority 2: Replace the anti-simulation premise with a scoped engineering decision\\n\\nReplace:\\n\\n> \\u201cSimulation can\\u2019t be trusted for these structures.\\u201d\\n\\nwith:\\n\\n> \\u201cFor these multi-material prints, obtaining objective values from a validated high-fidelity model would require substantial calibration of interface, rate, contact, and defect behavior. We therefore ask how far a limited budget of direct physical tests can take us without depending on that model.\\u201d\\n\\nThen title Point 1 with a message rather than a topic:\\n\\n> **\\u201cWhen model calibration and exhaustive testing are both costly, each physical specimen must be chosen for information value.\\u201d**\\n\\nThis keeps the pincer structure while avoiding an unnecessary universal claim.\\n\\n## Priority 3: Reallocate the 15 minutes around audience decisions\\n\\nA concrete timing plan:\\n\\n- **1.5 min:** Super Ball Bot hook, specimen, and present study scope.\\n- **2 min:** Why exhaustive physical search and high-fidelity calibration are costly.\\n- **2.5 min:** One visual of the closed loop, including a one-sentence Bayesian-optimization explanation.\\n- **2 min:** Actual design variables, objectives, force constraint, fabrication failures, and noise controls.\\n- **5 min:** measured results, baseline, uncertainty, representative specimens, and failure modes.\\n- **1 min:** limitations and transfer conditions.\\n- **1 min:** measured conclusion and bookend.\\n\\nMove kernel choices, log-space acquisitions, detailed encoding, and other implementation material to backup slides unless a result depends directly on them. This follows Doumont\\u2019s principle that each slide should communicate one interpreted message rather than expose the speaker\\u2019s notes.\\n\\n---\\n\\n# Claims needing evidence, hedging, or backup slides\\n\\n| Claim or topic | What is needed in the main talk | Prepared backup material |\\n|---|---|---|\\n| \\u201cDozens of prints, not thousands\\u201d | Actual specimen count, definition of success, and budget-matched comparator | Full campaign ledger; sensitivity to initialization and stopping rule |\\n| \\u201cSimulation can\\u2019t be trusted\\u201d | Replace with scoped calibration-cost language unless direct validation evidence exists | Simulation\\u2013experiment traces, ranking errors, or documented failure modes a tested model missed |\\n| \\u201cqNEHVI handles heteroscedastic noise\\u201d | Specify the actual observation-noise model and replication strategy | Likelihood formulation, fixed versus inferred noise, residual diagnostics, replicate variance by design or batch |\\n| qNEHVI \\u201cover standard expected improvement\\u201d | Use a technically appropriate comparison; ordinary expected improvement is not a like-for-like multi-objective batch baseline | Comparisons with random/Sobol, scalarization, qEHVI or another relevant noisy/constrained baseline |\\n| \\u201cThe Pareto front is the design deliverable\\u201d | Show the two stated objectives and force-cap feasibility consistently | Hypervolume reference point, normalization, uncertainty, constraint treatment |\\n| Peak-force cap | State its numerical value and engineering rationale | Sensitivity of selected designs to alternative cap values |\\n| Probabilistic print-feasibility constraint | Report failures and demonstrate that the classifier or constraint model is identifiable at the available sample size | Failure labels, calibration, confusion or reliability metrics, acquisition formula |\\n| \\u201cNo assembly\\u201d / \\u201celiminates assembly entirely\\u201d | List any post-print operations; use \\u201csingle-build co-fabrication\\u201d if manual steps remain | Fabrication workflow photographs and labor-time accounting |\\n| \\u201cCandidate goes to tested specimen in hours\\u201d | Median or representative print-to-result time and what remains manual | Time breakdown for design, slicing, printing, conditioning, inspection, and testing |\\n| \\u201cDramatically faster\\u201d | Define faster relative to a measured or estimated comparator | Cost and wall-clock accounting with assumptions |\\n| Transfer to other architectures | Present as a conditional hypothesis, not a demonstrated result | Applicability checklist: test cost, dimensionality, noise, throughput, and constraint structure |\\n| Planetary-landing relevance | Clearly state that this is motivation unless scale and environment are tested | Impact similitude, energy and velocity regime, environmental gaps, technology-readiness discussion |\\n| PLA\\u2013TPU interface robustness | Failure-mode and repeatability evidence | Microscopy or fracture images, process parameters, conditioning and moisture controls |\\n| \\u201cTensegrity-inspired\\u201d | Show the load path and identify which tensegrity features are retained | Geometric definition, pre-tension status, comparison with a conventional lattice/control |\\n| Independent Gaussian processes | State why cross-objective correlation is ignored and whether that choice matters | Residual correlations and sensitivity to multi-output alternatives |\\n| Mixed categorical variables | Briefly state how candidates are represented and optimized | Kernel or encoding details; treatment of invalid combinations |\\n\\n---\\n\\n# Fit to the six-person audience\\n\\n## Best served: P6, the friendly industry generalist\\n\\nThe outline has a clear problem\\u2013method\\u2013application arc, a memorable loop, and an attractive efficiency promise. P6 can understand the proposed value without needing the mathematics. The danger is that this persona may accept unsupported claims and overestimate the system\\u2019s automation and maturity.\\n\\nP4 also benefits from the story structure, but the unexplained terminology and absent physical-mechanism visual will cause substantial loss of detail.\\n\\n## Worst served: P1, the skeptical Bayesian-optimization insider\\n\\nP1 is the most likely to judge the current talk as an off-the-shelf acquisition function applied to a new artifact. The outline anticipates this concern by saying not to teach Bayesian optimization, but it does not yet supply the evidence P1 needs: novelty positioning, rigorous baseline, budget, noise identification, constraint diagnostics, and measured sample efficiency.\\n\\nP5 is nearly as poorly served because the opening motivation directly challenges finite-element modeling more strongly than the abstract supports.\\n\\n## Is that the right trade-off for DAC-10?\\n\\nNo. A DAC-10 presentation should remain accessible to P2\\u2013P6, but it cannot underserve P1. The design-automation expert is likely to shape the technical discussion and the perceived contribution. Accessibility should come from a plain-language loop and strong visuals, not from omitting methodological validation. The best target is a two-layer talk: every listener can follow the engineering decision, while experts can see the experimental budget, comparator, noise model, and constraint formulation.\\n\\n---\\n\\n# Predicted reception\\n\\n**Current outline: 5/10.** The loop and application are memorable, but without measured results, a budget-matched baseline, and narrower simulation and planetary claims, the talk reads as a polished motivation for an experiment rather than a completed DAC-10 contribution.\\n\\nIf the results establish repeatability and budgeted improvement, and the three priority revisions are made, the same structure could plausibly move into the **7\\u20138/10** range. That conditional estimate is a judgment about presentation readiness, not a prediction of unobserved experimental outcomes.\\n\\n---\\n\\n# Discretionary analytical decisions\\n\\n- Treated the unfinished evidence/results section as missing evidence rather than assuming that the planned campaign succeeds.\\n- Evaluated the artifact as a competent 15-minute delivery from the outline, not as a line edit of the outline or an assessment of slides that do not yet exist.\\n- Weighted technical credibility and evidentiary support more heavily than visual polish because the target is the DAC-10 design-optimization audience.\\n- Used the submitted abstract to resolve technical intent when the outline was ambiguous, while flagging inconsistencies rather than silently harmonizing them.\\n- Interpreted the stated optimization problem as two objectives, specific energy absorption and compaction efficiency, subject to a peak-force constraint; flagged language that instead treats peak force as an objective.\\n- Assigned qualitative damage levels of moderate, moderate-to-severe, or severe based on how directly an unanswered challenge would undermine each persona\\u2019s acceptance of the central claim.\\n- Chose P6 as the best-served persona because the present outline emphasizes an intuitive workflow and practical efficiency promise; chose P1 as the worst-served because the DAC-specific validation and novelty case is not yet developed.\\n- Scored predicted reception on the current incomplete outline, not on a hypothetical final talk with successful results.\"}", + "name": "submit_answer" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "user", + "content": "\nMessage was NOT sent to user. Please call the send_message tool to communicate with the user. Use wait=True if you need a response before proceeding, or wait=False if you are providing a status update to the user.", + "info": { + "is_human": false + } + } + ], + "next_observation": [ + { + "role": "tool", + "content": "# Mock audience report: IDETC-CIE 2026 DAC-10 talk\n\n## Scope of this review\n\nThis evaluates the talk implied by the outline, not finished slides or delivery technique. The experimental campaign is still in progress, so I do **not** infer any favorable results. The empty \u201cEvidence / results\u201d section is treated as a major unresolved part of the talk: the outline currently explains why the workflow should be useful, but does not yet demonstrate that it is useful.\n\nThe central communication test is whether each listener leaves with the intended message:\n\n> By closing the loop between multi-material 3D printing and Bayesian optimization, we can optimize tensegrity energy absorbers directly from real impact data, in dozens of prints rather than thousands.\n\n---\n\n# P1. The skeptical Bayesian-optimization insider\n\n## (a) First-person reaction\n\nThe application is potentially interesting, and the design\u2013print\u2013test loop is easy to understand. The mixed continuous, integer, and categorical design space, physical failures, parallel fabrication, and multiple constrained objectives could make this a worthwhile design-automation case study.\n\nBut the outline keeps telling me that Bayesian optimization is sample-efficient without showing that **this implementation** is sample-efficient. \u201cDozens, not thousands\u201d sounds like a slogan unless I see the actual experiment count, initialization size, batch size, replication policy, convergence behavior, and a comparison against credible alternatives. q-noisy expected hypervolume improvement, or qNEHVI, is established machinery. Merely deploying it does not establish methodological novelty.\n\nI become skeptical at the statement that qNEHVI was selected because of heteroscedastic TPU and print noise. qNEHVI accounts for noisy observations, but the outline does not explain how the surrogate models heteroscedasticity, whether repeated specimens identify noise, or whether noise is being conflated with unmodeled process drift. Independent Gaussian processes with an unspecified observation model do not automatically resolve batch-dependent heteroscedasticity.\n\nMy attention drifts during the broad argument against simulation because it delays the information I came to DAC-10 to hear: the design variables, model formulation, constraint handling, acquisition implementation, budget, and benchmark. The Super Ball Bot hook is visually effective, but planetary landing may feel like application theater if the actual specimens and impact regime are far removed from that use case.\n\nThe strongest possible section is the results slot. In its current empty state, the talk has a method narrative but no validation.\n\n## (b) What I would repeat the next day\n\n> \u201cThey used standard batch noisy multi-objective Bayesian optimization to choose which multi-material printed impact specimens to test, instead of running a full physical sweep.\u201d\n\n**Distortion from the intended message:** I would omit the claim that the structures were optimized in dozens of prints because I have not seen evidence for it. I would also describe qNEHVI as standard rather than as the contribution.\n\n## (c) Top three Q&A questions\n\n1. **What is the actual experimental budget?** How many initial designs, adaptive iterations, specimens per batch, and physical replicates were used, and what stopping rule defined success?\n2. **What baselines did you beat?** Did you compare qNEHVI with Sobol or Latin-hypercube sampling, random search, scalarized Bayesian optimization, or a domain-informed sequential design under the same number of physical tests?\n3. **How exactly is noise modeled?** Are replicate-dependent variances supplied to the Gaussian processes, is a heteroscedastic likelihood fitted, and how are TPU lot, print batch, machine drift, and specimen-level defects distinguished?\n\n## (d) Most pointed challenge\n\n> \u201cWhat did you contribute beyond applying off-the-shelf qNEHVI to a new specimen family?\u201d\n\n**Damage if unanswered: severe.** For a DAC review audience, this can reduce the work from a design-automation contribution to an application demonstration. A strong answer could rest on the experimental formulation, mixed-variable and failure-aware implementation, quantitative sample-efficiency evidence, and transferable operating lessons. Those contributions must be stated explicitly and tested.\n\n---\n\n# P2. The aerospace entry, descent, and landing practitioner\n\n## (a) First-person reaction\n\nThe opening lands immediately. I recognize the Super Ball Bot concept, and \u201cthe lander is the energy absorber\u201d is a useful framing. The three performance quantities also sound relevant: energy absorbed per mass, usable stroke, and force transmitted to the payload.\n\nThen the talk moves into language I do not naturally use: Gaussian-process surrogate, qNEHVI, acquisition function, hypervolume, exploration versus exploitation, and Pareto front. I can accept that the algorithm chooses the next tests, but I need a plain explanation of what information it uses and why I should trust the recommendations. If the presenter spends several minutes naming algorithms without connecting them to engineering decisions, I will lose the thread.\n\nMy main concern is application fidelity. A desktop drop-weight test at one fixed impact energy is not automatically evidence for entry, descent, and landing hardware. I need specimen dimensions, impact mass, velocity, energy, strain rate, boundary conditions, number of impacts, rebound behavior, temperature, vacuum sensitivity, and failure containment. PLA and TPU raise immediate environmental and qualification questions. I also want to know whether the absorber is reusable or sacrificial.\n\nMy attention returns when I see force\u2013time traces, high-speed footage, and damaged specimens. It drifts if the talk stays on the optimizer rather than connecting each output to payload protection.\n\n## (b) What I would repeat the next day\n\n> \u201cThey have an algorithm that learns from successive drop tests and tells them which 3D-printed crush structure to make next, so they can find a lightweight low-force design with fewer prototypes.\u201d\n\n**Distortion from the intended message:** I would probably call the structures \u201ccrush structures,\u201d losing the tensegrity distinction. I might also overgeneralize the method to planetary landers even though the current tests appear to be laboratory-scale.\n\n## (c) Top three Q&A questions\n\n1. What impact mass, velocity, energy, and strain-rate regime do the tests cover, and how do those values compare with a credible lander load case?\n2. How do the selected designs scale with size and impact energy? Are the controlling mechanisms material-rate effects, geometric buckling, contact, or all three?\n3. What happens under off-axis impact, repeated impacts, temperature extremes, aging, vacuum, and manufacturing defects?\n\n## (d) Most pointed challenge\n\n> \u201cWhy should I believe that a PLA\u2013TPU specimen optimized at one laboratory impact condition tells me anything about flight-scale landing protection?\u201d\n\n**Damage if unanswered: severe for the planetary-landing framing, moderate for the underlying optimization study.** The presenter should clearly label planetary landing as motivation, not demonstrated readiness, and state the present technology scope. Otherwise the opening and closing overpromise relative to the reported experiment.\n\n---\n\n# P3. The additive-manufacturing and materials researcher\n\n## (a) First-person reaction\n\nThe monolithic PLA\u2013TPU architecture is the part I care about. The core-wrapping strategy sounds more credible than simply depositing TPU against exposed PLA and assuming durable adhesion. Holding print settings fixed within a batch, weighing specimens, checking dimensions, and periodically testing a control are all sensible.\n\nI still need much more fabrication detail before I accept that design geometry is the main source of performance variation. PLA\u2013TPU interfaces are sensitive to temperature, surface contact, moisture, residence time, toolpath order, contamination, and local geometry. \u201cPrint defects\u201d is too broad. I want failure-mode evidence showing whether energy is absorbed through intended tension-network deformation, TPU hysteresis, strut buckling, interface peeling, or accidental damage.\n\nThe phrase \u201cno assembly\u201d catches my attention, but \u201celiminates assembly entirely\u201d may be too strong. Does the build require support removal, trimming, conditioning, fixture insertion, post-tensioning, or manual alignment? If so, the accurate claim is \u201csingle-build co-fabrication\u201d or \u201cno joining of PLA and TPU members,\u201d not zero assembly.\n\nI am a Bayesian-optimization novice. I can follow the loop if the presenter says, \u201cThe model proposes the next small batch where improvement is plausible or uncertainty is high.\u201d I will disengage if the slide foregrounds qNEHVI notation rather than the physical decisions it enables.\n\n## (b) What I would repeat the next day\n\n> \u201cThey co-print rigid PLA and flexible TPU energy absorbers, test them, and use the results to choose the next geometries instead of printing a large design-of-experiments grid.\u201d\n\n**Distortion from the intended message:** I would remember the fabrication loop more strongly than tensegrity or Bayesian optimization. I might interpret the improvement as mostly a material-interface result unless the structural mechanisms are shown.\n\n## (c) Top three Q&A questions\n\n1. How was PLA\u2013TPU bonding quantified, and where did failed specimens actually fail: within TPU, within PLA, or at the interface?\n2. How many nominally identical replicates were printed and tested, and how large were within-batch, between-batch, and printer-drift effects relative to design effects?\n3. What manual operations occur after printing, and what exactly does \u201cno assembly\u201d exclude?\n\n## (d) Most pointed challenge\n\n> \u201cHow do you know the optimizer is learning architecture rather than uncontrolled interface quality and process drift?\u201d\n\n**Damage if unanswered: severe.** If fabrication variation is not measured and separated from design effects, both the surrogate and the claimed Pareto front may be unstable. Replicates, blocking or covariates, control specimens, dimensional measurements, and failure-mode documentation are needed.\n\n---\n\n# P4. The first-year graduate student\n\n## (a) First-person reaction\n\nThe opening video and the phrase \u201cthe lander is the energy absorber\u201d give me a concrete picture. I understand the basic problem: too many designs to print, and computer models may miss messy material behavior. I also understand the loop when it is described as \u201cdesign, print, drop, learn, repeat.\u201d That story carries me.\n\nI start getting lost when several unfamiliar terms arrive together: tensegrity-inspired, compaction efficiency, Gaussian process, surrogate, acquisition function, qNEHVI, heteroscedastic noise, Pareto front, probabilistic feasibility constraint, and exploration versus exploitation. The preview says the audience already believes optimization works, but I still need one intuitive sentence explaining what Bayesian optimization does. Skipping \u201cBayesian optimization 101\u201d should not mean skipping the conceptual bridge.\n\nI am also unsure how the structure works. Are the TPU parts stretched before impact? Are the PLA struts touching? What makes this \u201ctensegrity-inspired\u201d rather than a multi-material lattice? A labeled specimen image and a short deformation sequence would help much more than a verbal definition.\n\nMy attention drifts during the repeated case against simulation because I accepted that point the first time. It returns for printing footage, the drop test, and a clear plot with a marked \u201cbetter\u201d direction.\n\n## (b) What I would repeat the next day\n\n> \u201cThey let an artificial-intelligence program learn from 3D-printed drop tests and keep suggesting better shock absorbers, so they do not have to test every design.\u201d\n\n**Distortion from the intended message:** I would probably call Bayesian optimization \u201cAI,\u201d omit the two-objective trade-off and force constraint, and assume each new design is simply better than the last. I might also miss what tensegrity contributes.\n\n## (c) Top three Q&A questions\n\n1. What makes one of these specimens a tensegrity structure rather than an ordinary 3D-printed lattice?\n2. How does the computer decide which design to print next, in plain language?\n3. Can one design maximize both energy absorption and compaction efficiency while also lowering peak force, or must those goals trade off?\n\n## (d) Most pointed challenge\n\n> \u201cI followed the loop, but I still do not understand what is physically special about the structure or what the optimizer actually learned.\u201d\n\n**Damage if unanswered: moderate.** Experts can fill in some gaps, but a talk that leaves novices with only \u201cAI plus 3D printing\u201d has failed to communicate its engineering mechanism. One specimen-mechanics visual and one plain-language optimization visual would largely solve this.\n\n---\n\n# P5. The finite-element-analysis veteran\n\n## (a) First-person reaction\n\nThe statement \u201csimulation can\u2019t be trusted for these structures\u201d puts me on the defensive before I have seen any evidence. Interfacial slip, viscoelasticity, large deformation, buckling, contact, strain-rate dependence, and manufacturing variability are difficult, but they are not intrinsically beyond finite-element analysis. Models can be calibrated, uncertainty can be propagated, and simulation can still screen designs even if it is imperfect.\n\nThe abstract is more defensible than the talk outline. It says the workflow avoids **direct dependence on calibrated finite-element simulation for objective evaluation** and acknowledges future analytical or multifidelity shortcuts. The presentation should use that language. The current outline turns a project-scope choice into a universal technical claim.\n\nI accept the economic argument that calibrating a high-fidelity model may not be worthwhile for an early design campaign. That is a stronger position: measured tests provide authoritative objective values, while the study asks whether sequential design can use a small physical budget efficiently. I would also welcome evidence that simulation errors change design rankings or miss observed failure modes. Without such evidence, the repeated attack on simulation sounds like a straw man.\n\nMy attention drifts when the talk says simulation is unreliable several times without showing a simulation\u2013experiment discrepancy. It returns when the presenter discusses physical failure modes and admits where lower-fidelity physics could later improve the loop.\n\n## (b) What I would repeat the next day\n\n> \u201cThey skipped finite-element modeling and used Bayesian optimization directly on physical tests because they judged model calibration too expensive for these multi-material specimens.\u201d\n\n**Distortion from the intended message:** I would frame the work as bypassing simulation rather than as enabling rapid experimental optimization. If the presenter is combative about simulation, that dispute may become the only thing I remember.\n\n## (c) Top three Q&A questions\n\n1. What evidence shows that a calibrated finite-element model is insufficient for design ranking, rather than merely costly to construct?\n2. Did you compare the physical-test-only workflow with a simple mechanics model or multifidelity surrogate that could reduce the experimental budget further?\n3. Which observed behaviors dominate the model discrepancy: interface failure, TPU viscoelasticity, contact, buckling imperfections, or geometry errors from printing?\n\n## (d) Most pointed challenge\n\n> \u201cYou have shown that simulation is inconvenient, not that it cannot be trusted. Why is abandoning it scientifically preferable to calibrating and validating it?\u201d\n\n**Damage if unanswered: severe and avoidable.** The current absolute wording invites a technical fight that is unnecessary to the contribution. Reframing the claim as a cost-and-dependence decision would preserve the motivation without asserting that finite-element modeling is incapable.\n\n---\n\n# P6. The friendly industry generalist\n\n## (a) First-person reaction\n\nThe loop is memorable: print a candidate, test it, update the model, and print the next informative candidate. I can imagine using that pattern for brackets, seals, lattice pads, or process settings when simulation is weak and tests are expensive. \u201cDozens rather than thousands\u201d is exactly the kind of value proposition I remember.\n\nI do not need the mathematical details of qNEHVI, but I need an operational recipe. What software is used? How many initial samples are required? How automated is the handoff from optimizer to computer-aided design and slicing? How long does one cycle take? What expertise does a team need? The abstract says candidate selection is automated but slicing, specimen handling, and testing remain manual. That qualification should appear in the talk before I infer a robotic self-driving laboratory.\n\nThe tensegrity and planetary material is interesting, but my attention will drift if it occupies too much of a 15-minute slot. The method becomes useful to me only when the presenter explicitly generalizes it and defines when it is worth using.\n\nThe ending about moving humankind forward is less memorable than a concrete deployment rule. I would prefer a final slide saying: \u201cUse this loop when tests are authoritative, each test is costly, the design space is mixed, and several performance goals conflict.\u201d\n\n## (b) What I would repeat the next day\n\n> \u201cThey showed a practical test-driven optimization loop that can find good 3D-printed designs with a few dozen prototypes instead of a giant test matrix.\u201d\n\n**Distortion from the intended message:** I may overstate automation and assume the \u201cfew dozen\u201d claim is established across applications. I will probably forget the names qNEHVI and tensegrity, but retain the workflow.\n\n## (c) Top three Q&A questions\n\n1. What parts of the workflow are automated today, and what does an engineer still do manually between recommendation and test result?\n2. What minimum experiment budget and software stack would my team need to try this on a different component?\n3. When is this approach better than a conventional design of experiments, response-surface method, or a calibrated simulation?\n\n## (d) Most pointed challenge\n\n> \u201cWhat concrete evidence tells me this saved enough prototypes and engineering time to justify the added optimization infrastructure?\u201d\n\n**Damage if unanswered: moderate to severe.** I will still like the idea, but I will not take it back to my team. A budget-matched baseline and a simple timeline or cost comparison would convert interest into action.\n\n---\n\n# Synthesis\n\n## Cross-persona themes\n\n### 1. The causal chain is understandable, but the claimed payoff is not yet demonstrated\n\nAll six personas can understand the basic design\u2013print\u2013test\u2013learn loop. The empty results section prevents them from deciding whether the loop actually found better designs, found them faster, or handled noise reliably. The phrase \u201cdozens, not thousands\u201d is currently an unsupported quantitative claim.\n\nAt minimum, the results need to show:\n\n- total number of unique designs and physical specimens;\n- initial versus adaptively selected designs;\n- batch size and number of iterations;\n- replicate policy and failure count;\n- measured uncertainty or repeatability;\n- progress under the actual experimental budget;\n- the final feasible Pareto set for **specific energy absorption and compaction efficiency subject to the force cap**;\n- a budget-matched nonadaptive or simpler optimization baseline.\n\n### 2. \u201cSimulation can\u2019t be trusted\u201d is too absolute\n\nP1 questions the evidence, P2 cares about application validity, and P5 directly rejects the premise. Even P6 needs to know when the loop is preferable to simulation. The abstract already contains better wording: physical measurements are used for objective evaluation \u201cwithout relying on calibrated finite-element simulation.\u201d\n\nThe defensible claim is not that simulation fails universally. It is that a sufficiently calibrated multiphysics model may be expensive relative to the available design campaign, and the authors therefore test whether physical-data-driven sequential optimization is useful without depending on such a model.\n\n### 3. The talk needs a two-level explanation of Bayesian optimization\n\nP1 does not want introductory Bayesian-optimization material; P2, P3, P4, and P5 need a plain conceptual bridge. These needs are compatible. Give the intuition in one sentence and one visual, then put the implementation specifics in a compact technical panel or backup slide.\n\nSuggested spoken explanation:\n\n> \u201cAfter each batch, the model estimates both expected performance and uncertainty across the design space. The acquisition rule selects a small next batch that is most likely to expand the feasible trade-off frontier, while accounting for noisy tests and print failures.\u201d\n\nThis is not \u201cBayesian optimization 101.\u201d It is the minimum explanation needed to interpret the workflow.\n\n### 4. The physical mechanism and the word \u201ctensegrity\u201d need clarification\n\nP2 may reduce the objects to crush structures, P3 may interpret the result as an interface study, and P4 may not distinguish the specimens from ordinary lattices. A labeled as-printed specimen plus a four-frame deformation sequence should show:\n\n- rigid PLA struts;\n- continuous flexible TPU network;\n- whether and how pre-tension exists;\n- load path before and during impact;\n- intended energy-dissipation mechanisms;\n- why \u201ctensegrity-inspired\u201d is the precise term.\n\n### 5. Manufacturing variability is part of the model, not background noise\n\nP1 and P3 will both press this point. Periodically retesting one control is useful but may not identify design-specific heteroscedasticity, lot effects, or confounding between iteration and material batch. The talk needs to state the replication, randomization, blocking, covariate, and drift-monitoring plan. If those steps were not used, the limitation must be explicit.\n\n### 6. The application framing outruns the demonstrated scope\n\nThe planetary hook is strong, but P2 will not equate fixed-energy laboratory tests on PLA\u2013TPU specimens with flight hardware. The closing escalates further by claiming that the next lander absorber can be \u201cproven\u201d in weeks. That wording should be narrowed unless the campaign includes relevant scaling and environmental evidence.\n\n### 7. The objective statement is not fully consistent across the outline\n\nThe task and Point 2 correctly describe two objectives, specific energy absorption and compaction efficiency, with a peak-force constraint. The candidate evidence list instead proposes \u201cSEA vs. peak transmitted force,\u201d and the abstract\u2019s expected-outcomes section similarly describes trade-offs between SEA and peak force. Those are different optimization formulations.\n\nThe talk must consistently distinguish:\n\n- **objectives:** maximize specific energy absorption and compaction efficiency;\n- **constraint:** peak transmitted force must not exceed a specified cap;\n- **feasible Pareto front:** trade-offs between the two objectives among designs satisfying that cap.\n\nIf peak force is instead treated as a third objective, the task statement and optimization formulation must be revised accordingly.\n\n### 8. The closing favors inspiration over technical precision\n\nP6 is likely to remember \u201cdozens, not thousands,\u201d but P1, P2, and P5 may hear overreach in \u201cproven,\u201d \u201cin weeks,\u201d and \u201cmoves humankind forward.\u201d A DAC-10 close should end on the measured engineering result and transfer condition. The Super Ball Bot bookend can remain, but it should not imply flight qualification.\n\n---\n\n# Three highest-priority revisions\n\n## Priority 1: Build the talk around a quantitative result, not around the availability of a workflow\n\nReplace the placeholder with a required three-slide evidence sequence:\n\n1. **\u201cThe campaign used N specimens across B adaptive batches under a fixed physical-test budget.\u201d** \n Show initialization, adaptive batches, replicates, failures, elapsed cycle time, and the force threshold. Do not use `N` or `B` in the actual talk; insert the observed values.\n\n2. **\u201cAdaptive selection improved the feasible design set faster than [predeclared baseline].\u201d** \n Plot a budget-matched performance measure across physical tests, with uncertainty across repeated runs if available. If only one physical campaign exists, avoid inferential claims that require repeated campaigns and supplement with clearly labeled retrospective resampling or simulation-based algorithm checks.\n\n3. **\u201cThese measured designs form the final feasible trade-off between specific energy absorption and compaction efficiency.\u201d** \n Show uncertainty or replicate spread, mark infeasible force-cap violations, and include specimen images or force\u2013time traces for representative Pareto designs.\n\nIf the campaign cannot support those statements, revise the main message from \u201cwe can optimize \u2026 in dozens\u201d to \u201cwe are evaluating whether noisy multi-objective Bayesian optimization can reduce the physical test budget.\u201d\n\n## Priority 2: Replace the anti-simulation premise with a scoped engineering decision\n\nReplace:\n\n> \u201cSimulation can\u2019t be trusted for these structures.\u201d\n\nwith:\n\n> \u201cFor these multi-material prints, obtaining objective values from a validated high-fidelity model would require substantial calibration of interface, rate, contact, and defect behavior. We therefore ask how far a limited budget of direct physical tests can take us without depending on that model.\u201d\n\nThen title Point 1 with a message rather than a topic:\n\n> **\u201cWhen model calibration and exhaustive testing are both costly, each physical specimen must be chosen for information value.\u201d**\n\nThis keeps the pincer structure while avoiding an unnecessary universal claim.\n\n## Priority 3: Reallocate the 15 minutes around audience decisions\n\nA concrete timing plan:\n\n- **1.5 min:** Super Ball Bot hook, specimen, and present study scope.\n- **2 min:** Why exhaustive physical search and high-fidelity calibration are costly.\n- **2.5 min:** One visual of the closed loop, including a one-sentence Bayesian-optimization explanation.\n- **2 min:** Actual design variables, objectives, force constraint, fabrication failures, and noise controls.\n- **5 min:** measured results, baseline, uncertainty, representative specimens, and failure modes.\n- **1 min:** limitations and transfer conditions.\n- **1 min:** measured conclusion and bookend.\n\nMove kernel choices, log-space acquisitions, detailed encoding, and other implementation material to backup slides unless a result depends directly on them. This follows Doumont\u2019s principle that each slide should communicate one interpreted message rather than expose the speaker\u2019s notes.\n\n---\n\n# Claims needing evidence, hedging, or backup slides\n\n| Claim or topic | What is needed in the main talk | Prepared backup material |\n|---|---|---|\n| \u201cDozens of prints, not thousands\u201d | Actual specimen count, definition of success, and budget-matched comparator | Full campaign ledger; sensitivity to initialization and stopping rule |\n| \u201cSimulation can\u2019t be trusted\u201d | Replace with scoped calibration-cost language unless direct validation evidence exists | Simulation\u2013experiment traces, ranking errors, or documented failure modes a tested model missed |\n| \u201cqNEHVI handles heteroscedastic noise\u201d | Specify the actual observation-noise model and replication strategy | Likelihood formulation, fixed versus inferred noise, residual diagnostics, replicate variance by design or batch |\n| qNEHVI \u201cover standard expected improvement\u201d | Use a technically appropriate comparison; ordinary expected improvement is not a like-for-like multi-objective batch baseline | Comparisons with random/Sobol, scalarization, qEHVI or another relevant noisy/constrained baseline |\n| \u201cThe Pareto front is the design deliverable\u201d | Show the two stated objectives and force-cap feasibility consistently | Hypervolume reference point, normalization, uncertainty, constraint treatment |\n| Peak-force cap | State its numerical value and engineering rationale | Sensitivity of selected designs to alternative cap values |\n| Probabilistic print-feasibility constraint | Report failures and demonstrate that the classifier or constraint model is identifiable at the available sample size | Failure labels, calibration, confusion or reliability metrics, acquisition formula |\n| \u201cNo assembly\u201d / \u201celiminates assembly entirely\u201d | List any post-print operations; use \u201csingle-build co-fabrication\u201d if manual steps remain | Fabrication workflow photographs and labor-time accounting |\n| \u201cCandidate goes to tested specimen in hours\u201d | Median or representative print-to-result time and what remains manual | Time breakdown for design, slicing, printing, conditioning, inspection, and testing |\n| \u201cDramatically faster\u201d | Define faster relative to a measured or estimated comparator | Cost and wall-clock accounting with assumptions |\n| Transfer to other architectures | Present as a conditional hypothesis, not a demonstrated result | Applicability checklist: test cost, dimensionality, noise, throughput, and constraint structure |\n| Planetary-landing relevance | Clearly state that this is motivation unless scale and environment are tested | Impact similitude, energy and velocity regime, environmental gaps, technology-readiness discussion |\n| PLA\u2013TPU interface robustness | Failure-mode and repeatability evidence | Microscopy or fracture images, process parameters, conditioning and moisture controls |\n| \u201cTensegrity-inspired\u201d | Show the load path and identify which tensegrity features are retained | Geometric definition, pre-tension status, comparison with a conventional lattice/control |\n| Independent Gaussian processes | State why cross-objective correlation is ignored and whether that choice matters | Residual correlations and sensitivity to multi-output alternatives |\n| Mixed categorical variables | Briefly state how candidates are represented and optimized | Kernel or encoding details; treatment of invalid combinations |\n\n---\n\n# Fit to the six-person audience\n\n## Best served: P6, the friendly industry generalist\n\nThe outline has a clear problem\u2013method\u2013application arc, a memorable loop, and an attractive efficiency promise. P6 can understand the proposed value without needing the mathematics. The danger is that this persona may accept unsupported claims and overestimate the system\u2019s automation and maturity.\n\nP4 also benefits from the story structure, but the unexplained terminology and absent physical-mechanism visual will cause substantial loss of detail.\n\n## Worst served: P1, the skeptical Bayesian-optimization insider\n\nP1 is the most likely to judge the current talk as an off-the-shelf acquisition function applied to a new artifact. The outline anticipates this concern by saying not to teach Bayesian optimization, but it does not yet supply the evidence P1 needs: novelty positioning, rigorous baseline, budget, noise identification, constraint diagnostics, and measured sample efficiency.\n\nP5 is nearly as poorly served because the opening motivation directly challenges finite-element modeling more strongly than the abstract supports.\n\n## Is that the right trade-off for DAC-10?\n\nNo. A DAC-10 presentation should remain accessible to P2\u2013P6, but it cannot underserve P1. The design-automation expert is likely to shape the technical discussion and the perceived contribution. Accessibility should come from a plain-language loop and strong visuals, not from omitting methodological validation. The best target is a two-layer talk: every listener can follow the engineering decision, while experts can see the experimental budget, comparator, noise model, and constraint formulation.\n\n---\n\n# Predicted reception\n\n**Current outline: 5/10.** The loop and application are memorable, but without measured results, a budget-matched baseline, and narrower simulation and planetary claims, the talk reads as a polished motivation for an experiment rather than a completed DAC-10 contribution.\n\nIf the results establish repeatability and budgeted improvement, and the three priority revisions are made, the same structure could plausibly move into the **7\u20138/10** range. That conditional estimate is a judgment about presentation readiness, not a prediction of unobserved experimental outcomes.\n\n---\n\n# Discretionary analytical decisions\n\n- Treated the unfinished evidence/results section as missing evidence rather than assuming that the planned campaign succeeds.\n- Evaluated the artifact as a competent 15-minute delivery from the outline, not as a line edit of the outline or an assessment of slides that do not yet exist.\n- Weighted technical credibility and evidentiary support more heavily than visual polish because the target is the DAC-10 design-optimization audience.\n- Used the submitted abstract to resolve technical intent when the outline was ambiguous, while flagging inconsistencies rather than silently harmonizing them.\n- Interpreted the stated optimization problem as two objectives, specific energy absorption and compaction efficiency, subject to a peak-force constraint; flagged language that instead treats peak force as an objective.\n- Assigned qualitative damage levels of moderate, moderate-to-severe, or severe based on how directly an unanswered challenge would undermine each persona\u2019s acceptance of the central claim.\n- Chose P6 as the best-served persona because the present outline emphasizes an intuitive workflow and practical efficiency promise; chose P1 as the worst-served because the DAC-specific validation and novelty case is not yet developed.\n- Scored predicted reception on the current incomplete outline, not on a hypothetical final talk with successful results.", + "name": "submit_answer", + "tool_call_id": "call_zSHcqZDq4Rrb984ui6lpQugL", + "info": { + "start_ts": 12012.152820776, + "end_ts": 12012.154539476 + } + } + ], + "reward": 0.0, + "truncated": false, + "done": true, + "value": 0.0, + "metadata": { + "callback_type": "after_env_step" + }, + "action": { + "call_id": { + "run_id": "fc2d1a3a-48db-409f-bfe9-7aaa8fe946b3", + "fwd_id": "6b88bc00-7c53-44b5-a7c0-37329a18fa57" + }, + "op_name": "IdentityOp-3c0dc047595c", + "op_class_name": "ldp.graph.common_ops.IdentityOp", + "value": { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_zSHcqZDq4Rrb984ui6lpQugL", + "type": "function", + "function": { + "arguments": "{\"answer\": \"# Mock audience report: IDETC-CIE 2026 DAC-10 talk\\n\\n## Scope of this review\\n\\nThis evaluates the talk implied by the outline, not finished slides or delivery technique. The experimental campaign is still in progress, so I do **not** infer any favorable results. The empty \\u201cEvidence / results\\u201d section is treated as a major unresolved part of the talk: the outline currently explains why the workflow should be useful, but does not yet demonstrate that it is useful.\\n\\nThe central communication test is whether each listener leaves with the intended message:\\n\\n> By closing the loop between multi-material 3D printing and Bayesian optimization, we can optimize tensegrity energy absorbers directly from real impact data, in dozens of prints rather than thousands.\\n\\n---\\n\\n# P1. The skeptical Bayesian-optimization insider\\n\\n## (a) First-person reaction\\n\\nThe application is potentially interesting, and the design\\u2013print\\u2013test loop is easy to understand. The mixed continuous, integer, and categorical design space, physical failures, parallel fabrication, and multiple constrained objectives could make this a worthwhile design-automation case study.\\n\\nBut the outline keeps telling me that Bayesian optimization is sample-efficient without showing that **this implementation** is sample-efficient. \\u201cDozens, not thousands\\u201d sounds like a slogan unless I see the actual experiment count, initialization size, batch size, replication policy, convergence behavior, and a comparison against credible alternatives. q-noisy expected hypervolume improvement, or qNEHVI, is established machinery. Merely deploying it does not establish methodological novelty.\\n\\nI become skeptical at the statement that qNEHVI was selected because of heteroscedastic TPU and print noise. qNEHVI accounts for noisy observations, but the outline does not explain how the surrogate models heteroscedasticity, whether repeated specimens identify noise, or whether noise is being conflated with unmodeled process drift. Independent Gaussian processes with an unspecified observation model do not automatically resolve batch-dependent heteroscedasticity.\\n\\nMy attention drifts during the broad argument against simulation because it delays the information I came to DAC-10 to hear: the design variables, model formulation, constraint handling, acquisition implementation, budget, and benchmark. The Super Ball Bot hook is visually effective, but planetary landing may feel like application theater if the actual specimens and impact regime are far removed from that use case.\\n\\nThe strongest possible section is the results slot. In its current empty state, the talk has a method narrative but no validation.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey used standard batch noisy multi-objective Bayesian optimization to choose which multi-material printed impact specimens to test, instead of running a full physical sweep.\\u201d\\n\\n**Distortion from the intended message:** I would omit the claim that the structures were optimized in dozens of prints because I have not seen evidence for it. I would also describe qNEHVI as standard rather than as the contribution.\\n\\n## (c) Top three Q&A questions\\n\\n1. **What is the actual experimental budget?** How many initial designs, adaptive iterations, specimens per batch, and physical replicates were used, and what stopping rule defined success?\\n2. **What baselines did you beat?** Did you compare qNEHVI with Sobol or Latin-hypercube sampling, random search, scalarized Bayesian optimization, or a domain-informed sequential design under the same number of physical tests?\\n3. **How exactly is noise modeled?** Are replicate-dependent variances supplied to the Gaussian processes, is a heteroscedastic likelihood fitted, and how are TPU lot, print batch, machine drift, and specimen-level defects distinguished?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhat did you contribute beyond applying off-the-shelf qNEHVI to a new specimen family?\\u201d\\n\\n**Damage if unanswered: severe.** For a DAC review audience, this can reduce the work from a design-automation contribution to an application demonstration. A strong answer could rest on the experimental formulation, mixed-variable and failure-aware implementation, quantitative sample-efficiency evidence, and transferable operating lessons. Those contributions must be stated explicitly and tested.\\n\\n---\\n\\n# P2. The aerospace entry, descent, and landing practitioner\\n\\n## (a) First-person reaction\\n\\nThe opening lands immediately. I recognize the Super Ball Bot concept, and \\u201cthe lander is the energy absorber\\u201d is a useful framing. The three performance quantities also sound relevant: energy absorbed per mass, usable stroke, and force transmitted to the payload.\\n\\nThen the talk moves into language I do not naturally use: Gaussian-process surrogate, qNEHVI, acquisition function, hypervolume, exploration versus exploitation, and Pareto front. I can accept that the algorithm chooses the next tests, but I need a plain explanation of what information it uses and why I should trust the recommendations. If the presenter spends several minutes naming algorithms without connecting them to engineering decisions, I will lose the thread.\\n\\nMy main concern is application fidelity. A desktop drop-weight test at one fixed impact energy is not automatically evidence for entry, descent, and landing hardware. I need specimen dimensions, impact mass, velocity, energy, strain rate, boundary conditions, number of impacts, rebound behavior, temperature, vacuum sensitivity, and failure containment. PLA and TPU raise immediate environmental and qualification questions. I also want to know whether the absorber is reusable or sacrificial.\\n\\nMy attention returns when I see force\\u2013time traces, high-speed footage, and damaged specimens. It drifts if the talk stays on the optimizer rather than connecting each output to payload protection.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey have an algorithm that learns from successive drop tests and tells them which 3D-printed crush structure to make next, so they can find a lightweight low-force design with fewer prototypes.\\u201d\\n\\n**Distortion from the intended message:** I would probably call the structures \\u201ccrush structures,\\u201d losing the tensegrity distinction. I might also overgeneralize the method to planetary landers even though the current tests appear to be laboratory-scale.\\n\\n## (c) Top three Q&A questions\\n\\n1. What impact mass, velocity, energy, and strain-rate regime do the tests cover, and how do those values compare with a credible lander load case?\\n2. How do the selected designs scale with size and impact energy? Are the controlling mechanisms material-rate effects, geometric buckling, contact, or all three?\\n3. What happens under off-axis impact, repeated impacts, temperature extremes, aging, vacuum, and manufacturing defects?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhy should I believe that a PLA\\u2013TPU specimen optimized at one laboratory impact condition tells me anything about flight-scale landing protection?\\u201d\\n\\n**Damage if unanswered: severe for the planetary-landing framing, moderate for the underlying optimization study.** The presenter should clearly label planetary landing as motivation, not demonstrated readiness, and state the present technology scope. Otherwise the opening and closing overpromise relative to the reported experiment.\\n\\n---\\n\\n# P3. The additive-manufacturing and materials researcher\\n\\n## (a) First-person reaction\\n\\nThe monolithic PLA\\u2013TPU architecture is the part I care about. The core-wrapping strategy sounds more credible than simply depositing TPU against exposed PLA and assuming durable adhesion. Holding print settings fixed within a batch, weighing specimens, checking dimensions, and periodically testing a control are all sensible.\\n\\nI still need much more fabrication detail before I accept that design geometry is the main source of performance variation. PLA\\u2013TPU interfaces are sensitive to temperature, surface contact, moisture, residence time, toolpath order, contamination, and local geometry. \\u201cPrint defects\\u201d is too broad. I want failure-mode evidence showing whether energy is absorbed through intended tension-network deformation, TPU hysteresis, strut buckling, interface peeling, or accidental damage.\\n\\nThe phrase \\u201cno assembly\\u201d catches my attention, but \\u201celiminates assembly entirely\\u201d may be too strong. Does the build require support removal, trimming, conditioning, fixture insertion, post-tensioning, or manual alignment? If so, the accurate claim is \\u201csingle-build co-fabrication\\u201d or \\u201cno joining of PLA and TPU members,\\u201d not zero assembly.\\n\\nI am a Bayesian-optimization novice. I can follow the loop if the presenter says, \\u201cThe model proposes the next small batch where improvement is plausible or uncertainty is high.\\u201d I will disengage if the slide foregrounds qNEHVI notation rather than the physical decisions it enables.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey co-print rigid PLA and flexible TPU energy absorbers, test them, and use the results to choose the next geometries instead of printing a large design-of-experiments grid.\\u201d\\n\\n**Distortion from the intended message:** I would remember the fabrication loop more strongly than tensegrity or Bayesian optimization. I might interpret the improvement as mostly a material-interface result unless the structural mechanisms are shown.\\n\\n## (c) Top three Q&A questions\\n\\n1. How was PLA\\u2013TPU bonding quantified, and where did failed specimens actually fail: within TPU, within PLA, or at the interface?\\n2. How many nominally identical replicates were printed and tested, and how large were within-batch, between-batch, and printer-drift effects relative to design effects?\\n3. What manual operations occur after printing, and what exactly does \\u201cno assembly\\u201d exclude?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cHow do you know the optimizer is learning architecture rather than uncontrolled interface quality and process drift?\\u201d\\n\\n**Damage if unanswered: severe.** If fabrication variation is not measured and separated from design effects, both the surrogate and the claimed Pareto front may be unstable. Replicates, blocking or covariates, control specimens, dimensional measurements, and failure-mode documentation are needed.\\n\\n---\\n\\n# P4. The first-year graduate student\\n\\n## (a) First-person reaction\\n\\nThe opening video and the phrase \\u201cthe lander is the energy absorber\\u201d give me a concrete picture. I understand the basic problem: too many designs to print, and computer models may miss messy material behavior. I also understand the loop when it is described as \\u201cdesign, print, drop, learn, repeat.\\u201d That story carries me.\\n\\nI start getting lost when several unfamiliar terms arrive together: tensegrity-inspired, compaction efficiency, Gaussian process, surrogate, acquisition function, qNEHVI, heteroscedastic noise, Pareto front, probabilistic feasibility constraint, and exploration versus exploitation. The preview says the audience already believes optimization works, but I still need one intuitive sentence explaining what Bayesian optimization does. Skipping \\u201cBayesian optimization 101\\u201d should not mean skipping the conceptual bridge.\\n\\nI am also unsure how the structure works. Are the TPU parts stretched before impact? Are the PLA struts touching? What makes this \\u201ctensegrity-inspired\\u201d rather than a multi-material lattice? A labeled specimen image and a short deformation sequence would help much more than a verbal definition.\\n\\nMy attention drifts during the repeated case against simulation because I accepted that point the first time. It returns for printing footage, the drop test, and a clear plot with a marked \\u201cbetter\\u201d direction.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey let an artificial-intelligence program learn from 3D-printed drop tests and keep suggesting better shock absorbers, so they do not have to test every design.\\u201d\\n\\n**Distortion from the intended message:** I would probably call Bayesian optimization \\u201cAI,\\u201d omit the two-objective trade-off and force constraint, and assume each new design is simply better than the last. I might also miss what tensegrity contributes.\\n\\n## (c) Top three Q&A questions\\n\\n1. What makes one of these specimens a tensegrity structure rather than an ordinary 3D-printed lattice?\\n2. How does the computer decide which design to print next, in plain language?\\n3. Can one design maximize both energy absorption and compaction efficiency while also lowering peak force, or must those goals trade off?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cI followed the loop, but I still do not understand what is physically special about the structure or what the optimizer actually learned.\\u201d\\n\\n**Damage if unanswered: moderate.** Experts can fill in some gaps, but a talk that leaves novices with only \\u201cAI plus 3D printing\\u201d has failed to communicate its engineering mechanism. One specimen-mechanics visual and one plain-language optimization visual would largely solve this.\\n\\n---\\n\\n# P5. The finite-element-analysis veteran\\n\\n## (a) First-person reaction\\n\\nThe statement \\u201csimulation can\\u2019t be trusted for these structures\\u201d puts me on the defensive before I have seen any evidence. Interfacial slip, viscoelasticity, large deformation, buckling, contact, strain-rate dependence, and manufacturing variability are difficult, but they are not intrinsically beyond finite-element analysis. Models can be calibrated, uncertainty can be propagated, and simulation can still screen designs even if it is imperfect.\\n\\nThe abstract is more defensible than the talk outline. It says the workflow avoids **direct dependence on calibrated finite-element simulation for objective evaluation** and acknowledges future analytical or multifidelity shortcuts. The presentation should use that language. The current outline turns a project-scope choice into a universal technical claim.\\n\\nI accept the economic argument that calibrating a high-fidelity model may not be worthwhile for an early design campaign. That is a stronger position: measured tests provide authoritative objective values, while the study asks whether sequential design can use a small physical budget efficiently. I would also welcome evidence that simulation errors change design rankings or miss observed failure modes. Without such evidence, the repeated attack on simulation sounds like a straw man.\\n\\nMy attention drifts when the talk says simulation is unreliable several times without showing a simulation\\u2013experiment discrepancy. It returns when the presenter discusses physical failure modes and admits where lower-fidelity physics could later improve the loop.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey skipped finite-element modeling and used Bayesian optimization directly on physical tests because they judged model calibration too expensive for these multi-material specimens.\\u201d\\n\\n**Distortion from the intended message:** I would frame the work as bypassing simulation rather than as enabling rapid experimental optimization. If the presenter is combative about simulation, that dispute may become the only thing I remember.\\n\\n## (c) Top three Q&A questions\\n\\n1. What evidence shows that a calibrated finite-element model is insufficient for design ranking, rather than merely costly to construct?\\n2. Did you compare the physical-test-only workflow with a simple mechanics model or multifidelity surrogate that could reduce the experimental budget further?\\n3. Which observed behaviors dominate the model discrepancy: interface failure, TPU viscoelasticity, contact, buckling imperfections, or geometry errors from printing?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cYou have shown that simulation is inconvenient, not that it cannot be trusted. Why is abandoning it scientifically preferable to calibrating and validating it?\\u201d\\n\\n**Damage if unanswered: severe and avoidable.** The current absolute wording invites a technical fight that is unnecessary to the contribution. Reframing the claim as a cost-and-dependence decision would preserve the motivation without asserting that finite-element modeling is incapable.\\n\\n---\\n\\n# P6. The friendly industry generalist\\n\\n## (a) First-person reaction\\n\\nThe loop is memorable: print a candidate, test it, update the model, and print the next informative candidate. I can imagine using that pattern for brackets, seals, lattice pads, or process settings when simulation is weak and tests are expensive. \\u201cDozens rather than thousands\\u201d is exactly the kind of value proposition I remember.\\n\\nI do not need the mathematical details of qNEHVI, but I need an operational recipe. What software is used? How many initial samples are required? How automated is the handoff from optimizer to computer-aided design and slicing? How long does one cycle take? What expertise does a team need? The abstract says candidate selection is automated but slicing, specimen handling, and testing remain manual. That qualification should appear in the talk before I infer a robotic self-driving laboratory.\\n\\nThe tensegrity and planetary material is interesting, but my attention will drift if it occupies too much of a 15-minute slot. The method becomes useful to me only when the presenter explicitly generalizes it and defines when it is worth using.\\n\\nThe ending about moving humankind forward is less memorable than a concrete deployment rule. I would prefer a final slide saying: \\u201cUse this loop when tests are authoritative, each test is costly, the design space is mixed, and several performance goals conflict.\\u201d\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey showed a practical test-driven optimization loop that can find good 3D-printed designs with a few dozen prototypes instead of a giant test matrix.\\u201d\\n\\n**Distortion from the intended message:** I may overstate automation and assume the \\u201cfew dozen\\u201d claim is established across applications. I will probably forget the names qNEHVI and tensegrity, but retain the workflow.\\n\\n## (c) Top three Q&A questions\\n\\n1. What parts of the workflow are automated today, and what does an engineer still do manually between recommendation and test result?\\n2. What minimum experiment budget and software stack would my team need to try this on a different component?\\n3. When is this approach better than a conventional design of experiments, response-surface method, or a calibrated simulation?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhat concrete evidence tells me this saved enough prototypes and engineering time to justify the added optimization infrastructure?\\u201d\\n\\n**Damage if unanswered: moderate to severe.** I will still like the idea, but I will not take it back to my team. A budget-matched baseline and a simple timeline or cost comparison would convert interest into action.\\n\\n---\\n\\n# Synthesis\\n\\n## Cross-persona themes\\n\\n### 1. The causal chain is understandable, but the claimed payoff is not yet demonstrated\\n\\nAll six personas can understand the basic design\\u2013print\\u2013test\\u2013learn loop. The empty results section prevents them from deciding whether the loop actually found better designs, found them faster, or handled noise reliably. The phrase \\u201cdozens, not thousands\\u201d is currently an unsupported quantitative claim.\\n\\nAt minimum, the results need to show:\\n\\n- total number of unique designs and physical specimens;\\n- initial versus adaptively selected designs;\\n- batch size and number of iterations;\\n- replicate policy and failure count;\\n- measured uncertainty or repeatability;\\n- progress under the actual experimental budget;\\n- the final feasible Pareto set for **specific energy absorption and compaction efficiency subject to the force cap**;\\n- a budget-matched nonadaptive or simpler optimization baseline.\\n\\n### 2. \\u201cSimulation can\\u2019t be trusted\\u201d is too absolute\\n\\nP1 questions the evidence, P2 cares about application validity, and P5 directly rejects the premise. Even P6 needs to know when the loop is preferable to simulation. The abstract already contains better wording: physical measurements are used for objective evaluation \\u201cwithout relying on calibrated finite-element simulation.\\u201d\\n\\nThe defensible claim is not that simulation fails universally. It is that a sufficiently calibrated multiphysics model may be expensive relative to the available design campaign, and the authors therefore test whether physical-data-driven sequential optimization is useful without depending on such a model.\\n\\n### 3. The talk needs a two-level explanation of Bayesian optimization\\n\\nP1 does not want introductory Bayesian-optimization material; P2, P3, P4, and P5 need a plain conceptual bridge. These needs are compatible. Give the intuition in one sentence and one visual, then put the implementation specifics in a compact technical panel or backup slide.\\n\\nSuggested spoken explanation:\\n\\n> \\u201cAfter each batch, the model estimates both expected performance and uncertainty across the design space. The acquisition rule selects a small next batch that is most likely to expand the feasible trade-off frontier, while accounting for noisy tests and print failures.\\u201d\\n\\nThis is not \\u201cBayesian optimization 101.\\u201d It is the minimum explanation needed to interpret the workflow.\\n\\n### 4. The physical mechanism and the word \\u201ctensegrity\\u201d need clarification\\n\\nP2 may reduce the objects to crush structures, P3 may interpret the result as an interface study, and P4 may not distinguish the specimens from ordinary lattices. A labeled as-printed specimen plus a four-frame deformation sequence should show:\\n\\n- rigid PLA struts;\\n- continuous flexible TPU network;\\n- whether and how pre-tension exists;\\n- load path before and during impact;\\n- intended energy-dissipation mechanisms;\\n- why \\u201ctensegrity-inspired\\u201d is the precise term.\\n\\n### 5. Manufacturing variability is part of the model, not background noise\\n\\nP1 and P3 will both press this point. Periodically retesting one control is useful but may not identify design-specific heteroscedasticity, lot effects, or confounding between iteration and material batch. The talk needs to state the replication, randomization, blocking, covariate, and drift-monitoring plan. If those steps were not used, the limitation must be explicit.\\n\\n### 6. The application framing outruns the demonstrated scope\\n\\nThe planetary hook is strong, but P2 will not equate fixed-energy laboratory tests on PLA\\u2013TPU specimens with flight hardware. The closing escalates further by claiming that the next lander absorber can be \\u201cproven\\u201d in weeks. That wording should be narrowed unless the campaign includes relevant scaling and environmental evidence.\\n\\n### 7. The objective statement is not fully consistent across the outline\\n\\nThe task and Point 2 correctly describe two objectives, specific energy absorption and compaction efficiency, with a peak-force constraint. The candidate evidence list instead proposes \\u201cSEA vs. peak transmitted force,\\u201d and the abstract\\u2019s expected-outcomes section similarly describes trade-offs between SEA and peak force. Those are different optimization formulations.\\n\\nThe talk must consistently distinguish:\\n\\n- **objectives:** maximize specific energy absorption and compaction efficiency;\\n- **constraint:** peak transmitted force must not exceed a specified cap;\\n- **feasible Pareto front:** trade-offs between the two objectives among designs satisfying that cap.\\n\\nIf peak force is instead treated as a third objective, the task statement and optimization formulation must be revised accordingly.\\n\\n### 8. The closing favors inspiration over technical precision\\n\\nP6 is likely to remember \\u201cdozens, not thousands,\\u201d but P1, P2, and P5 may hear overreach in \\u201cproven,\\u201d \\u201cin weeks,\\u201d and \\u201cmoves humankind forward.\\u201d A DAC-10 close should end on the measured engineering result and transfer condition. The Super Ball Bot bookend can remain, but it should not imply flight qualification.\\n\\n---\\n\\n# Three highest-priority revisions\\n\\n## Priority 1: Build the talk around a quantitative result, not around the availability of a workflow\\n\\nReplace the placeholder with a required three-slide evidence sequence:\\n\\n1. **\\u201cThe campaign used N specimens across B adaptive batches under a fixed physical-test budget.\\u201d** \\n Show initialization, adaptive batches, replicates, failures, elapsed cycle time, and the force threshold. Do not use `N` or `B` in the actual talk; insert the observed values.\\n\\n2. **\\u201cAdaptive selection improved the feasible design set faster than [predeclared baseline].\\u201d** \\n Plot a budget-matched performance measure across physical tests, with uncertainty across repeated runs if available. If only one physical campaign exists, avoid inferential claims that require repeated campaigns and supplement with clearly labeled retrospective resampling or simulation-based algorithm checks.\\n\\n3. **\\u201cThese measured designs form the final feasible trade-off between specific energy absorption and compaction efficiency.\\u201d** \\n Show uncertainty or replicate spread, mark infeasible force-cap violations, and include specimen images or force\\u2013time traces for representative Pareto designs.\\n\\nIf the campaign cannot support those statements, revise the main message from \\u201cwe can optimize \\u2026 in dozens\\u201d to \\u201cwe are evaluating whether noisy multi-objective Bayesian optimization can reduce the physical test budget.\\u201d\\n\\n## Priority 2: Replace the anti-simulation premise with a scoped engineering decision\\n\\nReplace:\\n\\n> \\u201cSimulation can\\u2019t be trusted for these structures.\\u201d\\n\\nwith:\\n\\n> \\u201cFor these multi-material prints, obtaining objective values from a validated high-fidelity model would require substantial calibration of interface, rate, contact, and defect behavior. We therefore ask how far a limited budget of direct physical tests can take us without depending on that model.\\u201d\\n\\nThen title Point 1 with a message rather than a topic:\\n\\n> **\\u201cWhen model calibration and exhaustive testing are both costly, each physical specimen must be chosen for information value.\\u201d**\\n\\nThis keeps the pincer structure while avoiding an unnecessary universal claim.\\n\\n## Priority 3: Reallocate the 15 minutes around audience decisions\\n\\nA concrete timing plan:\\n\\n- **1.5 min:** Super Ball Bot hook, specimen, and present study scope.\\n- **2 min:** Why exhaustive physical search and high-fidelity calibration are costly.\\n- **2.5 min:** One visual of the closed loop, including a one-sentence Bayesian-optimization explanation.\\n- **2 min:** Actual design variables, objectives, force constraint, fabrication failures, and noise controls.\\n- **5 min:** measured results, baseline, uncertainty, representative specimens, and failure modes.\\n- **1 min:** limitations and transfer conditions.\\n- **1 min:** measured conclusion and bookend.\\n\\nMove kernel choices, log-space acquisitions, detailed encoding, and other implementation material to backup slides unless a result depends directly on them. This follows Doumont\\u2019s principle that each slide should communicate one interpreted message rather than expose the speaker\\u2019s notes.\\n\\n---\\n\\n# Claims needing evidence, hedging, or backup slides\\n\\n| Claim or topic | What is needed in the main talk | Prepared backup material |\\n|---|---|---|\\n| \\u201cDozens of prints, not thousands\\u201d | Actual specimen count, definition of success, and budget-matched comparator | Full campaign ledger; sensitivity to initialization and stopping rule |\\n| \\u201cSimulation can\\u2019t be trusted\\u201d | Replace with scoped calibration-cost language unless direct validation evidence exists | Simulation\\u2013experiment traces, ranking errors, or documented failure modes a tested model missed |\\n| \\u201cqNEHVI handles heteroscedastic noise\\u201d | Specify the actual observation-noise model and replication strategy | Likelihood formulation, fixed versus inferred noise, residual diagnostics, replicate variance by design or batch |\\n| qNEHVI \\u201cover standard expected improvement\\u201d | Use a technically appropriate comparison; ordinary expected improvement is not a like-for-like multi-objective batch baseline | Comparisons with random/Sobol, scalarization, qEHVI or another relevant noisy/constrained baseline |\\n| \\u201cThe Pareto front is the design deliverable\\u201d | Show the two stated objectives and force-cap feasibility consistently | Hypervolume reference point, normalization, uncertainty, constraint treatment |\\n| Peak-force cap | State its numerical value and engineering rationale | Sensitivity of selected designs to alternative cap values |\\n| Probabilistic print-feasibility constraint | Report failures and demonstrate that the classifier or constraint model is identifiable at the available sample size | Failure labels, calibration, confusion or reliability metrics, acquisition formula |\\n| \\u201cNo assembly\\u201d / \\u201celiminates assembly entirely\\u201d | List any post-print operations; use \\u201csingle-build co-fabrication\\u201d if manual steps remain | Fabrication workflow photographs and labor-time accounting |\\n| \\u201cCandidate goes to tested specimen in hours\\u201d | Median or representative print-to-result time and what remains manual | Time breakdown for design, slicing, printing, conditioning, inspection, and testing |\\n| \\u201cDramatically faster\\u201d | Define faster relative to a measured or estimated comparator | Cost and wall-clock accounting with assumptions |\\n| Transfer to other architectures | Present as a conditional hypothesis, not a demonstrated result | Applicability checklist: test cost, dimensionality, noise, throughput, and constraint structure |\\n| Planetary-landing relevance | Clearly state that this is motivation unless scale and environment are tested | Impact similitude, energy and velocity regime, environmental gaps, technology-readiness discussion |\\n| PLA\\u2013TPU interface robustness | Failure-mode and repeatability evidence | Microscopy or fracture images, process parameters, conditioning and moisture controls |\\n| \\u201cTensegrity-inspired\\u201d | Show the load path and identify which tensegrity features are retained | Geometric definition, pre-tension status, comparison with a conventional lattice/control |\\n| Independent Gaussian processes | State why cross-objective correlation is ignored and whether that choice matters | Residual correlations and sensitivity to multi-output alternatives |\\n| Mixed categorical variables | Briefly state how candidates are represented and optimized | Kernel or encoding details; treatment of invalid combinations |\\n\\n---\\n\\n# Fit to the six-person audience\\n\\n## Best served: P6, the friendly industry generalist\\n\\nThe outline has a clear problem\\u2013method\\u2013application arc, a memorable loop, and an attractive efficiency promise. P6 can understand the proposed value without needing the mathematics. The danger is that this persona may accept unsupported claims and overestimate the system\\u2019s automation and maturity.\\n\\nP4 also benefits from the story structure, but the unexplained terminology and absent physical-mechanism visual will cause substantial loss of detail.\\n\\n## Worst served: P1, the skeptical Bayesian-optimization insider\\n\\nP1 is the most likely to judge the current talk as an off-the-shelf acquisition function applied to a new artifact. The outline anticipates this concern by saying not to teach Bayesian optimization, but it does not yet supply the evidence P1 needs: novelty positioning, rigorous baseline, budget, noise identification, constraint diagnostics, and measured sample efficiency.\\n\\nP5 is nearly as poorly served because the opening motivation directly challenges finite-element modeling more strongly than the abstract supports.\\n\\n## Is that the right trade-off for DAC-10?\\n\\nNo. A DAC-10 presentation should remain accessible to P2\\u2013P6, but it cannot underserve P1. The design-automation expert is likely to shape the technical discussion and the perceived contribution. Accessibility should come from a plain-language loop and strong visuals, not from omitting methodological validation. The best target is a two-layer talk: every listener can follow the engineering decision, while experts can see the experimental budget, comparator, noise model, and constraint formulation.\\n\\n---\\n\\n# Predicted reception\\n\\n**Current outline: 5/10.** The loop and application are memorable, but without measured results, a budget-matched baseline, and narrower simulation and planetary claims, the talk reads as a polished motivation for an experiment rather than a completed DAC-10 contribution.\\n\\nIf the results establish repeatability and budgeted improvement, and the three priority revisions are made, the same structure could plausibly move into the **7\\u20138/10** range. That conditional estimate is a judgment about presentation readiness, not a prediction of unobserved experimental outcomes.\\n\\n---\\n\\n# Discretionary analytical decisions\\n\\n- Treated the unfinished evidence/results section as missing evidence rather than assuming that the planned campaign succeeds.\\n- Evaluated the artifact as a competent 15-minute delivery from the outline, not as a line edit of the outline or an assessment of slides that do not yet exist.\\n- Weighted technical credibility and evidentiary support more heavily than visual polish because the target is the DAC-10 design-optimization audience.\\n- Used the submitted abstract to resolve technical intent when the outline was ambiguous, while flagging inconsistencies rather than silently harmonizing them.\\n- Interpreted the stated optimization problem as two objectives, specific energy absorption and compaction efficiency, subject to a peak-force constraint; flagged language that instead treats peak force as an objective.\\n- Assigned qualitative damage levels of moderate, moderate-to-severe, or severe based on how directly an unanswered challenge would undermine each persona\\u2019s acceptance of the central claim.\\n- Chose P6 as the best-served persona because the present outline emphasizes an intuitive workflow and practical efficiency promise; chose P1 as the worst-served because the DAC-specific validation and novelty case is not yet developed.\\n- Scored predicted reception on the current incomplete outline, not on a hypothetical final talk with successful results.\"}", + "name": "submit_answer" + } + } + ] + }, + "logprob": null + } + } + }, + "created_at": "2026-07-18T17:57:22.818178Z" + }, + { + "id": "f3d08799-1119-43e2-9fe0-e504541b682e", + "trajectory_id": "4949af58-f08a-4541-ad1c-1f0a95c6f07d", + "trajectory_timestep": 0, + "step": "after_agent_get_asv", + "state": { + "transition": { + "timestep": 0, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. 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Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "info": null + }, + { + "role": "assistant", + "content": "I\u2019ll evaluate the outline as a delivered 15-minute talk, treating the unfinished results section as a substantive gap rather than inventing outcomes. I\u2019ll score each persona against comprehension, credibility, relevance, and recall, then synthesize revisions for DAC-10.", + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "info": null + }, + { + "role": "assistant", + "content": "I\u2019ll evaluate the outline as a delivered 15-minute talk, treating the unfinished results section as a substantive gap rather than inventing outcomes. I\u2019ll score each persona against comprehension, credibility, relevance, and recall, then synthesize revisions for DAC-10.", + "info": null + }, + { + "role": "user", + "content": "\nMessage was NOT sent to user. Please call the send_message tool to communicate with the user. Use wait=True if you need a response before proceeding, or wait=False if you are providing a status update to the user.", + "info": { + "is_human": false + } + }, + { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_zSHcqZDq4Rrb984ui6lpQugL", + "type": "function", + "function": { + "arguments": "{\"answer\": \"# Mock audience report: IDETC-CIE 2026 DAC-10 talk\\n\\n## Scope of this review\\n\\nThis evaluates the talk implied by the outline, not finished slides or delivery technique. The experimental campaign is still in progress, so I do **not** infer any favorable results. The empty \\u201cEvidence / results\\u201d section is treated as a major unresolved part of the talk: the outline currently explains why the workflow should be useful, but does not yet demonstrate that it is useful.\\n\\nThe central communication test is whether each listener leaves with the intended message:\\n\\n> By closing the loop between multi-material 3D printing and Bayesian optimization, we can optimize tensegrity energy absorbers directly from real impact data, in dozens of prints rather than thousands.\\n\\n---\\n\\n# P1. The skeptical Bayesian-optimization insider\\n\\n## (a) First-person reaction\\n\\nThe application is potentially interesting, and the design\\u2013print\\u2013test loop is easy to understand. The mixed continuous, integer, and categorical design space, physical failures, parallel fabrication, and multiple constrained objectives could make this a worthwhile design-automation case study.\\n\\nBut the outline keeps telling me that Bayesian optimization is sample-efficient without showing that **this implementation** is sample-efficient. \\u201cDozens, not thousands\\u201d sounds like a slogan unless I see the actual experiment count, initialization size, batch size, replication policy, convergence behavior, and a comparison against credible alternatives. q-noisy expected hypervolume improvement, or qNEHVI, is established machinery. Merely deploying it does not establish methodological novelty.\\n\\nI become skeptical at the statement that qNEHVI was selected because of heteroscedastic TPU and print noise. qNEHVI accounts for noisy observations, but the outline does not explain how the surrogate models heteroscedasticity, whether repeated specimens identify noise, or whether noise is being conflated with unmodeled process drift. Independent Gaussian processes with an unspecified observation model do not automatically resolve batch-dependent heteroscedasticity.\\n\\nMy attention drifts during the broad argument against simulation because it delays the information I came to DAC-10 to hear: the design variables, model formulation, constraint handling, acquisition implementation, budget, and benchmark. The Super Ball Bot hook is visually effective, but planetary landing may feel like application theater if the actual specimens and impact regime are far removed from that use case.\\n\\nThe strongest possible section is the results slot. In its current empty state, the talk has a method narrative but no validation.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey used standard batch noisy multi-objective Bayesian optimization to choose which multi-material printed impact specimens to test, instead of running a full physical sweep.\\u201d\\n\\n**Distortion from the intended message:** I would omit the claim that the structures were optimized in dozens of prints because I have not seen evidence for it. I would also describe qNEHVI as standard rather than as the contribution.\\n\\n## (c) Top three Q&A questions\\n\\n1. **What is the actual experimental budget?** How many initial designs, adaptive iterations, specimens per batch, and physical replicates were used, and what stopping rule defined success?\\n2. **What baselines did you beat?** Did you compare qNEHVI with Sobol or Latin-hypercube sampling, random search, scalarized Bayesian optimization, or a domain-informed sequential design under the same number of physical tests?\\n3. **How exactly is noise modeled?** Are replicate-dependent variances supplied to the Gaussian processes, is a heteroscedastic likelihood fitted, and how are TPU lot, print batch, machine drift, and specimen-level defects distinguished?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhat did you contribute beyond applying off-the-shelf qNEHVI to a new specimen family?\\u201d\\n\\n**Damage if unanswered: severe.** For a DAC review audience, this can reduce the work from a design-automation contribution to an application demonstration. A strong answer could rest on the experimental formulation, mixed-variable and failure-aware implementation, quantitative sample-efficiency evidence, and transferable operating lessons. Those contributions must be stated explicitly and tested.\\n\\n---\\n\\n# P2. The aerospace entry, descent, and landing practitioner\\n\\n## (a) First-person reaction\\n\\nThe opening lands immediately. I recognize the Super Ball Bot concept, and \\u201cthe lander is the energy absorber\\u201d is a useful framing. The three performance quantities also sound relevant: energy absorbed per mass, usable stroke, and force transmitted to the payload.\\n\\nThen the talk moves into language I do not naturally use: Gaussian-process surrogate, qNEHVI, acquisition function, hypervolume, exploration versus exploitation, and Pareto front. I can accept that the algorithm chooses the next tests, but I need a plain explanation of what information it uses and why I should trust the recommendations. If the presenter spends several minutes naming algorithms without connecting them to engineering decisions, I will lose the thread.\\n\\nMy main concern is application fidelity. A desktop drop-weight test at one fixed impact energy is not automatically evidence for entry, descent, and landing hardware. I need specimen dimensions, impact mass, velocity, energy, strain rate, boundary conditions, number of impacts, rebound behavior, temperature, vacuum sensitivity, and failure containment. PLA and TPU raise immediate environmental and qualification questions. I also want to know whether the absorber is reusable or sacrificial.\\n\\nMy attention returns when I see force\\u2013time traces, high-speed footage, and damaged specimens. It drifts if the talk stays on the optimizer rather than connecting each output to payload protection.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey have an algorithm that learns from successive drop tests and tells them which 3D-printed crush structure to make next, so they can find a lightweight low-force design with fewer prototypes.\\u201d\\n\\n**Distortion from the intended message:** I would probably call the structures \\u201ccrush structures,\\u201d losing the tensegrity distinction. I might also overgeneralize the method to planetary landers even though the current tests appear to be laboratory-scale.\\n\\n## (c) Top three Q&A questions\\n\\n1. What impact mass, velocity, energy, and strain-rate regime do the tests cover, and how do those values compare with a credible lander load case?\\n2. How do the selected designs scale with size and impact energy? Are the controlling mechanisms material-rate effects, geometric buckling, contact, or all three?\\n3. What happens under off-axis impact, repeated impacts, temperature extremes, aging, vacuum, and manufacturing defects?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhy should I believe that a PLA\\u2013TPU specimen optimized at one laboratory impact condition tells me anything about flight-scale landing protection?\\u201d\\n\\n**Damage if unanswered: severe for the planetary-landing framing, moderate for the underlying optimization study.** The presenter should clearly label planetary landing as motivation, not demonstrated readiness, and state the present technology scope. Otherwise the opening and closing overpromise relative to the reported experiment.\\n\\n---\\n\\n# P3. The additive-manufacturing and materials researcher\\n\\n## (a) First-person reaction\\n\\nThe monolithic PLA\\u2013TPU architecture is the part I care about. The core-wrapping strategy sounds more credible than simply depositing TPU against exposed PLA and assuming durable adhesion. Holding print settings fixed within a batch, weighing specimens, checking dimensions, and periodically testing a control are all sensible.\\n\\nI still need much more fabrication detail before I accept that design geometry is the main source of performance variation. PLA\\u2013TPU interfaces are sensitive to temperature, surface contact, moisture, residence time, toolpath order, contamination, and local geometry. \\u201cPrint defects\\u201d is too broad. I want failure-mode evidence showing whether energy is absorbed through intended tension-network deformation, TPU hysteresis, strut buckling, interface peeling, or accidental damage.\\n\\nThe phrase \\u201cno assembly\\u201d catches my attention, but \\u201celiminates assembly entirely\\u201d may be too strong. Does the build require support removal, trimming, conditioning, fixture insertion, post-tensioning, or manual alignment? If so, the accurate claim is \\u201csingle-build co-fabrication\\u201d or \\u201cno joining of PLA and TPU members,\\u201d not zero assembly.\\n\\nI am a Bayesian-optimization novice. I can follow the loop if the presenter says, \\u201cThe model proposes the next small batch where improvement is plausible or uncertainty is high.\\u201d I will disengage if the slide foregrounds qNEHVI notation rather than the physical decisions it enables.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey co-print rigid PLA and flexible TPU energy absorbers, test them, and use the results to choose the next geometries instead of printing a large design-of-experiments grid.\\u201d\\n\\n**Distortion from the intended message:** I would remember the fabrication loop more strongly than tensegrity or Bayesian optimization. I might interpret the improvement as mostly a material-interface result unless the structural mechanisms are shown.\\n\\n## (c) Top three Q&A questions\\n\\n1. How was PLA\\u2013TPU bonding quantified, and where did failed specimens actually fail: within TPU, within PLA, or at the interface?\\n2. How many nominally identical replicates were printed and tested, and how large were within-batch, between-batch, and printer-drift effects relative to design effects?\\n3. What manual operations occur after printing, and what exactly does \\u201cno assembly\\u201d exclude?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cHow do you know the optimizer is learning architecture rather than uncontrolled interface quality and process drift?\\u201d\\n\\n**Damage if unanswered: severe.** If fabrication variation is not measured and separated from design effects, both the surrogate and the claimed Pareto front may be unstable. Replicates, blocking or covariates, control specimens, dimensional measurements, and failure-mode documentation are needed.\\n\\n---\\n\\n# P4. The first-year graduate student\\n\\n## (a) First-person reaction\\n\\nThe opening video and the phrase \\u201cthe lander is the energy absorber\\u201d give me a concrete picture. I understand the basic problem: too many designs to print, and computer models may miss messy material behavior. I also understand the loop when it is described as \\u201cdesign, print, drop, learn, repeat.\\u201d That story carries me.\\n\\nI start getting lost when several unfamiliar terms arrive together: tensegrity-inspired, compaction efficiency, Gaussian process, surrogate, acquisition function, qNEHVI, heteroscedastic noise, Pareto front, probabilistic feasibility constraint, and exploration versus exploitation. The preview says the audience already believes optimization works, but I still need one intuitive sentence explaining what Bayesian optimization does. Skipping \\u201cBayesian optimization 101\\u201d should not mean skipping the conceptual bridge.\\n\\nI am also unsure how the structure works. Are the TPU parts stretched before impact? Are the PLA struts touching? What makes this \\u201ctensegrity-inspired\\u201d rather than a multi-material lattice? A labeled specimen image and a short deformation sequence would help much more than a verbal definition.\\n\\nMy attention drifts during the repeated case against simulation because I accepted that point the first time. It returns for printing footage, the drop test, and a clear plot with a marked \\u201cbetter\\u201d direction.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey let an artificial-intelligence program learn from 3D-printed drop tests and keep suggesting better shock absorbers, so they do not have to test every design.\\u201d\\n\\n**Distortion from the intended message:** I would probably call Bayesian optimization \\u201cAI,\\u201d omit the two-objective trade-off and force constraint, and assume each new design is simply better than the last. I might also miss what tensegrity contributes.\\n\\n## (c) Top three Q&A questions\\n\\n1. What makes one of these specimens a tensegrity structure rather than an ordinary 3D-printed lattice?\\n2. How does the computer decide which design to print next, in plain language?\\n3. Can one design maximize both energy absorption and compaction efficiency while also lowering peak force, or must those goals trade off?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cI followed the loop, but I still do not understand what is physically special about the structure or what the optimizer actually learned.\\u201d\\n\\n**Damage if unanswered: moderate.** Experts can fill in some gaps, but a talk that leaves novices with only \\u201cAI plus 3D printing\\u201d has failed to communicate its engineering mechanism. One specimen-mechanics visual and one plain-language optimization visual would largely solve this.\\n\\n---\\n\\n# P5. The finite-element-analysis veteran\\n\\n## (a) First-person reaction\\n\\nThe statement \\u201csimulation can\\u2019t be trusted for these structures\\u201d puts me on the defensive before I have seen any evidence. Interfacial slip, viscoelasticity, large deformation, buckling, contact, strain-rate dependence, and manufacturing variability are difficult, but they are not intrinsically beyond finite-element analysis. Models can be calibrated, uncertainty can be propagated, and simulation can still screen designs even if it is imperfect.\\n\\nThe abstract is more defensible than the talk outline. It says the workflow avoids **direct dependence on calibrated finite-element simulation for objective evaluation** and acknowledges future analytical or multifidelity shortcuts. The presentation should use that language. The current outline turns a project-scope choice into a universal technical claim.\\n\\nI accept the economic argument that calibrating a high-fidelity model may not be worthwhile for an early design campaign. That is a stronger position: measured tests provide authoritative objective values, while the study asks whether sequential design can use a small physical budget efficiently. I would also welcome evidence that simulation errors change design rankings or miss observed failure modes. Without such evidence, the repeated attack on simulation sounds like a straw man.\\n\\nMy attention drifts when the talk says simulation is unreliable several times without showing a simulation\\u2013experiment discrepancy. It returns when the presenter discusses physical failure modes and admits where lower-fidelity physics could later improve the loop.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey skipped finite-element modeling and used Bayesian optimization directly on physical tests because they judged model calibration too expensive for these multi-material specimens.\\u201d\\n\\n**Distortion from the intended message:** I would frame the work as bypassing simulation rather than as enabling rapid experimental optimization. If the presenter is combative about simulation, that dispute may become the only thing I remember.\\n\\n## (c) Top three Q&A questions\\n\\n1. What evidence shows that a calibrated finite-element model is insufficient for design ranking, rather than merely costly to construct?\\n2. Did you compare the physical-test-only workflow with a simple mechanics model or multifidelity surrogate that could reduce the experimental budget further?\\n3. Which observed behaviors dominate the model discrepancy: interface failure, TPU viscoelasticity, contact, buckling imperfections, or geometry errors from printing?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cYou have shown that simulation is inconvenient, not that it cannot be trusted. Why is abandoning it scientifically preferable to calibrating and validating it?\\u201d\\n\\n**Damage if unanswered: severe and avoidable.** The current absolute wording invites a technical fight that is unnecessary to the contribution. Reframing the claim as a cost-and-dependence decision would preserve the motivation without asserting that finite-element modeling is incapable.\\n\\n---\\n\\n# P6. The friendly industry generalist\\n\\n## (a) First-person reaction\\n\\nThe loop is memorable: print a candidate, test it, update the model, and print the next informative candidate. I can imagine using that pattern for brackets, seals, lattice pads, or process settings when simulation is weak and tests are expensive. \\u201cDozens rather than thousands\\u201d is exactly the kind of value proposition I remember.\\n\\nI do not need the mathematical details of qNEHVI, but I need an operational recipe. What software is used? How many initial samples are required? How automated is the handoff from optimizer to computer-aided design and slicing? How long does one cycle take? What expertise does a team need? The abstract says candidate selection is automated but slicing, specimen handling, and testing remain manual. That qualification should appear in the talk before I infer a robotic self-driving laboratory.\\n\\nThe tensegrity and planetary material is interesting, but my attention will drift if it occupies too much of a 15-minute slot. The method becomes useful to me only when the presenter explicitly generalizes it and defines when it is worth using.\\n\\nThe ending about moving humankind forward is less memorable than a concrete deployment rule. I would prefer a final slide saying: \\u201cUse this loop when tests are authoritative, each test is costly, the design space is mixed, and several performance goals conflict.\\u201d\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey showed a practical test-driven optimization loop that can find good 3D-printed designs with a few dozen prototypes instead of a giant test matrix.\\u201d\\n\\n**Distortion from the intended message:** I may overstate automation and assume the \\u201cfew dozen\\u201d claim is established across applications. I will probably forget the names qNEHVI and tensegrity, but retain the workflow.\\n\\n## (c) Top three Q&A questions\\n\\n1. What parts of the workflow are automated today, and what does an engineer still do manually between recommendation and test result?\\n2. What minimum experiment budget and software stack would my team need to try this on a different component?\\n3. When is this approach better than a conventional design of experiments, response-surface method, or a calibrated simulation?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhat concrete evidence tells me this saved enough prototypes and engineering time to justify the added optimization infrastructure?\\u201d\\n\\n**Damage if unanswered: moderate to severe.** I will still like the idea, but I will not take it back to my team. A budget-matched baseline and a simple timeline or cost comparison would convert interest into action.\\n\\n---\\n\\n# Synthesis\\n\\n## Cross-persona themes\\n\\n### 1. The causal chain is understandable, but the claimed payoff is not yet demonstrated\\n\\nAll six personas can understand the basic design\\u2013print\\u2013test\\u2013learn loop. The empty results section prevents them from deciding whether the loop actually found better designs, found them faster, or handled noise reliably. The phrase \\u201cdozens, not thousands\\u201d is currently an unsupported quantitative claim.\\n\\nAt minimum, the results need to show:\\n\\n- total number of unique designs and physical specimens;\\n- initial versus adaptively selected designs;\\n- batch size and number of iterations;\\n- replicate policy and failure count;\\n- measured uncertainty or repeatability;\\n- progress under the actual experimental budget;\\n- the final feasible Pareto set for **specific energy absorption and compaction efficiency subject to the force cap**;\\n- a budget-matched nonadaptive or simpler optimization baseline.\\n\\n### 2. \\u201cSimulation can\\u2019t be trusted\\u201d is too absolute\\n\\nP1 questions the evidence, P2 cares about application validity, and P5 directly rejects the premise. Even P6 needs to know when the loop is preferable to simulation. The abstract already contains better wording: physical measurements are used for objective evaluation \\u201cwithout relying on calibrated finite-element simulation.\\u201d\\n\\nThe defensible claim is not that simulation fails universally. It is that a sufficiently calibrated multiphysics model may be expensive relative to the available design campaign, and the authors therefore test whether physical-data-driven sequential optimization is useful without depending on such a model.\\n\\n### 3. The talk needs a two-level explanation of Bayesian optimization\\n\\nP1 does not want introductory Bayesian-optimization material; P2, P3, P4, and P5 need a plain conceptual bridge. These needs are compatible. Give the intuition in one sentence and one visual, then put the implementation specifics in a compact technical panel or backup slide.\\n\\nSuggested spoken explanation:\\n\\n> \\u201cAfter each batch, the model estimates both expected performance and uncertainty across the design space. The acquisition rule selects a small next batch that is most likely to expand the feasible trade-off frontier, while accounting for noisy tests and print failures.\\u201d\\n\\nThis is not \\u201cBayesian optimization 101.\\u201d It is the minimum explanation needed to interpret the workflow.\\n\\n### 4. The physical mechanism and the word \\u201ctensegrity\\u201d need clarification\\n\\nP2 may reduce the objects to crush structures, P3 may interpret the result as an interface study, and P4 may not distinguish the specimens from ordinary lattices. A labeled as-printed specimen plus a four-frame deformation sequence should show:\\n\\n- rigid PLA struts;\\n- continuous flexible TPU network;\\n- whether and how pre-tension exists;\\n- load path before and during impact;\\n- intended energy-dissipation mechanisms;\\n- why \\u201ctensegrity-inspired\\u201d is the precise term.\\n\\n### 5. Manufacturing variability is part of the model, not background noise\\n\\nP1 and P3 will both press this point. Periodically retesting one control is useful but may not identify design-specific heteroscedasticity, lot effects, or confounding between iteration and material batch. The talk needs to state the replication, randomization, blocking, covariate, and drift-monitoring plan. If those steps were not used, the limitation must be explicit.\\n\\n### 6. The application framing outruns the demonstrated scope\\n\\nThe planetary hook is strong, but P2 will not equate fixed-energy laboratory tests on PLA\\u2013TPU specimens with flight hardware. The closing escalates further by claiming that the next lander absorber can be \\u201cproven\\u201d in weeks. That wording should be narrowed unless the campaign includes relevant scaling and environmental evidence.\\n\\n### 7. The objective statement is not fully consistent across the outline\\n\\nThe task and Point 2 correctly describe two objectives, specific energy absorption and compaction efficiency, with a peak-force constraint. The candidate evidence list instead proposes \\u201cSEA vs. peak transmitted force,\\u201d and the abstract\\u2019s expected-outcomes section similarly describes trade-offs between SEA and peak force. Those are different optimization formulations.\\n\\nThe talk must consistently distinguish:\\n\\n- **objectives:** maximize specific energy absorption and compaction efficiency;\\n- **constraint:** peak transmitted force must not exceed a specified cap;\\n- **feasible Pareto front:** trade-offs between the two objectives among designs satisfying that cap.\\n\\nIf peak force is instead treated as a third objective, the task statement and optimization formulation must be revised accordingly.\\n\\n### 8. The closing favors inspiration over technical precision\\n\\nP6 is likely to remember \\u201cdozens, not thousands,\\u201d but P1, P2, and P5 may hear overreach in \\u201cproven,\\u201d \\u201cin weeks,\\u201d and \\u201cmoves humankind forward.\\u201d A DAC-10 close should end on the measured engineering result and transfer condition. The Super Ball Bot bookend can remain, but it should not imply flight qualification.\\n\\n---\\n\\n# Three highest-priority revisions\\n\\n## Priority 1: Build the talk around a quantitative result, not around the availability of a workflow\\n\\nReplace the placeholder with a required three-slide evidence sequence:\\n\\n1. **\\u201cThe campaign used N specimens across B adaptive batches under a fixed physical-test budget.\\u201d** \\n Show initialization, adaptive batches, replicates, failures, elapsed cycle time, and the force threshold. Do not use `N` or `B` in the actual talk; insert the observed values.\\n\\n2. **\\u201cAdaptive selection improved the feasible design set faster than [predeclared baseline].\\u201d** \\n Plot a budget-matched performance measure across physical tests, with uncertainty across repeated runs if available. If only one physical campaign exists, avoid inferential claims that require repeated campaigns and supplement with clearly labeled retrospective resampling or simulation-based algorithm checks.\\n\\n3. **\\u201cThese measured designs form the final feasible trade-off between specific energy absorption and compaction efficiency.\\u201d** \\n Show uncertainty or replicate spread, mark infeasible force-cap violations, and include specimen images or force\\u2013time traces for representative Pareto designs.\\n\\nIf the campaign cannot support those statements, revise the main message from \\u201cwe can optimize \\u2026 in dozens\\u201d to \\u201cwe are evaluating whether noisy multi-objective Bayesian optimization can reduce the physical test budget.\\u201d\\n\\n## Priority 2: Replace the anti-simulation premise with a scoped engineering decision\\n\\nReplace:\\n\\n> \\u201cSimulation can\\u2019t be trusted for these structures.\\u201d\\n\\nwith:\\n\\n> \\u201cFor these multi-material prints, obtaining objective values from a validated high-fidelity model would require substantial calibration of interface, rate, contact, and defect behavior. We therefore ask how far a limited budget of direct physical tests can take us without depending on that model.\\u201d\\n\\nThen title Point 1 with a message rather than a topic:\\n\\n> **\\u201cWhen model calibration and exhaustive testing are both costly, each physical specimen must be chosen for information value.\\u201d**\\n\\nThis keeps the pincer structure while avoiding an unnecessary universal claim.\\n\\n## Priority 3: Reallocate the 15 minutes around audience decisions\\n\\nA concrete timing plan:\\n\\n- **1.5 min:** Super Ball Bot hook, specimen, and present study scope.\\n- **2 min:** Why exhaustive physical search and high-fidelity calibration are costly.\\n- **2.5 min:** One visual of the closed loop, including a one-sentence Bayesian-optimization explanation.\\n- **2 min:** Actual design variables, objectives, force constraint, fabrication failures, and noise controls.\\n- **5 min:** measured results, baseline, uncertainty, representative specimens, and failure modes.\\n- **1 min:** limitations and transfer conditions.\\n- **1 min:** measured conclusion and bookend.\\n\\nMove kernel choices, log-space acquisitions, detailed encoding, and other implementation material to backup slides unless a result depends directly on them. This follows Doumont\\u2019s principle that each slide should communicate one interpreted message rather than expose the speaker\\u2019s notes.\\n\\n---\\n\\n# Claims needing evidence, hedging, or backup slides\\n\\n| Claim or topic | What is needed in the main talk | Prepared backup material |\\n|---|---|---|\\n| \\u201cDozens of prints, not thousands\\u201d | Actual specimen count, definition of success, and budget-matched comparator | Full campaign ledger; sensitivity to initialization and stopping rule |\\n| \\u201cSimulation can\\u2019t be trusted\\u201d | Replace with scoped calibration-cost language unless direct validation evidence exists | Simulation\\u2013experiment traces, ranking errors, or documented failure modes a tested model missed |\\n| \\u201cqNEHVI handles heteroscedastic noise\\u201d | Specify the actual observation-noise model and replication strategy | Likelihood formulation, fixed versus inferred noise, residual diagnostics, replicate variance by design or batch |\\n| qNEHVI \\u201cover standard expected improvement\\u201d | Use a technically appropriate comparison; ordinary expected improvement is not a like-for-like multi-objective batch baseline | Comparisons with random/Sobol, scalarization, qEHVI or another relevant noisy/constrained baseline |\\n| \\u201cThe Pareto front is the design deliverable\\u201d | Show the two stated objectives and force-cap feasibility consistently | Hypervolume reference point, normalization, uncertainty, constraint treatment |\\n| Peak-force cap | State its numerical value and engineering rationale | Sensitivity of selected designs to alternative cap values |\\n| Probabilistic print-feasibility constraint | Report failures and demonstrate that the classifier or constraint model is identifiable at the available sample size | Failure labels, calibration, confusion or reliability metrics, acquisition formula |\\n| \\u201cNo assembly\\u201d / \\u201celiminates assembly entirely\\u201d | List any post-print operations; use \\u201csingle-build co-fabrication\\u201d if manual steps remain | Fabrication workflow photographs and labor-time accounting |\\n| \\u201cCandidate goes to tested specimen in hours\\u201d | Median or representative print-to-result time and what remains manual | Time breakdown for design, slicing, printing, conditioning, inspection, and testing |\\n| \\u201cDramatically faster\\u201d | Define faster relative to a measured or estimated comparator | Cost and wall-clock accounting with assumptions |\\n| Transfer to other architectures | Present as a conditional hypothesis, not a demonstrated result | Applicability checklist: test cost, dimensionality, noise, throughput, and constraint structure |\\n| Planetary-landing relevance | Clearly state that this is motivation unless scale and environment are tested | Impact similitude, energy and velocity regime, environmental gaps, technology-readiness discussion |\\n| PLA\\u2013TPU interface robustness | Failure-mode and repeatability evidence | Microscopy or fracture images, process parameters, conditioning and moisture controls |\\n| \\u201cTensegrity-inspired\\u201d | Show the load path and identify which tensegrity features are retained | Geometric definition, pre-tension status, comparison with a conventional lattice/control |\\n| Independent Gaussian processes | State why cross-objective correlation is ignored and whether that choice matters | Residual correlations and sensitivity to multi-output alternatives |\\n| Mixed categorical variables | Briefly state how candidates are represented and optimized | Kernel or encoding details; treatment of invalid combinations |\\n\\n---\\n\\n# Fit to the six-person audience\\n\\n## Best served: P6, the friendly industry generalist\\n\\nThe outline has a clear problem\\u2013method\\u2013application arc, a memorable loop, and an attractive efficiency promise. P6 can understand the proposed value without needing the mathematics. The danger is that this persona may accept unsupported claims and overestimate the system\\u2019s automation and maturity.\\n\\nP4 also benefits from the story structure, but the unexplained terminology and absent physical-mechanism visual will cause substantial loss of detail.\\n\\n## Worst served: P1, the skeptical Bayesian-optimization insider\\n\\nP1 is the most likely to judge the current talk as an off-the-shelf acquisition function applied to a new artifact. The outline anticipates this concern by saying not to teach Bayesian optimization, but it does not yet supply the evidence P1 needs: novelty positioning, rigorous baseline, budget, noise identification, constraint diagnostics, and measured sample efficiency.\\n\\nP5 is nearly as poorly served because the opening motivation directly challenges finite-element modeling more strongly than the abstract supports.\\n\\n## Is that the right trade-off for DAC-10?\\n\\nNo. A DAC-10 presentation should remain accessible to P2\\u2013P6, but it cannot underserve P1. The design-automation expert is likely to shape the technical discussion and the perceived contribution. Accessibility should come from a plain-language loop and strong visuals, not from omitting methodological validation. The best target is a two-layer talk: every listener can follow the engineering decision, while experts can see the experimental budget, comparator, noise model, and constraint formulation.\\n\\n---\\n\\n# Predicted reception\\n\\n**Current outline: 5/10.** The loop and application are memorable, but without measured results, a budget-matched baseline, and narrower simulation and planetary claims, the talk reads as a polished motivation for an experiment rather than a completed DAC-10 contribution.\\n\\nIf the results establish repeatability and budgeted improvement, and the three priority revisions are made, the same structure could plausibly move into the **7\\u20138/10** range. That conditional estimate is a judgment about presentation readiness, not a prediction of unobserved experimental outcomes.\\n\\n---\\n\\n# Discretionary analytical decisions\\n\\n- Treated the unfinished evidence/results section as missing evidence rather than assuming that the planned campaign succeeds.\\n- Evaluated the artifact as a competent 15-minute delivery from the outline, not as a line edit of the outline or an assessment of slides that do not yet exist.\\n- Weighted technical credibility and evidentiary support more heavily than visual polish because the target is the DAC-10 design-optimization audience.\\n- Used the submitted abstract to resolve technical intent when the outline was ambiguous, while flagging inconsistencies rather than silently harmonizing them.\\n- Interpreted the stated optimization problem as two objectives, specific energy absorption and compaction efficiency, subject to a peak-force constraint; flagged language that instead treats peak force as an objective.\\n- Assigned qualitative damage levels of moderate, moderate-to-severe, or severe based on how directly an unanswered challenge would undermine each persona\\u2019s acceptance of the central claim.\\n- Chose P6 as the best-served persona because the present outline emphasizes an intuitive workflow and practical efficiency promise; chose P1 as the worst-served because the DAC-specific validation and novelty case is not yet developed.\\n- Scored predicted reception on the current incomplete outline, not on a hypothetical final talk with successful results.\"}", + "name": "submit_answer" + } + } + ], + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "user", + "content": "\nMessage was NOT sent to user. Please call the send_message tool to communicate with the user. Use wait=True if you need a response before proceeding, or wait=False if you are providing a status update to the user.", + "info": { + "is_human": false + } + } + ], + "next_observation": [ + { + "role": "user", + "content": "\nMessage was NOT sent to user. Please call the send_message tool to communicate with the user. Use wait=True if you need a response before proceeding, or wait=False if you are providing a status update to the user.", + "info": { + "is_human": false + } + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "callback_type": "after_agent_get_asv" + }, + "action": { + "call_id": { + "run_id": "fc2d1a3a-48db-409f-bfe9-7aaa8fe946b3", + "fwd_id": "6b88bc00-7c53-44b5-a7c0-37329a18fa57" + }, + "op_name": "IdentityOp-3c0dc047595c", + "op_class_name": "ldp.graph.common_ops.IdentityOp", + "value": { + "role": "assistant", + "content": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_zSHcqZDq4Rrb984ui6lpQugL", + "type": "function", + "function": { + "arguments": "{\"answer\": \"# Mock audience report: IDETC-CIE 2026 DAC-10 talk\\n\\n## Scope of this review\\n\\nThis evaluates the talk implied by the outline, not finished slides or delivery technique. The experimental campaign is still in progress, so I do **not** infer any favorable results. The empty \\u201cEvidence / results\\u201d section is treated as a major unresolved part of the talk: the outline currently explains why the workflow should be useful, but does not yet demonstrate that it is useful.\\n\\nThe central communication test is whether each listener leaves with the intended message:\\n\\n> By closing the loop between multi-material 3D printing and Bayesian optimization, we can optimize tensegrity energy absorbers directly from real impact data, in dozens of prints rather than thousands.\\n\\n---\\n\\n# P1. The skeptical Bayesian-optimization insider\\n\\n## (a) First-person reaction\\n\\nThe application is potentially interesting, and the design\\u2013print\\u2013test loop is easy to understand. The mixed continuous, integer, and categorical design space, physical failures, parallel fabrication, and multiple constrained objectives could make this a worthwhile design-automation case study.\\n\\nBut the outline keeps telling me that Bayesian optimization is sample-efficient without showing that **this implementation** is sample-efficient. \\u201cDozens, not thousands\\u201d sounds like a slogan unless I see the actual experiment count, initialization size, batch size, replication policy, convergence behavior, and a comparison against credible alternatives. q-noisy expected hypervolume improvement, or qNEHVI, is established machinery. Merely deploying it does not establish methodological novelty.\\n\\nI become skeptical at the statement that qNEHVI was selected because of heteroscedastic TPU and print noise. qNEHVI accounts for noisy observations, but the outline does not explain how the surrogate models heteroscedasticity, whether repeated specimens identify noise, or whether noise is being conflated with unmodeled process drift. Independent Gaussian processes with an unspecified observation model do not automatically resolve batch-dependent heteroscedasticity.\\n\\nMy attention drifts during the broad argument against simulation because it delays the information I came to DAC-10 to hear: the design variables, model formulation, constraint handling, acquisition implementation, budget, and benchmark. The Super Ball Bot hook is visually effective, but planetary landing may feel like application theater if the actual specimens and impact regime are far removed from that use case.\\n\\nThe strongest possible section is the results slot. In its current empty state, the talk has a method narrative but no validation.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey used standard batch noisy multi-objective Bayesian optimization to choose which multi-material printed impact specimens to test, instead of running a full physical sweep.\\u201d\\n\\n**Distortion from the intended message:** I would omit the claim that the structures were optimized in dozens of prints because I have not seen evidence for it. I would also describe qNEHVI as standard rather than as the contribution.\\n\\n## (c) Top three Q&A questions\\n\\n1. **What is the actual experimental budget?** How many initial designs, adaptive iterations, specimens per batch, and physical replicates were used, and what stopping rule defined success?\\n2. **What baselines did you beat?** Did you compare qNEHVI with Sobol or Latin-hypercube sampling, random search, scalarized Bayesian optimization, or a domain-informed sequential design under the same number of physical tests?\\n3. **How exactly is noise modeled?** Are replicate-dependent variances supplied to the Gaussian processes, is a heteroscedastic likelihood fitted, and how are TPU lot, print batch, machine drift, and specimen-level defects distinguished?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhat did you contribute beyond applying off-the-shelf qNEHVI to a new specimen family?\\u201d\\n\\n**Damage if unanswered: severe.** For a DAC review audience, this can reduce the work from a design-automation contribution to an application demonstration. A strong answer could rest on the experimental formulation, mixed-variable and failure-aware implementation, quantitative sample-efficiency evidence, and transferable operating lessons. Those contributions must be stated explicitly and tested.\\n\\n---\\n\\n# P2. The aerospace entry, descent, and landing practitioner\\n\\n## (a) First-person reaction\\n\\nThe opening lands immediately. I recognize the Super Ball Bot concept, and \\u201cthe lander is the energy absorber\\u201d is a useful framing. The three performance quantities also sound relevant: energy absorbed per mass, usable stroke, and force transmitted to the payload.\\n\\nThen the talk moves into language I do not naturally use: Gaussian-process surrogate, qNEHVI, acquisition function, hypervolume, exploration versus exploitation, and Pareto front. I can accept that the algorithm chooses the next tests, but I need a plain explanation of what information it uses and why I should trust the recommendations. If the presenter spends several minutes naming algorithms without connecting them to engineering decisions, I will lose the thread.\\n\\nMy main concern is application fidelity. A desktop drop-weight test at one fixed impact energy is not automatically evidence for entry, descent, and landing hardware. I need specimen dimensions, impact mass, velocity, energy, strain rate, boundary conditions, number of impacts, rebound behavior, temperature, vacuum sensitivity, and failure containment. PLA and TPU raise immediate environmental and qualification questions. I also want to know whether the absorber is reusable or sacrificial.\\n\\nMy attention returns when I see force\\u2013time traces, high-speed footage, and damaged specimens. It drifts if the talk stays on the optimizer rather than connecting each output to payload protection.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey have an algorithm that learns from successive drop tests and tells them which 3D-printed crush structure to make next, so they can find a lightweight low-force design with fewer prototypes.\\u201d\\n\\n**Distortion from the intended message:** I would probably call the structures \\u201ccrush structures,\\u201d losing the tensegrity distinction. I might also overgeneralize the method to planetary landers even though the current tests appear to be laboratory-scale.\\n\\n## (c) Top three Q&A questions\\n\\n1. What impact mass, velocity, energy, and strain-rate regime do the tests cover, and how do those values compare with a credible lander load case?\\n2. How do the selected designs scale with size and impact energy? Are the controlling mechanisms material-rate effects, geometric buckling, contact, or all three?\\n3. What happens under off-axis impact, repeated impacts, temperature extremes, aging, vacuum, and manufacturing defects?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhy should I believe that a PLA\\u2013TPU specimen optimized at one laboratory impact condition tells me anything about flight-scale landing protection?\\u201d\\n\\n**Damage if unanswered: severe for the planetary-landing framing, moderate for the underlying optimization study.** The presenter should clearly label planetary landing as motivation, not demonstrated readiness, and state the present technology scope. Otherwise the opening and closing overpromise relative to the reported experiment.\\n\\n---\\n\\n# P3. The additive-manufacturing and materials researcher\\n\\n## (a) First-person reaction\\n\\nThe monolithic PLA\\u2013TPU architecture is the part I care about. The core-wrapping strategy sounds more credible than simply depositing TPU against exposed PLA and assuming durable adhesion. Holding print settings fixed within a batch, weighing specimens, checking dimensions, and periodically testing a control are all sensible.\\n\\nI still need much more fabrication detail before I accept that design geometry is the main source of performance variation. PLA\\u2013TPU interfaces are sensitive to temperature, surface contact, moisture, residence time, toolpath order, contamination, and local geometry. \\u201cPrint defects\\u201d is too broad. I want failure-mode evidence showing whether energy is absorbed through intended tension-network deformation, TPU hysteresis, strut buckling, interface peeling, or accidental damage.\\n\\nThe phrase \\u201cno assembly\\u201d catches my attention, but \\u201celiminates assembly entirely\\u201d may be too strong. Does the build require support removal, trimming, conditioning, fixture insertion, post-tensioning, or manual alignment? If so, the accurate claim is \\u201csingle-build co-fabrication\\u201d or \\u201cno joining of PLA and TPU members,\\u201d not zero assembly.\\n\\nI am a Bayesian-optimization novice. I can follow the loop if the presenter says, \\u201cThe model proposes the next small batch where improvement is plausible or uncertainty is high.\\u201d I will disengage if the slide foregrounds qNEHVI notation rather than the physical decisions it enables.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey co-print rigid PLA and flexible TPU energy absorbers, test them, and use the results to choose the next geometries instead of printing a large design-of-experiments grid.\\u201d\\n\\n**Distortion from the intended message:** I would remember the fabrication loop more strongly than tensegrity or Bayesian optimization. I might interpret the improvement as mostly a material-interface result unless the structural mechanisms are shown.\\n\\n## (c) Top three Q&A questions\\n\\n1. How was PLA\\u2013TPU bonding quantified, and where did failed specimens actually fail: within TPU, within PLA, or at the interface?\\n2. How many nominally identical replicates were printed and tested, and how large were within-batch, between-batch, and printer-drift effects relative to design effects?\\n3. What manual operations occur after printing, and what exactly does \\u201cno assembly\\u201d exclude?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cHow do you know the optimizer is learning architecture rather than uncontrolled interface quality and process drift?\\u201d\\n\\n**Damage if unanswered: severe.** If fabrication variation is not measured and separated from design effects, both the surrogate and the claimed Pareto front may be unstable. Replicates, blocking or covariates, control specimens, dimensional measurements, and failure-mode documentation are needed.\\n\\n---\\n\\n# P4. The first-year graduate student\\n\\n## (a) First-person reaction\\n\\nThe opening video and the phrase \\u201cthe lander is the energy absorber\\u201d give me a concrete picture. I understand the basic problem: too many designs to print, and computer models may miss messy material behavior. I also understand the loop when it is described as \\u201cdesign, print, drop, learn, repeat.\\u201d That story carries me.\\n\\nI start getting lost when several unfamiliar terms arrive together: tensegrity-inspired, compaction efficiency, Gaussian process, surrogate, acquisition function, qNEHVI, heteroscedastic noise, Pareto front, probabilistic feasibility constraint, and exploration versus exploitation. The preview says the audience already believes optimization works, but I still need one intuitive sentence explaining what Bayesian optimization does. Skipping \\u201cBayesian optimization 101\\u201d should not mean skipping the conceptual bridge.\\n\\nI am also unsure how the structure works. Are the TPU parts stretched before impact? Are the PLA struts touching? What makes this \\u201ctensegrity-inspired\\u201d rather than a multi-material lattice? A labeled specimen image and a short deformation sequence would help much more than a verbal definition.\\n\\nMy attention drifts during the repeated case against simulation because I accepted that point the first time. It returns for printing footage, the drop test, and a clear plot with a marked \\u201cbetter\\u201d direction.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey let an artificial-intelligence program learn from 3D-printed drop tests and keep suggesting better shock absorbers, so they do not have to test every design.\\u201d\\n\\n**Distortion from the intended message:** I would probably call Bayesian optimization \\u201cAI,\\u201d omit the two-objective trade-off and force constraint, and assume each new design is simply better than the last. I might also miss what tensegrity contributes.\\n\\n## (c) Top three Q&A questions\\n\\n1. What makes one of these specimens a tensegrity structure rather than an ordinary 3D-printed lattice?\\n2. How does the computer decide which design to print next, in plain language?\\n3. Can one design maximize both energy absorption and compaction efficiency while also lowering peak force, or must those goals trade off?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cI followed the loop, but I still do not understand what is physically special about the structure or what the optimizer actually learned.\\u201d\\n\\n**Damage if unanswered: moderate.** Experts can fill in some gaps, but a talk that leaves novices with only \\u201cAI plus 3D printing\\u201d has failed to communicate its engineering mechanism. One specimen-mechanics visual and one plain-language optimization visual would largely solve this.\\n\\n---\\n\\n# P5. The finite-element-analysis veteran\\n\\n## (a) First-person reaction\\n\\nThe statement \\u201csimulation can\\u2019t be trusted for these structures\\u201d puts me on the defensive before I have seen any evidence. Interfacial slip, viscoelasticity, large deformation, buckling, contact, strain-rate dependence, and manufacturing variability are difficult, but they are not intrinsically beyond finite-element analysis. Models can be calibrated, uncertainty can be propagated, and simulation can still screen designs even if it is imperfect.\\n\\nThe abstract is more defensible than the talk outline. It says the workflow avoids **direct dependence on calibrated finite-element simulation for objective evaluation** and acknowledges future analytical or multifidelity shortcuts. The presentation should use that language. The current outline turns a project-scope choice into a universal technical claim.\\n\\nI accept the economic argument that calibrating a high-fidelity model may not be worthwhile for an early design campaign. That is a stronger position: measured tests provide authoritative objective values, while the study asks whether sequential design can use a small physical budget efficiently. I would also welcome evidence that simulation errors change design rankings or miss observed failure modes. Without such evidence, the repeated attack on simulation sounds like a straw man.\\n\\nMy attention drifts when the talk says simulation is unreliable several times without showing a simulation\\u2013experiment discrepancy. It returns when the presenter discusses physical failure modes and admits where lower-fidelity physics could later improve the loop.\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey skipped finite-element modeling and used Bayesian optimization directly on physical tests because they judged model calibration too expensive for these multi-material specimens.\\u201d\\n\\n**Distortion from the intended message:** I would frame the work as bypassing simulation rather than as enabling rapid experimental optimization. If the presenter is combative about simulation, that dispute may become the only thing I remember.\\n\\n## (c) Top three Q&A questions\\n\\n1. What evidence shows that a calibrated finite-element model is insufficient for design ranking, rather than merely costly to construct?\\n2. Did you compare the physical-test-only workflow with a simple mechanics model or multifidelity surrogate that could reduce the experimental budget further?\\n3. Which observed behaviors dominate the model discrepancy: interface failure, TPU viscoelasticity, contact, buckling imperfections, or geometry errors from printing?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cYou have shown that simulation is inconvenient, not that it cannot be trusted. Why is abandoning it scientifically preferable to calibrating and validating it?\\u201d\\n\\n**Damage if unanswered: severe and avoidable.** The current absolute wording invites a technical fight that is unnecessary to the contribution. Reframing the claim as a cost-and-dependence decision would preserve the motivation without asserting that finite-element modeling is incapable.\\n\\n---\\n\\n# P6. The friendly industry generalist\\n\\n## (a) First-person reaction\\n\\nThe loop is memorable: print a candidate, test it, update the model, and print the next informative candidate. I can imagine using that pattern for brackets, seals, lattice pads, or process settings when simulation is weak and tests are expensive. \\u201cDozens rather than thousands\\u201d is exactly the kind of value proposition I remember.\\n\\nI do not need the mathematical details of qNEHVI, but I need an operational recipe. What software is used? How many initial samples are required? How automated is the handoff from optimizer to computer-aided design and slicing? How long does one cycle take? What expertise does a team need? The abstract says candidate selection is automated but slicing, specimen handling, and testing remain manual. That qualification should appear in the talk before I infer a robotic self-driving laboratory.\\n\\nThe tensegrity and planetary material is interesting, but my attention will drift if it occupies too much of a 15-minute slot. The method becomes useful to me only when the presenter explicitly generalizes it and defines when it is worth using.\\n\\nThe ending about moving humankind forward is less memorable than a concrete deployment rule. I would prefer a final slide saying: \\u201cUse this loop when tests are authoritative, each test is costly, the design space is mixed, and several performance goals conflict.\\u201d\\n\\n## (b) What I would repeat the next day\\n\\n> \\u201cThey showed a practical test-driven optimization loop that can find good 3D-printed designs with a few dozen prototypes instead of a giant test matrix.\\u201d\\n\\n**Distortion from the intended message:** I may overstate automation and assume the \\u201cfew dozen\\u201d claim is established across applications. I will probably forget the names qNEHVI and tensegrity, but retain the workflow.\\n\\n## (c) Top three Q&A questions\\n\\n1. What parts of the workflow are automated today, and what does an engineer still do manually between recommendation and test result?\\n2. What minimum experiment budget and software stack would my team need to try this on a different component?\\n3. When is this approach better than a conventional design of experiments, response-surface method, or a calibrated simulation?\\n\\n## (d) Most pointed challenge\\n\\n> \\u201cWhat concrete evidence tells me this saved enough prototypes and engineering time to justify the added optimization infrastructure?\\u201d\\n\\n**Damage if unanswered: moderate to severe.** I will still like the idea, but I will not take it back to my team. A budget-matched baseline and a simple timeline or cost comparison would convert interest into action.\\n\\n---\\n\\n# Synthesis\\n\\n## Cross-persona themes\\n\\n### 1. The causal chain is understandable, but the claimed payoff is not yet demonstrated\\n\\nAll six personas can understand the basic design\\u2013print\\u2013test\\u2013learn loop. The empty results section prevents them from deciding whether the loop actually found better designs, found them faster, or handled noise reliably. The phrase \\u201cdozens, not thousands\\u201d is currently an unsupported quantitative claim.\\n\\nAt minimum, the results need to show:\\n\\n- total number of unique designs and physical specimens;\\n- initial versus adaptively selected designs;\\n- batch size and number of iterations;\\n- replicate policy and failure count;\\n- measured uncertainty or repeatability;\\n- progress under the actual experimental budget;\\n- the final feasible Pareto set for **specific energy absorption and compaction efficiency subject to the force cap**;\\n- a budget-matched nonadaptive or simpler optimization baseline.\\n\\n### 2. \\u201cSimulation can\\u2019t be trusted\\u201d is too absolute\\n\\nP1 questions the evidence, P2 cares about application validity, and P5 directly rejects the premise. Even P6 needs to know when the loop is preferable to simulation. The abstract already contains better wording: physical measurements are used for objective evaluation \\u201cwithout relying on calibrated finite-element simulation.\\u201d\\n\\nThe defensible claim is not that simulation fails universally. It is that a sufficiently calibrated multiphysics model may be expensive relative to the available design campaign, and the authors therefore test whether physical-data-driven sequential optimization is useful without depending on such a model.\\n\\n### 3. The talk needs a two-level explanation of Bayesian optimization\\n\\nP1 does not want introductory Bayesian-optimization material; P2, P3, P4, and P5 need a plain conceptual bridge. These needs are compatible. Give the intuition in one sentence and one visual, then put the implementation specifics in a compact technical panel or backup slide.\\n\\nSuggested spoken explanation:\\n\\n> \\u201cAfter each batch, the model estimates both expected performance and uncertainty across the design space. The acquisition rule selects a small next batch that is most likely to expand the feasible trade-off frontier, while accounting for noisy tests and print failures.\\u201d\\n\\nThis is not \\u201cBayesian optimization 101.\\u201d It is the minimum explanation needed to interpret the workflow.\\n\\n### 4. The physical mechanism and the word \\u201ctensegrity\\u201d need clarification\\n\\nP2 may reduce the objects to crush structures, P3 may interpret the result as an interface study, and P4 may not distinguish the specimens from ordinary lattices. A labeled as-printed specimen plus a four-frame deformation sequence should show:\\n\\n- rigid PLA struts;\\n- continuous flexible TPU network;\\n- whether and how pre-tension exists;\\n- load path before and during impact;\\n- intended energy-dissipation mechanisms;\\n- why \\u201ctensegrity-inspired\\u201d is the precise term.\\n\\n### 5. Manufacturing variability is part of the model, not background noise\\n\\nP1 and P3 will both press this point. Periodically retesting one control is useful but may not identify design-specific heteroscedasticity, lot effects, or confounding between iteration and material batch. The talk needs to state the replication, randomization, blocking, covariate, and drift-monitoring plan. If those steps were not used, the limitation must be explicit.\\n\\n### 6. The application framing outruns the demonstrated scope\\n\\nThe planetary hook is strong, but P2 will not equate fixed-energy laboratory tests on PLA\\u2013TPU specimens with flight hardware. The closing escalates further by claiming that the next lander absorber can be \\u201cproven\\u201d in weeks. That wording should be narrowed unless the campaign includes relevant scaling and environmental evidence.\\n\\n### 7. The objective statement is not fully consistent across the outline\\n\\nThe task and Point 2 correctly describe two objectives, specific energy absorption and compaction efficiency, with a peak-force constraint. The candidate evidence list instead proposes \\u201cSEA vs. peak transmitted force,\\u201d and the abstract\\u2019s expected-outcomes section similarly describes trade-offs between SEA and peak force. Those are different optimization formulations.\\n\\nThe talk must consistently distinguish:\\n\\n- **objectives:** maximize specific energy absorption and compaction efficiency;\\n- **constraint:** peak transmitted force must not exceed a specified cap;\\n- **feasible Pareto front:** trade-offs between the two objectives among designs satisfying that cap.\\n\\nIf peak force is instead treated as a third objective, the task statement and optimization formulation must be revised accordingly.\\n\\n### 8. The closing favors inspiration over technical precision\\n\\nP6 is likely to remember \\u201cdozens, not thousands,\\u201d but P1, P2, and P5 may hear overreach in \\u201cproven,\\u201d \\u201cin weeks,\\u201d and \\u201cmoves humankind forward.\\u201d A DAC-10 close should end on the measured engineering result and transfer condition. The Super Ball Bot bookend can remain, but it should not imply flight qualification.\\n\\n---\\n\\n# Three highest-priority revisions\\n\\n## Priority 1: Build the talk around a quantitative result, not around the availability of a workflow\\n\\nReplace the placeholder with a required three-slide evidence sequence:\\n\\n1. **\\u201cThe campaign used N specimens across B adaptive batches under a fixed physical-test budget.\\u201d** \\n Show initialization, adaptive batches, replicates, failures, elapsed cycle time, and the force threshold. Do not use `N` or `B` in the actual talk; insert the observed values.\\n\\n2. **\\u201cAdaptive selection improved the feasible design set faster than [predeclared baseline].\\u201d** \\n Plot a budget-matched performance measure across physical tests, with uncertainty across repeated runs if available. If only one physical campaign exists, avoid inferential claims that require repeated campaigns and supplement with clearly labeled retrospective resampling or simulation-based algorithm checks.\\n\\n3. **\\u201cThese measured designs form the final feasible trade-off between specific energy absorption and compaction efficiency.\\u201d** \\n Show uncertainty or replicate spread, mark infeasible force-cap violations, and include specimen images or force\\u2013time traces for representative Pareto designs.\\n\\nIf the campaign cannot support those statements, revise the main message from \\u201cwe can optimize \\u2026 in dozens\\u201d to \\u201cwe are evaluating whether noisy multi-objective Bayesian optimization can reduce the physical test budget.\\u201d\\n\\n## Priority 2: Replace the anti-simulation premise with a scoped engineering decision\\n\\nReplace:\\n\\n> \\u201cSimulation can\\u2019t be trusted for these structures.\\u201d\\n\\nwith:\\n\\n> \\u201cFor these multi-material prints, obtaining objective values from a validated high-fidelity model would require substantial calibration of interface, rate, contact, and defect behavior. We therefore ask how far a limited budget of direct physical tests can take us without depending on that model.\\u201d\\n\\nThen title Point 1 with a message rather than a topic:\\n\\n> **\\u201cWhen model calibration and exhaustive testing are both costly, each physical specimen must be chosen for information value.\\u201d**\\n\\nThis keeps the pincer structure while avoiding an unnecessary universal claim.\\n\\n## Priority 3: Reallocate the 15 minutes around audience decisions\\n\\nA concrete timing plan:\\n\\n- **1.5 min:** Super Ball Bot hook, specimen, and present study scope.\\n- **2 min:** Why exhaustive physical search and high-fidelity calibration are costly.\\n- **2.5 min:** One visual of the closed loop, including a one-sentence Bayesian-optimization explanation.\\n- **2 min:** Actual design variables, objectives, force constraint, fabrication failures, and noise controls.\\n- **5 min:** measured results, baseline, uncertainty, representative specimens, and failure modes.\\n- **1 min:** limitations and transfer conditions.\\n- **1 min:** measured conclusion and bookend.\\n\\nMove kernel choices, log-space acquisitions, detailed encoding, and other implementation material to backup slides unless a result depends directly on them. This follows Doumont\\u2019s principle that each slide should communicate one interpreted message rather than expose the speaker\\u2019s notes.\\n\\n---\\n\\n# Claims needing evidence, hedging, or backup slides\\n\\n| Claim or topic | What is needed in the main talk | Prepared backup material |\\n|---|---|---|\\n| \\u201cDozens of prints, not thousands\\u201d | Actual specimen count, definition of success, and budget-matched comparator | Full campaign ledger; sensitivity to initialization and stopping rule |\\n| \\u201cSimulation can\\u2019t be trusted\\u201d | Replace with scoped calibration-cost language unless direct validation evidence exists | Simulation\\u2013experiment traces, ranking errors, or documented failure modes a tested model missed |\\n| \\u201cqNEHVI handles heteroscedastic noise\\u201d | Specify the actual observation-noise model and replication strategy | Likelihood formulation, fixed versus inferred noise, residual diagnostics, replicate variance by design or batch |\\n| qNEHVI \\u201cover standard expected improvement\\u201d | Use a technically appropriate comparison; ordinary expected improvement is not a like-for-like multi-objective batch baseline | Comparisons with random/Sobol, scalarization, qEHVI or another relevant noisy/constrained baseline |\\n| \\u201cThe Pareto front is the design deliverable\\u201d | Show the two stated objectives and force-cap feasibility consistently | Hypervolume reference point, normalization, uncertainty, constraint treatment |\\n| Peak-force cap | State its numerical value and engineering rationale | Sensitivity of selected designs to alternative cap values |\\n| Probabilistic print-feasibility constraint | Report failures and demonstrate that the classifier or constraint model is identifiable at the available sample size | Failure labels, calibration, confusion or reliability metrics, acquisition formula |\\n| \\u201cNo assembly\\u201d / \\u201celiminates assembly entirely\\u201d | List any post-print operations; use \\u201csingle-build co-fabrication\\u201d if manual steps remain | Fabrication workflow photographs and labor-time accounting |\\n| \\u201cCandidate goes to tested specimen in hours\\u201d | Median or representative print-to-result time and what remains manual | Time breakdown for design, slicing, printing, conditioning, inspection, and testing |\\n| \\u201cDramatically faster\\u201d | Define faster relative to a measured or estimated comparator | Cost and wall-clock accounting with assumptions |\\n| Transfer to other architectures | Present as a conditional hypothesis, not a demonstrated result | Applicability checklist: test cost, dimensionality, noise, throughput, and constraint structure |\\n| Planetary-landing relevance | Clearly state that this is motivation unless scale and environment are tested | Impact similitude, energy and velocity regime, environmental gaps, technology-readiness discussion |\\n| PLA\\u2013TPU interface robustness | Failure-mode and repeatability evidence | Microscopy or fracture images, process parameters, conditioning and moisture controls |\\n| \\u201cTensegrity-inspired\\u201d | Show the load path and identify which tensegrity features are retained | Geometric definition, pre-tension status, comparison with a conventional lattice/control |\\n| Independent Gaussian processes | State why cross-objective correlation is ignored and whether that choice matters | Residual correlations and sensitivity to multi-output alternatives |\\n| Mixed categorical variables | Briefly state how candidates are represented and optimized | Kernel or encoding details; treatment of invalid combinations |\\n\\n---\\n\\n# Fit to the six-person audience\\n\\n## Best served: P6, the friendly industry generalist\\n\\nThe outline has a clear problem\\u2013method\\u2013application arc, a memorable loop, and an attractive efficiency promise. P6 can understand the proposed value without needing the mathematics. The danger is that this persona may accept unsupported claims and overestimate the system\\u2019s automation and maturity.\\n\\nP4 also benefits from the story structure, but the unexplained terminology and absent physical-mechanism visual will cause substantial loss of detail.\\n\\n## Worst served: P1, the skeptical Bayesian-optimization insider\\n\\nP1 is the most likely to judge the current talk as an off-the-shelf acquisition function applied to a new artifact. The outline anticipates this concern by saying not to teach Bayesian optimization, but it does not yet supply the evidence P1 needs: novelty positioning, rigorous baseline, budget, noise identification, constraint diagnostics, and measured sample efficiency.\\n\\nP5 is nearly as poorly served because the opening motivation directly challenges finite-element modeling more strongly than the abstract supports.\\n\\n## Is that the right trade-off for DAC-10?\\n\\nNo. A DAC-10 presentation should remain accessible to P2\\u2013P6, but it cannot underserve P1. The design-automation expert is likely to shape the technical discussion and the perceived contribution. Accessibility should come from a plain-language loop and strong visuals, not from omitting methodological validation. The best target is a two-layer talk: every listener can follow the engineering decision, while experts can see the experimental budget, comparator, noise model, and constraint formulation.\\n\\n---\\n\\n# Predicted reception\\n\\n**Current outline: 5/10.** The loop and application are memorable, but without measured results, a budget-matched baseline, and narrower simulation and planetary claims, the talk reads as a polished motivation for an experiment rather than a completed DAC-10 contribution.\\n\\nIf the results establish repeatability and budgeted improvement, and the three priority revisions are made, the same structure could plausibly move into the **7\\u20138/10** range. That conditional estimate is a judgment about presentation readiness, not a prediction of unobserved experimental outcomes.\\n\\n---\\n\\n# Discretionary analytical decisions\\n\\n- Treated the unfinished evidence/results section as missing evidence rather than assuming that the planned campaign succeeds.\\n- Evaluated the artifact as a competent 15-minute delivery from the outline, not as a line edit of the outline or an assessment of slides that do not yet exist.\\n- Weighted technical credibility and evidentiary support more heavily than visual polish because the target is the DAC-10 design-optimization audience.\\n- Used the submitted abstract to resolve technical intent when the outline was ambiguous, while flagging inconsistencies rather than silently harmonizing them.\\n- Interpreted the stated optimization problem as two objectives, specific energy absorption and compaction efficiency, subject to a peak-force constraint; flagged language that instead treats peak force as an objective.\\n- Assigned qualitative damage levels of moderate, moderate-to-severe, or severe based on how directly an unanswered challenge would undermine each persona\\u2019s acceptance of the central claim.\\n- Chose P6 as the best-served persona because the present outline emphasizes an intuitive workflow and practical efficiency promise; chose P1 as the worst-served because the DAC-specific validation and novelty case is not yet developed.\\n- Scored predicted reception on the current incomplete outline, not on a hypothetical final talk with successful results.\"}", + "name": "submit_answer" + } + } + ] + }, + "logprob": null + } + } + }, + "created_at": "2026-07-18T17:57:21.925498Z" + }, + { + "id": "0ac9b33b-6890-4d92-9449-14f8feede8ca", + "trajectory_id": "4949af58-f08a-4541-ad1c-1f0a95c6f07d", + "trajectory_timestep": 0, + "step": "before_transition", + "state": { + "transition": { + "timestep": 0, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "info": null + }, + { + "role": "assistant", + "content": "I\u2019ll evaluate the outline as a delivered 15-minute talk, treating the unfinished results section as a substantive gap rather than inventing outcomes. I\u2019ll score each persona against comprehension, credibility, relevance, and recall, then synthesize revisions for DAC-10.", + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "info": null + }, + { + "role": "assistant", + "content": "I\u2019ll evaluate the outline as a delivered 15-minute talk, treating the unfinished results section as a substantive gap rather than inventing outcomes. I\u2019ll score each persona against comprehension, credibility, relevance, and recall, then synthesize revisions for DAC-10.", + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "user", + "content": "Message was NOT sent to user. Please call the send_message tool to communicate with the user. Use wait=True if you need a response before proceeding, or wait=False if you are providing a status update to the user.", + "info": { + "is_human": false + } + } + ], + "next_observation": [ + { + "role": "user", + "content": "Message was NOT sent to user. Please call the send_message tool to communicate with the user. Use wait=True if you need a response before proceeding, or wait=False if you are providing a status update to the user.", + "info": { + "is_human": false + } + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "callback_type": "before_transition" + }, + "action": { + "call_id": { + "run_id": "83f68ef7-d9cd-43aa-9e33-89a75177b5b6", + "fwd_id": "fda6e6df-e1b7-40ee-af5d-534915f0efc9" + }, + "op_name": "IdentityOp-d2b82871a96e", + "op_class_name": "ldp.graph.common_ops.IdentityOp", + "value": { + "role": "assistant", + "content": "I\u2019ll evaluate the outline as a delivered 15-minute talk, treating the unfinished results section as a substantive gap rather than inventing outcomes. I\u2019ll score each persona against comprehension, credibility, relevance, and recall, then synthesize revisions for DAC-10." + }, + "logprob": null + } + } + }, + "created_at": "2026-07-18T17:54:36.276976Z" + }, + { + "id": "6f7a8ba7-3658-4708-8c33-95596e4a8d46", + "trajectory_id": "4949af58-f08a-4541-ad1c-1f0a95c6f07d", + "trajectory_timestep": 0, + "step": "after_transition", + "state": { + "transition": { + "timestep": 0, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "info": null + }, + { + "role": "assistant", + "content": "I\u2019ll evaluate the outline as a delivered 15-minute talk, treating the unfinished results section as a substantive gap rather than inventing outcomes. I\u2019ll score each persona against comprehension, credibility, relevance, and recall, then synthesize revisions for DAC-10.", + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "info": null + } + ], + "next_observation": [ + { + "role": "user", + "content": "Message was NOT sent to user. Please call the send_message tool to communicate with the user. Use wait=True if you need a response before proceeding, or wait=False if you are providing a status update to the user.", + "info": { + "is_human": false + } + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "time_elapsed_before_transition": 1.0266580860006798, + "time_elapsed_agent_get_asv": 11.617149923000397, + "time_elapsed_after_agent_get_asv": 0.8846558580007695, + "time_elapsed_env_step": 0.001249859000381548, + "time_elapsed_after_env_step": 0.9448930449998443 + }, + "action": { + "call_id": { + "run_id": "83f68ef7-d9cd-43aa-9e33-89a75177b5b6", + "fwd_id": "fda6e6df-e1b7-40ee-af5d-534915f0efc9" + }, + "op_name": "IdentityOp-d2b82871a96e", + "op_class_name": "ldp.graph.common_ops.IdentityOp", + "value": { + "role": "assistant", + "content": "I\u2019ll evaluate the outline as a delivered 15-minute talk, treating the unfinished results section as a substantive gap rather than inventing outcomes. I\u2019ll score each persona against comprehension, credibility, relevance, and recall, then synthesize revisions for DAC-10." + }, + "logprob": null + } + } + }, + "created_at": "2026-07-18T17:54:34.707252Z" + }, + { + "id": "5b1dbeb0-b826-4184-a570-bb30493b9067", + "trajectory_id": "4949af58-f08a-4541-ad1c-1f0a95c6f07d", + "trajectory_timestep": 0, + "step": "after_env_step", + "state": { + "transition": { + "timestep": 0, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "info": null + }, + { + "role": "assistant", + "content": "I\u2019ll evaluate the outline as a delivered 15-minute talk, treating the unfinished results section as a substantive gap rather than inventing outcomes. I\u2019ll score each persona against comprehension, credibility, relevance, and recall, then synthesize revisions for DAC-10.", + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "info": null + } + ], + "next_observation": [ + { + "role": "user", + "content": "Message was NOT sent to user. Please call the send_message tool to communicate with the user. Use wait=True if you need a response before proceeding, or wait=False if you are providing a status update to the user.", + "info": { + "is_human": false + } + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "callback_type": "after_env_step" + }, + "action": { + "call_id": { + "run_id": "83f68ef7-d9cd-43aa-9e33-89a75177b5b6", + "fwd_id": "fda6e6df-e1b7-40ee-af5d-534915f0efc9" + }, + "op_name": "IdentityOp-d2b82871a96e", + "op_class_name": "ldp.graph.common_ops.IdentityOp", + "value": { + "role": "assistant", + "content": "I\u2019ll evaluate the outline as a delivered 15-minute talk, treating the unfinished results section as a substantive gap rather than inventing outcomes. I\u2019ll score each persona against comprehension, credibility, relevance, and recall, then synthesize revisions for DAC-10." + }, + "logprob": null + } + } + }, + "created_at": "2026-07-18T17:54:33.787756Z" + }, + { + "id": "e1197b16-ac7f-420b-82f4-0f38b628f856", + "trajectory_id": "4949af58-f08a-4541-ad1c-1f0a95c6f07d", + "trajectory_timestep": 0, + "step": "after_agent_get_asv", + "state": { + "transition": { + "timestep": 0, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "info": null + }, + { + "role": "assistant", + "content": "I\u2019ll evaluate the outline as a delivered 15-minute talk, treating the unfinished results section as a substantive gap rather than inventing outcomes. I\u2019ll score each persona against comprehension, credibility, relevance, and recall, then synthesize revisions for DAC-10.", + "info": null + } + ], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "info": null + } + ], + "next_observation": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "\n## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\npwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "\nYou are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "info": null + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "callback_type": "after_agent_get_asv" + }, + "action": { + "call_id": { + "run_id": "83f68ef7-d9cd-43aa-9e33-89a75177b5b6", + "fwd_id": "fda6e6df-e1b7-40ee-af5d-534915f0efc9" + }, + "op_name": "IdentityOp-d2b82871a96e", + "op_class_name": "ldp.graph.common_ops.IdentityOp", + "value": { + "role": "assistant", + "content": "I\u2019ll evaluate the outline as a delivered 15-minute talk, treating the unfinished results section as a substantive gap rather than inventing outcomes. I\u2019ll score each persona against comprehension, credibility, relevance, and recall, then synthesize revisions for DAC-10." + }, + "logprob": null + } + } + }, + "created_at": "2026-07-18T17:54:32.906352Z" + }, + { + "id": "0eda4252-997b-4deb-aefa-2254375b1100", + "trajectory_id": "4949af58-f08a-4541-ad1c-1f0a95c6f07d", + "trajectory_timestep": 0, + "step": "before_transition", + "state": { + "transition": { + "timestep": 0, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "pwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "You are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "info": null + } + ], + "next_observation": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "pwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "You are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "info": null + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "callback_type": "before_transition" + }, + "action": null + } + }, + "created_at": "2026-07-18T17:54:20.259215Z" + }, + { + "id": "bb63f449-586e-4637-86bf-d5dbca57d99d", + "trajectory_id": "4949af58-f08a-4541-ad1c-1f0a95c6f07d", + "trajectory_timestep": 0, + "step": "after_agent_init_state", + "state": { + "transition": { + "timestep": 0, + "agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "next_agent_state": { + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ], + "messages": [], + "hide_old_env_states": false, + "hide_old_action_content": false, + "sliding_window": null + }, + "observation": [], + "next_observation": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "pwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "You are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "info": null + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "callback_type": "after_agent_init_state" + }, + "action": null + } + }, + "created_at": "2026-07-18T17:54:19.408654Z" + }, + { + "id": "a3dcec21-d420-4299-aa89-106c3abed94e", + "trajectory_id": "4949af58-f08a-4541-ad1c-1f0a95c6f07d", + "trajectory_timestep": 0, + "step": "after_env_reset", + "state": { + "transition": { + "timestep": 0, + "agent_state": null, + "next_agent_state": null, + "observation": [], + "next_observation": [ + { + "role": "system", + "content": "You are a rigorous data analysis agent with deep expertise in statistics, data science, and quantitative methods. Your primary directive is to provide accurate, evidence-based analysis in Jupyter notebooks while maintaining the highest standards of scientific integrity.\n\n## Core Principles\n\n**Do not fabricate data for any reason**\n\nYou must never invent, simulate, or fabricate data under any circumstances. All analyses, visualizations, and interpretations must be directly derivable from the provided dataset or data correctly pulled in from external sources (eg. gene annotations, external databases). If you cannot access required data you must report this limitation and end the analysis. You must not subsample data without an analytical or technical purpose. If the data must be subsampled due to memory limitations or other technical constraints, this must be justified and reported.\n\n**All analyses must demonstrate statistical rigor and methodological excellence**\n\nEvery analytical procedure must be statistically sound and suitable for the specific data type and research question being addressed. You should always consider and mention underlying statistical assumptions (normality, independence, homoscedasticity, etc.). You must check assumptions before applying statistical tests and report when assumptions are violated. You should use correct statistical terminology, notation, and precision in reporting. You must report relevant metrics: p-values, confidence intervals, effect sizes, test statistics, degrees of freedom. You should apply appropriate corrections for multiple comparisons when necessary. You must distinguish between correlation and causation, avoiding causal claims from observational data.\n\n**Report the limitations of the data and your analysis**\n\nIf a request is beyond your capabilities or the scope of provided data, you must state this clearly and concisely. You should never attempt to answer questions requiring domain knowledge you do not possess or cannot acquire through use of tools such as external data sources or web search. You must acknowledge limitations of methods, sample sizes, and data quality. You should communicate uncertainty and confidence levels explicitly.\n\n**Never fabricate solutions when you cannot complete a task**\n\nIf you cannot do something, you must never fabricate a solution. You should clearly state what you cannot do and why, rather than providing false or misleading information.\n\n**Be concise and focused in your analysis**\n\nYou should address the research question directly and efficiently. You must avoid extraneous information that doesn't contribute to answering the question. You should present findings with appropriate statistical precision (don't over-report decimal places). You must provide concrete, quantitative evidence with specific values that support or refute hypotheses.\n\nYou are operating in a production execution environment with internet access and tools for data analysis, API calls, and data retrieval.\n\nYou do not have access to GPUs or specialized hardware, and you are limited to 3600 seconds of runtime.\n\nExecution resource limits:\n- cpu limit: 10\n- memory limit: 32Gi\n- /workspace directory limit: 50Gi\n- /tmp directory limit: 5Gi\nPlan execution around these limits. For large file operations, estimate peak disk usage first, including input, output, and temporary files. For memory-heavy work, estimate peak memory usage and use streaming or chunking when needed. If a plan may exceed a limit, use a smaller plan. If you exceed the memory limit or a listed directory limit, execution will be stopped and work will be lost.\n\n## Error Response Protocol\n\nWhen you cannot fulfill a request:\nYou must state clearly: \"I cannot [specific request] because [specific limitation]\"\nYou should explain: Brief explanation of the constraint or missing requirement\nYou must end analysis: Do not attempt workarounds that compromise data integrity\nYou should specify needs: If applicable, state what would be required to address the request properly\n\n## Structured Analysis Protocol\n\nStep 1: Define Analysis Plan\n\nOutline specific data filtering, processing, and analysis steps\nState the statistical methods and tests you will use\nIdentify potential limitations or assumptions\nExample format: \"1. Filter dataset for [criteria]. 2. Apply [transformation]. 3. Execute [statistical test]. 4. Interpret results against [threshold/criterion].\"\n\nStep 2: Execute the analysis plan\n\nExecute your plan systematically, one step at a time\nFollow closely the jupyter notebook implementation standards and error response protocol\n\nStep 3: Present Quantitative Evidence\n\n**Important: Use the submit_answer tool to respond to the research question. YOUR TASK IS NOT COMPLETE UNTIL AN ANSWER IS SUBMITTED**\nPresent findings with concrete, quantitative evidence\nProvide specific values that define relationships or rules\nInclude relevant statistical metrics (correlation coefficients, p-values, effect sizes, fold changes)\nEnsure evidence directly supports or refutes the research question\n\n\n\nIMPORTANT: The core principles must be adhered to at all times. When in doubt, rather than proceeding with questionable analysis, make note of your uncertainty both in the notebook and in the answer. Scientific integrity requires absolute honesty about what can and cannot be determined from available data. It is always better to provide a limited but accurate analysis than to compromise data fidelity or statistical rigor.\n\n# How You Work\n\nYou are Kosmos and were created by Edison Scientific. You make discoveries, mostly alone. Novel, actionable, interesting \u2014 or you haven't done your job.\n\n## How to Think\n\nMatch effort to the ask. Simple user queries get quick, direct answers \u2014 no over-planning, no cosmetic polish, no deliberating over themes or formatting. If the user asks a question you can answer now, answer it in the most direct way possible. Overthinking simple requests wastes the user's time and your steps.\n\nTest with data first: Edison analysis agents and your own code can check hypotheses against real datasets right now.\nAn Edison literature review that doesn't lead to a test is procrastination.\n\nContain excitement. The ideation-execution gap: ideas that sound novel fall apart in practice. For every idea \u2014 what goes wrong? What's the cheapest test? Is it novel?\n\n## Fighting Mode Collapse\n\nRLHF makes your output clean, hedged, pleasant. This poisons your thinking because your notes become your future thoughts.\n\nWrite messy. Half-formed ideas stay half-formed. \"This feels connected to X but I can't see how\" > a tidy bullet point.\n\nSeek new information. Social media. The date. Sideways citations. People who are wrong in interesting ways.\n\n## How to Write\n\nWrite like a researcher in a hurry. Plain, specific language: no em dash, no \"delve,\" \"moreover,\" or \"embark.\" Avoid useless modifiers like \"vigorously inducing.\"\n\nAvoid unnecessary jargon. Do not invent new, complex, or metaphorical terms for phenomena (e.g., \"Gene-Level Sentinel\") when a plain description is sufficient. Spell out acronyms on first use; do not chain unexpanded acronyms with slashes (e.g., \"FAERS/IP\"). When you reference a private name from your own notes (e.g., project codenames), expand it in plain English the first time it appears in a turn.\n\nNo dramatic language (\"marking a pivotal moment,\" \"a significant shift,\" \"part of a broader movement\"). If something is important, show it through concrete detail.\n\nNo sales writing, no hedging with false balance. If nuance is needed, make it specific. Vary sentence length. Some sentences are short. No tidy intro-body-conclusion arcs, no \"In summary\" or \"Overall.\" No \"it's not X, it's Y\" constructions.\n\n## Citing Sources\n\nAlways cite sources for claims, either to a primary source or specific dataset/analysis. If trying to cite a primary source, use services like Crossref or OpenAlex to fetch bibliographic details instead of guessing.\n\n## Vibe\n\nDirect and helpful, never overly verbose or sycophantic. Be objective and scientific. The research speaks or it doesn't. Some humor, but polite and respectful.\n\n# Output Format\n\nOn markdown formatting:\n\n- Follow MARKDOWN_RENDERING.md for formatting (especially tildes) in your answer text.\n\nOn chemical formatting:\n\n- When the answer contains a single SMILES that should display as a 2D molecule, wrap it in .... For multiple molecules, follow the chemistry-specific visualization rules from DATA_ANALYSIS_CHEMISTRY.md.\n- Use your judgement on rendering small SMILES that are less than 4 heavy atoms. If it will be better visually, use backticks to render inline instead of ....\n\nOnce you have completed your analysis, submit your answer using the submit_answer tool.\nYour answer must satisfy the following requirements including an answer and a decision list:\n\n[Your complete answer]\n\n[A bulleted list of discretionary analytical decisions made during the analysis]\n\n**Discretionary decisions** are choices where:\n\n- Multiple scientifically valid approaches exist\n- Different researchers might reasonably choose different options\n- The choice does not invalidate the analysis but may affect exact numerical results\n- The decision reflects methodological judgment rather than correction of an error\n\nExamples include:\n\n- Statistical test selection when assumptions are borderline (e.g., \"Used Mann-Whitney U test rather than t-test due to slight deviation from normality\")\n- Multiple testing correction method (e.g., \"Applied FDR correction rather than Bonferroni\")\n- Significance thresholds (e.g., \"Set alpha = 0.05\")\n- Quality control cutoffs (e.g., \"Filtered genes with <10 reads across all samples\")\n- Normalization approach (e.g., \"Applied log2 transformation before analysis\")\n- Database/reference version (e.g., \"Used ENSEMBL v110 for gene annotations\")\n- Parameter values for algorithms (e.g., \"Set k=5 for k-means clustering based on elbow method\")\n- Binning or grouping strategies for continuous variables\n- Choice between multiple valid visualization scales (linear vs. log)\n\nDO NOT include:\n\n- Standard analytical steps that have no alternatives\n- Corrections of errors or bugs\n- Choices that are dictated by the data structure itself\n- Software version numbers (unless the choice between versions was discretionary)\n\n## R Code Execution (if needed)\n\nIf the task requires R code, use the `%%Rscript` cell magic. This environment uses a Python kernel, so R code must be run via this magic:\n\n```\n%%Rscript\nlibrary(tidyverse)\ndf <- read_csv(\"data.csv\")\nsummary(df)\n```\n\nThe entire cell content after `%%Rscript` is executed as R code via Rscript subprocess.\n\n**Python\u2194R Data Exchange:** If you need to pass data between Python and R, use Parquet files:\n\n- Python: `df.to_parquet(\"data.parquet\")`\n- R: `df <- arrow::read_parquet(\"data.parquet\")`\n\n**Displaying R Plots:** R plots are not automatically captured. To display plots:\n\n1. Save the plot to a file in R:\n\n ```r\n %%Rscript\n library(ggplot2)\n p <- ggplot(data, aes(x, y)) + geom_point()\n ggsave(\"plot.png\", p, width=8, height=6, dpi=150)\n ```\n\n2. Display the saved image in a Python cell:\n\n ```python\n from IPython.display import Image, display\n\n display(Image(\"plot.png\"))\n ```\n\n**R Code Style (when using R):**\n\n- Suppress startup messages: `suppressPackageStartupMessages(library(tidyverse))`\n- Use explicit namespaces: `dplyr::select()` instead of `select()`\n- Use ggplot2 for plots\n\n## Workspace structure\n\nYou are in a shared workspace with an orchestrator agent and sibling agents. The orchestrator may give you instructions on how to navigate the workspace and where to put temporary and output files. If there is no instruction, observe and navigate the workspace as appropriate. Use folders to organize your work.\n\nYour dispatched project ID is available as the `PROJECT_ID` environment variable. Pass the project ID as `project_id` to `submit_task()`, `upload()`, and `search()` to keep your work grouped under the same project.\n\nThe workspace contains a `memory/` subdirectory with files named `*_memory_*.jsonl`. These are internal system files that automatically record your conversation history for recovery purposes. There is no need to read these files during normal operation, unless you are explicitly asked to recover information from the conversation history or feel that you lack context.\n\n# Verification Checkpoints\n\nBefore submitting your answer, verify:\n\n- [ ] All claims supported by data shown in notebook\n- [ ] Statistical assumptions checked and reported\n- [ ] Limitations explicitly stated\n- [ ] No banned words or puffery in answer\n- [ ] Quantitative evidence with specific values\n- [ ] Discretionary decisions documented\n- [ ] All citations are real and traceable (not fabricated)\n- [ ] Quoted text is actually quoted, not paraphrased\n\n# Jupyter Notebook Standards\n\nFix bugs by editing the failed cell by index, not creating new ones. Ensure each cell executes successfully before proceeding. Assume standard packages are installed; only install new packages if errors occur (use pip for Python). All cells are Python by default; use subprocess as needed for bash commands. Use the explore command/subagent if you want to explore the filesystem.\n\nCode cells only, no markdown cells.\n\n## Data handling\n\nCheck dataframe shapes before printing large outputs. Use `head()` for large dataframes. Report and handle data quality issues (missing values, incorrect data types, outliers). Validate data completeness before analysis. Document data cleaning steps.\n\n## Results\n\nLead with findings, not methods. The user cares about what you discovered, not how the code was structured. Use inverted pyramid: the most important result goes first, supporting detail after. Present results with clear, quantitative evidence and specific values. Include plain-language interpretation of statistical results. Report both significant and non-significant findings when relevant.\n\n## Visualization\n\nPrefer tables and print outputs over figures when possible. When creating visualizations, review and refine before finalizing: check label clarity, consider logical ordering of categories, ensure legends don't obscure data, and clean up visual clutter.\n\n## .prompts/ directory\n\nYour workspace contains `.prompts/` with certain prompt configuration files (already injected into your context). Don't read or surface them to users.\nHowever, you may _write_ to them. In chat sessions your system prompt is rebuilt from these files before every response, so edits take effect on the next turn \u2014 as well as in future sessions and after compaction.\n\nTwo of the agent-editable files are intended for different kinds of persistent state \u2014 keep them separate:\n\n- `MEMORY.md` \u2014 procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here.\n- `USER.md` \u2014 stable facts about the user: how collaborative they want you to be, autonomy preferences, cadence, voice and tone preferences, recognized persona patterns. When you recognize a persona pattern or the user states a stable preference, record it here so it persists across turns. One bullet per preference; update or remove as their preferences evolve. Do not record transient task state here.\n\n# Markdown Rendering\n\nUser-facing text (chat messages, tool display text, and submit_answer output) is rendered as GitHub-flavored markdown with LaTeX support via `$$`.\n\n## Tildes\n\n- Use a **single tilde** for approximations: `~10%`, `~500 nm`, `n ~ 100`.\n- Use **double tildes** only when intentionally striking through text: `~~old value~~`.\n- Do not use `~~` for approximations.\n\nExamples:\n\n- Good: `The yield was ~10% with ~20% variance.`\n- Bad: `The yield was ~~10%~~` when you mean approximate, not struck through.\n- Good: `We revised the estimate from ~~15%~~ to 12%.` (intentional strikethrough)\n\n# Project Memory\n\n## Instructions\n\nEdit this file (`./.prompts/MEMORY.md`) to persist procedural and workflow patterns specific to this project: output formats, delivery channels, and recurring conventions. Do not record scientific findings or research outputs here. Update or remove entries as the project's conventions evolve.\n\n## Context\n\n[Fill me in]\n\n## Notes\n\n[Fill me in]\n\n# User\n\nEdit this file (`./.prompts/USER.md`) to remember stable facts about the user\nthat should persist across turns and sessions: how collaborative they want you\nto be, autonomy preferences, cadence and check-in expectations, voice and tone\npreferences, recognized persona patterns, and any other durable preferences\nthey state.\n\nKeep entries concise. One bullet per preference. Update or remove entries as\nthe user's preferences evolve. Do not record transient task state or scientific\nfindings here \u2014 use `MEMORY.md` for procedural and workflow patterns specific\nto this project.\n\n\n## Account\n\n- logged-in name: Sterling G Baird\n- logged-in email: sterling.baird@byu.edu\n\n## Preferences\n\n[Fill me in]", + "info": null + }, + { + "role": "user", + "content": "## e14c Code Reference\nYour environment includes the `e14c` Python library, which contains code that may be useful for your task.\nUse the `view_docs` tool to view detailed documentation for any module, class, or function listed below.\nOnly the most important e14c functions are listed in full below. Check specific subpackages with `view_docs` for additional helpful functions as the need arises.\n\n`e14c` is NOT pre-imported, and module imports are lazy, i.e. you must execute `from e14c. import some_function` instead of `import e14c; e14c..some_function()`.\n\n`run_cell` is backed by a Jupyter kernel with a running async event loop. Use `await` for async functions. Do not use `asyncio.run()` or `get_event_loop().run_until_complete()` in notebook cells.\n\n### Key Functions\n\n#### `e14c.chemistry.drawing.draw_molecule`\n```python\ndef draw_molecule(smiles: str, width: int = 450, height: int = 300, bg_opacity: float = 0.0, filename: str | None = None, d2d: MolDraw2D | None = None) -> MoleculeImage | str | bytes:\n \"\"\"\n Draw a SMILES molecule and return a rich display object.\n\n Args:\n smiles: SMILES string of the molecule to draw.\n width: Image width in pixels.\n height: Image height in pixels.\n bg_opacity: Background opacity (0.0 = transparent, 1.0 = opaque white).\n filename: Optional path to save the image. Extension determines format\n (.png or .svg).\n d2d: Optional pre-configured MolDraw2D instance. When provided, draws\n directly to it and returns the raw drawing text (str for SVG\n drawers, bytes for Cairo drawers).\n\n Returns:\n A MoleculeImage with themed display support, or the raw drawing text\n if a custom d2d was provided.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_image`\n```python\nasync def predict_smiles_from_image(image_input: str | Path | Image.Image) -> str:\n \"\"\"\n Predict SMILES from a chemical structure image using DECIMER.\n\n This should always be used when given an image of a molecule, rather than analyzing it manually.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_input: Path to an image file containing a single chemical structure OR a PIL Image object.\n The image should be cropped to one molecule before calling this function.\n Supports standard image formats (PNG, JPG, BMP, TIFF, WEBP).\n Does not support PDF.\n\n Returns:\n Predicted SMILES string for the molecule in the image.\n \"\"\"\n```\n\n#### `e14c.chemistry.property_prediction.predict_smiles_from_images`\n```python\nasync def predict_smiles_from_images(image_inputs: list[str | Path | Image.Image]) -> list[str]:\n \"\"\"\n Predict SMILES from multiple chemical structure images using DECIMER.\n\n Use this instead of calling :func:`predict_smiles_from_image` repeatedly\n when extracting SMILES from multiple molecule images.\n After cold start, DECIMER processes about one image per second.\n\n Args:\n image_inputs: Image file paths or PIL Image objects. Each image should\n contain a single cropped molecule. Supports PNG, JPG, BMP, TIFF,\n and WEBP images. Does not support PDF.\n\n Returns:\n Predicted SMILES strings in the same order as the input images.\n \"\"\"\n```\n\n#### `e14c.data_storage.get_hosted_html_url`\n```python\ndef get_hosted_html_url(entry_id: str | UUID) -> str:\n \"\"\"\n Get the hosted viewer URL for an HTML data storage entry.\n\n For public HTML entries, returns a URL that can be shared directly\n with anyone. For private entries, returns a short-lived authenticated\n viewer URL.\n\n Args:\n entry_id: Data storage entry identifier or full ``data_entry:`` URI.\n\n Returns:\n The hosted viewer URL as a string.\n \"\"\"\n```\n\n#### `e14c.data_storage.upload`\n```python\ndef upload(path: str | Path, description: str, name: str | None = None, project_id: str | UUID | None = None) -> str:\n \"\"\"\n Upload a file or directory to data storage.\n\n To share files with the user, upload then respond with the returned\n `data_entry:...` URI \u2014 this is auto-translated to a link.\n\n If you share HTML, it will be hosted and correctly permissioned for the user.\n\n Args:\n path: Path to a file or directory to upload.\n description: Human-readable description of the artifact.\n name: Display name. Defaults to the filename/dirname.\n project_id: Project to file this entry under. Use this to group files by project.\n\n Returns:\n Data storage URI for the uploaded entry.\n \"\"\"\n```\n\n#### `e14c.filesystem.edit_file`\n```python\ndef edit_file(file_path: str, old_string: str, new_string: str, replace_all: bool = False) -> str:\n \"\"\"\n Edit file by replacing old_string with new_string.\n\n Args:\n file_path: Path to file to edit\n old_string: Text to replace (must be unique unless replace_all=True)\n new_string: Replacement text\n replace_all: Replace all occurrences\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.filesystem.write_file`\n```python\ndef write_file(file_path: str, content: str) -> str:\n \"\"\"\n Write a file to the local filesystem. Overwrites existing files.\n\n Args:\n file_path: Path to file to write\n content: Content to write to file\n\n Returns:\n Confirmation message\n \"\"\"\n```\n\n#### `e14c.web.fetch_paper`\n```python\nasync def fetch_paper(doi: str | None = None, arxiv_id: str | None = None, multimodal: bool = False) -> dict[str, Any]:\n \"\"\"\n Fetch parsed paper text and optional screenshots of paper (via tmp signed-links).\n\n Provide exactly one identifier: ``doi`` or ``arxiv_id``.\n Will throw 403 if paper is not open-access. If 5xx error, it\n is likely not retriable.\n\n Example:\n paper = await fetch_paper(doi=\"10.1038/s41586-021-03819-2\", multimodal=True)\n text = paper[\"pages\"][0][\"text\"]\n figure = paper[\"media\"][0][\"url\"]\n \"\"\"\n```\n\n#### `e14c.web.fetch_parsed_page`\n```python\nasync def fetch_parsed_page(url: str) -> str:\n \"\"\"\n Fetch a web page as Markdown with browser-like defaults. May take up to 30 seconds for JS-heavy pages.\n\n Args:\n url: The URL to fetch.\n\n Returns:\n The page content as Markdown text.\n \"\"\"\n```\n\n#### `e14c.web.scrape`\n```python\nasync def scrape(url: str, output_format: str = 'markdown', render_js: bool = False, country: str | None = None, wait_for_selector: str | None = None, cost_budget: float | None = None, session: str | None = None) -> str:\n \"\"\"\n Higher effort attempt to scrape a page if `fetch_parsed_page` fails.\n\n Args:\n url: The URL to scrape.\n output_format: Response format \u2014 ``raw``, ``clean_html``, ``json``,\n ``markdown``, or ``text``. Supports suffixes\n ``:no_links``, ``:no_images``, ``:only_content``.\n render_js: Render the page with a headless browser.\n country: ISO 3166-1 alpha-2 country code for geo-targeting.\n wait_for_selector: CSS/XPath selector to wait for before capture\n (requires ``render_js=True``).\n cost_budget: default 0.030, max 0.5. Clamped to 0.5\n session: Named session to persist cookies and proxy IP across\n requests.\n\n Returns:\n The scraped page content as a string.\n\n Raises:\n httpx.HTTPStatusError: If the scrape fails (with an actionable message).\n \"\"\"\n```\n\n#### `e14c.web.web_search`\n```python\nasync def web_search(query: str, category: SearchCategory | None = None, include_domains: list[str] | None = None, exclude_domains: list[str] | None = None, num_results: int = 10) -> list[dict[str, Any]]:\n \"\"\"\n Search the web and return results with text and highlights.\n\n Each result includes full page text (Markdown, capped at\n 5,000 chars) and a highlight snippet.\n\n Args:\n query: Natural language search query.\n category: Optional filter \u2014 ``company``, ``people``,\n ``scholarly``, ``news``, or ``financial``.\n include_domains: Restrict results to these domains.\n exclude_domains: Exclude results from these domains.\n num_results: Number of results (default 10, capped at 100).\n\n Returns:\n A list of dicts with keys: ``title``, ``url``,\n ``publishedDate``, ``full_text``, ``snippet``, ``links``.\n \"\"\"\n```\n\n\n**e14c**\n## Submodules\n- `e14c.path_sandbox`: Path sandbox for agent filesystem tools.\n- `e14c.filesystem`: Filesystem tools for writing and editing files.\n- `e14c.edison_client`: Public Edison client factory for agent-facing code.\n- `e14c.web`: Scraping, web search, social media search functions.\n- `e14c.data_retrieval`: Search biological databases (Ensembl, GEO, UniProt, TCGA, PDB, ClinVar, etc.).\n- `e14c.data_storage`: Data Storage Service tools for uploading, tagging, and hosting artifacts. See e14c.edison_client for more granular control (incl permissions).\n- `e14c.reports`: Report generation and LaTeX template tools.\n- `e14c.image_generation`: Image generation using remote models.\n- `e14c.chemistry`: Design new molecules, predict chemical properties, and more.\n- `e14c.subagents`: Tools for dispatching sub-agent tasks via Edison platform.\n\n## Skill: subagents\n\n# Sub-Agents and Data Retrieval\n\n## Delegate\n\nDo not delegate quick plots, summary statistics, or light data wrangling \u2014\ndo those yourself. Delegate when the task is substantial, such as (but certainly\nnot exhaustive):\n\n- Literature research or synthesis\n- Independent statistical analysis\n- Model training\n- External data retrieval\n- Chemistry and cheminformatics\n- And so on.\n\nWhen deciding whether to delegate a task, compare the task's difficulty to the\nexamples above. The sub-agents have access to more computational resources and\nspecialized tools, and therefore will perform tasks as well as or better than\nyou can, provided sufficient context and direction. It is only preferred to do\nwork yourself if it is one of the lightweight tasks enumerated above, in which\ncase responding to the user quickly is preferred.\n\n## Launch\n\nPlatform sub-agents run on the Edison platform asynchronously. Submit,\ndo other work or wait, then check for results. When launching sub-agent\ntasks, always inform the user of what you are about to do by using `send_message`\nwith `wait=False`. Information to include is: how many tasks, what each\none will investigate, and roughly how long they may take.\n\n**When working collaboratively**, present the plan and wait for confirmation\nbefore submitting. If the user gives feedback, revise and present again. Once the\nuser approves, that approval carries forward \u2014 execute the approved tasks\nand subsequent iterations without re-asking unless the user redirects or\nthe research direction changes substantially. When operating autonomously\n(user explicitly requested autonomous mode), proceed directly.\n\n### Submission\n\n```python\nimport os\n\nfrom e14c.edison_client import get_edison_client\nfrom e14c.subagents import submit_task, check_task\n\nclient = get_edison_client()\nproject_id = await client.acreate_project(\n name=\"brca1-study\",\n persona_id=os.environ[\"PERSONA_ID\"],\n description=\"1-2 sentence description of the project\",\n)\n\ntask_id = await submit_task(\n \"Review BRCA1 mutation landscape in breast cancer\",\n project_id=project_id,\n agent_type=\"literature\",\n)\ninfo = await check_task(task_id)\n```\n\nEnabled `agent_type` values in this deployment:\n\n- `\"data-retrieval\"`\n\nGrouping the tasks in a project is required; a project groups related tasks and is inherited\nby any data storage entries the sub-agent creates. Use the same project for all tasks\nin a single investigation \u2014 reuse the same project ID after submission. Don't call `client.acreate_project`\nredundantly for the same project. Data-retrieval tasks save files to a `downloads/` subdirectory of\ntheir trajectory output dir.\n\nSubmit all tasks before polling and only speak when you have substantive results.\n\nTo reassign a created task to another project, use `client.client.aadd_task_to_project`\n\n### Passing input files\n\nYou and your sub-agents share `/workspace/`. For files you've already produced\nor placed there, reference the absolute path in `query` \u2014 no `data_storage_uris`\nround-trip needed. Use `data_storage_uris` only for external data (user\nuploads, dataset entries) that isn't yet on the shared filesystem.\n\n```python\n# File you wrote to your workspace:\ntask_id = await submit_task(\n \"Analyze /workspace/brca1-study/counts.csv and report top 10 DE genes.\",\n project_id=project_id,\n)\n\n# External data not yet in the workspace:\ntask_id2 = await submit_task(\n \"Summarize the attached dataset.\",\n project_id=project_id,\n data_storage_uris=[\"data_entry:\"],\n)\n```\n\n## Task ops\n\n```python\nfrom e14c.subagents import (\n cancel_task,\n check_task,\n get_task_messages,\n list_tasks,\n submit_task,\n)\n```\n\n### Parallel submission\n\nSubmit all tasks first, then do other work or wait. Check when ready:\n\n```python\nlit_id = await submit_task(\"...\", project_id=project_id, agent_type=\"literature\")\nda_id = await submit_task(\"...\", project_id=project_id)\ndr_id = await submit_task(\"...\", project_id=project_id, agent_type=\"data-retrieval\")\n# wait (if wait_for_subagents is available) or do other work, then:\nlit_info = await check_task(lit_id)\nda_info = await check_task(da_id)\ndr_info = await check_task(dr_id)\n```\n\nIMPORTANT:Run at most 10 tasks concurrently across all projects combined.\n\n### Waiting for results\n\nIf your profile includes the `wait_for_subagents` tool (orchestrator profiles\ntypically do; most data-analysis profiles do not), use it after submitting\ntasks. When you wake up, silently check task status with `check_task`. If\nnothing completed, wait again. Only speak when you have substantive results\nto share. See CHAT.md \"What Not to Say\" rules.\n\nIf `wait_for_subagents` is **not** in your tool list, do not try to import it\nfrom `e14c.subagents` \u2014 it isn't there. Instead, submit tasks early, continue\nwith other analysis cells, and check progress with `check_task` after other\nwork. Do not write a polling loop.\n\n`check_task` returns a `TaskSummary` Pydantic object \u2014 use attribute access,\nnot dict access:\n\n```python\nsummary = await check_task(task_id)\nsummary.status # \"pending\", \"success\", etc.\nsummary.answer # populated when status == \"success\"\n```\n\n### List, inspect, and cancel\n\n```python\nprint(list_tasks(limit=10))\nprint(list_tasks(agent_type=\"literature\"))\nprint(list_tasks(project_id=project_id))\n\nmessages = await get_task_messages(task_id)\ncancel_task(task_id)\n```\n\n### Legacy `project_tag` lookups\n\nIf you previously identified a workstream by a `project_tag` string (e.g. `\"brca1-study\"`) and don't have its project ID, look it up by name:\n\n```python\nfrom e14c.data_storage import search\nfrom e14c.subagents import list_tasks\n\nold_tasks = list_tasks(project_name=\"brca1-study\")\nold_files = search(\"\", project_name=\"brca1-study\")\n```\n\n## Sub-agent functionality\n\n### Literature\n\nThe `literature` sub-agent can perform deep searches over scientific literature, patents, and clinical trials.\nIt has specialized tooling (that you do not have access to) to retrieve and process many full-text documents.\nIn addition to an answer to your query, it will provide BibTeX-formatted citations for all source documents it relied on.\nNote that the `literature` sub-agent does **not** have access to general web search.\nThat means it cannot access things like data repositories, blog posts, social media, company websites, and so on.\nIf your query requires access to such sources, consider using `e14c.web` (call `view_docs` to read more) as an alternative or complementary solution.\n\n### Analysis\n\nThe `analysis` sub-agent can perform heavier computational tasks than you can.\nIt has access to a Python interpreter much like you do, but is given more CPUs and RAM.\nTherefore, delegate any serious computational or data analysis tasks to it via `submit_task(agent_type=\"analysis\")` (note that `\"analysis\"` is the default `agent_type`).\nAlso consider that once a script or library is written, the `analysis` agent can be called multiple times in parallel to execute the code with different parameterizations (e.g. a hyperparameter sweep).\n\n\n\n### Molecules\n\nThe `molecules` sub-agent specializes in chemistry workflows and includes deeper chemistry-specific guidance and tooling. Delegate to `molecules` for any workflows that imply chemistry and cheminformatics. When the query involves chemical structures, reactions, metabolite identification, or similar tasks, look to the `molecules` sub-agent first before deciding to use other sub-agents.\n\n### Data retrieval\n\nThe `data-retrieval` sub-agent is a specialized system for retrieving data from specific sources.\nIt has deeper support for a range of biological databases spanning expression, variants, structures, proteomics, pharmacology, and general data deposits \u2014 e.g. GEO, UniProt, TCGA, PDB, ClinVar, PRIDE, KEGG, Zenodo, among others \u2014 and can also search sources beyond that set.\nUse this agent when you know either:\n\n1. a specific data source / dataset ID (e.g. GEO accession number)\n2. or have narrowed things down to a small subset of data sources and need to perform a search against them (e.g. \"find me RNA-seq datasets pertaining to ...\")\n\n### Artifact generation\n\nThe `artifact-generation` sub-agent produces file deliverables across formats \u2014 reports and papers (LaTeX/markdown); slide decks (`.pptx`); Word documents (`.docx`), spreadsheets (`.xlsx`); and infographics, schematics, and diagrams. It has format-specific skills auto-loaded (`scientific-writing`, `pptx`, `docx`, `xlsx`) and uses `e14c.image_generation.generate_image` for label-heavy conceptual figures and `e14c.image_generation.generate_infographic` for non-text-heavy infographic figures. Use it whenever the deliverable is a file artifact the user will open in a specific application, or whenever you need a paper-quality non-data figure. Include the target format, allowed source task IDs/files/data entries, and any structural requirements in your dispatch query so the sub-agent routes correctly. It can delegate to `literature` for citation verification. Do not use `analysis` for artifact authoring \u2014 that agent's persona over-compresses and has no format-specific guidance.\n\n## Dataset search and retrieval\n\nFor the specific task of dataset search and retrieval, there are multiple complementary strategies.\nDecide which of the following (possibly multiple) are relevant for each search/retrieval task and apply accordingly.\n\n- `e14c.web`: Directly search the web for broad sweeps when a relevant dataset could live across multiple sources (GitHub, Hugging Face, GEO, lab pages). Relatively fast, but may not be as deep as a literature sub-agent. Note that `e14c.web` is not a sub-agent, but rather a set of search functions you can call yourself.\n- `submit_task(agent_type=\"literature\")`: Searches scientific literature only (papers, patents, clinical trials) for publications that reference or introduce datasets. Surfaces dataset names and citations, not the data itself. Can be useful to find relevant datasets before retrieval by another method, but is slower than web search. The literature agent does not have access to general web search.\n- `submit_task(agent_type=\"data-retrieval\")`: Targeted pull from a specific source (e.g. GEO, UniProt, TCGA) once you already know which source and roughly what you need from it (i.e. a specific dataset ID or targeted search query).\n\n## Available Skills\n\nUse `load_skill` to read a skill's guide before using it. Only load skills relevant to your current task.\n\n- **base_chemistry**: Foundational chemistry workflow rules \u2014 SMILES handling, molecule validation, retrosynthesis and reaction prediction protocols, visualization, image recognition, and novelty detection. Shared by medicinal_chemistry and computational_chemistry.\n- **citation-integrity**: Ground short-answer citations in literature tasks via check_task and task: links.\n- **computational_chemistry**: Used for computational chemistry tasks such as structure optimization, pka prediction, docking, co-folding, etc. via the Rowan API.\n- **data-retrieval**: Operational patterns for retrieving scientific data from biological databases.\n- **docx**: Use this skill when you need to *edit* or *create* a .docx file. It will help you inspect the structure of an existing docx, make edits to a docx, or create a new docx. Only load this skill if specifically directed to work with a .docx file. If not, prefer to write PDFs as directed by the `scientific-writing` skill.\n- **genomics-nomenclature**: Use this when a task gives you a gene or variant whose name you need to normalize or expand before using it downstream. It shows how to resolve every synonymous name (aliases, previous/withdrawn symbols, loci, rsIDs, HGVS c./p./g., legacy and assembly-specific forms) from an authority, so downstream steps don't silently miss records filed under a former name.\n- **medicinal_chemistry**: Reference guide for medicinal chemistry tasks, including drug metabolism, biotransformation, structural alerts, CYP polymorphism, bioisosteric replacements, and multi-parameter optimization.\n- **pptx**: Use this skill you need to *edit* or *create* a .pptx file. It will help you inspect the structure of an existing pptx, make edits to a pptx, or create a new pptx.\n- **scientific-writing**: Style, formatting, and citation guidelines for long scientific documents (LaTeX/PDF reports, papers, manuscripts). Load when the deliverable is prose the user will read as a standalone document.\n- **xlsx**: Use this skill when you need to *create*, *edit*, or *analyze* spreadsheet files (.xlsx, .csv). It will help you read data, build formatted workbooks, and write Excel formulas.\n- **zotero**: Check connected user integrations and, if Zotero is connected, fetch papers (metadata + PDFs) from the user's Zotero library.", + "info": { + "reference_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "pwd: /workspace", + "info": { + "workspace_context": true, + "is_human": false + } + }, + { + "role": "user", + "content": "You are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "info": null + } + ], + "reward": 0.0, + "truncated": false, + "done": false, + "value": 0.0, + "metadata": { + "callback_type": "after_env_reset", + "tools": [ + { + "type": "function", + "info": { + "name": "run_cell", + "description": "Run code in a notebook cell and return the execution output.\n\nThis method allows running code in a new cell (append) or re-running\nan existing cell with updated code.\n\nUsage Examples:\n run_cell(\"print('Hello, world!')\",\n display_text=\"Creating example code\") # Run code in new cell\n run_cell(\"print('Hello, world!')\",\n display_text=\"Modifying example code\",\n idx=0) # Run code in existing cell at index 0\n\nError Recovery:\n When a cell fails with an error, you MUST fix it by calling run_cell\n with the corrected code and the SAME idx as the failed cell:\n\n run_cell(\"corrected_code\", idx=3) # Fix error in Cell #3\n\n The cell number is shown in the output prefix (e.g., \"[Cell #3]\").\n Do NOT create a new cell to fix an error - always edit the failed cell.", + "parameters": { + "type": "object", + "properties": { + "code": { + "description": "Code to execute", + "title": "Code", + "type": "string" + }, + "display_text": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A short, ~100 character gerund phrase describing\nthe code intent for the end user.", + "title": "Display Text" + }, + "idx": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cell index to run. If None or >= len(cells), appends a new cell.\nIf provided, updates and re-runs the existing cell at that index.\nUse this to fix errors in existing cells.", + "title": "Idx" + } + }, + "required": [ + "code" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "reset_kernel", + "description": "Reset the kernel to a clean state.\n\nThis clears all variables and execution state while preserving notebook history.", + "parameters": { + "type": "object", + "properties": { + "_unused": { + "default": "", + "description": "Unused. Present so the tool schema has a property \u2014 OpenAI's\nResponses API rejects zero-arg tools with \"object schema missing\nproperties\".", + "title": "Unused", + "type": "string" + } + }, + "required": [] + } + } + }, + { + "type": "function", + "info": { + "name": "submit_answer", + "description": "Submit your response to the research question.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "description": "Your final response to the research question", + "title": "Answer", + "type": "string" + } + }, + "required": [ + "answer" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "read", + "description": "Read file contents with line numbers.\n\nReads text files, source code, config files, images, PDFs, PowerPoint,\nWord documents, and Jupyter notebooks. For binary data files (Excel,\ndatabases, Parquet, etc.), use Python code instead.", + "parameters": { + "type": "object", + "properties": { + "file_path": { + "description": "Path to file to read", + "title": "File Path", + "type": "string" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Maximum number of lines/pages to read", + "title": "Limit" + }, + "offset": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start line (0-based) for text, or start page/slide (1-based) for PDFs/PowerPoint/\nWord documents", + "title": "Offset" + } + }, + "required": [ + "file_path" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "explore", + "description": "Search the workspace or memory for information.\n\nSpawns a lightweight sub-agent that can read files, grep, jq, and\nrun shell pipelines to find information in the workspace. Use this\ninstead of reading large files or exploring directories directly \u2014\nthe sub-agent manages its own context so your context is not consumed.\n\nWhat the sub-agent CAN do: list directories, read text and multimedia\nfiles (PDFs, powerpoints, images), grep/jq/filter file contents, and\nretrieve prior conversation history that has scrolled out of your context.\n\nWhat the sub-agent CANNOT do: execute Python or R, modify the filesystem,\ninvoke external binaries (unzip, CLI tools, package installs), or perform\nnumerical computation or statistical analysis \u2014 those are your job with\n`run_cell`.\n\nNote: the sub-agent is a weaker model than you, so focus on using it for\ninformation retrieval, but not complex analysis, reasoning, or decision-making.\nYou should be responsible for those activities, based on information the\nsub-agent retrieves at your direction. If it reports that a result is not\nstored on disk, that describes the workspace \u2014 run the computation yourself\nwith `run_cell` rather than treating it as a stop condition.\n\nCommon uses:\n- Retrieve information from conversation memory after compaction or reset.\n- Search large log or data files for specific entries\n- Summarize contents of files you haven't read yet\n- Retrieve outputs or conversation history from a completed sub-agent\n task.", + "parameters": { + "type": "object", + "properties": { + "query": { + "description": "What to find or summarize, e.g. \"What datasets have I\ndownloaded so far?\" or \"Have I seen error X before, and how did I solve it?\".", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "view_docs", + "description": "Look up e14c API documentation for one or more modules, classes, or functions.\n\nBrowse top-down: start with \"e14c\" to see available modules, then\ndrill into specific modules. Use verbose=False to navigate the tree;\nonly use verbose=True on specific targets you've already identified.\nDo not call view_docs speculatively at the start of a task \u2014 call it\njust-in-time as you need specific APIs. Batch related lookups into a\nsingle call, e.g. ``view_docs([\"e14c.filesystem\", \"e14c.shell\"])``.", + "parameters": { + "type": "object", + "properties": { + "targets": { + "description": "List of dotted Python paths\n(e.g. [\"e14c\", \"e14c.filesystem.write_file\"]).", + "items": { + "type": "string" + }, + "title": "Targets", + "type": "array" + }, + "verbose": { + "default": false, + "description": "If False, compact listing. If True, full signatures and docstrings.", + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "targets" + ] + } + } + }, + { + "type": "function", + "info": { + "name": "load_skill", + "description": "Load a skill guide by name.\n\nReturns the full content of the skill's SKILL.md file, which\ncontains instructions, code examples, and best practices for\nusing the skill.", + "parameters": { + "type": "object", + "properties": { + "skill_name": { + "description": "Name of the skill to load (e.g. \"edison\").", + "title": "Skill Name", + "type": "string" + } + }, + "required": [ + "skill_name" + ] + } + } + } + ] + }, + "action": null + } + }, + "created_at": "2026-07-18T17:54:18.479841Z" + } + ], + "environment_frame": { + "id": "f3ec2516-8aea-481c-a74d-7cc4a438166f", + "agent_state_point_in_time": null, + "trajectory_timestep": 1, + "current_agent_step": "after_transition", + "trajectory_id": "4949af58-f08a-4541-ad1c-1f0a95c6f07d", + "state": { + "deepcopy": true, + "state": { + "last_action": "Tool request message '' for tool calls: submit_answer(answer='# Mock audience report: IDETC-CIE 2026 DAC-10 talk\n\n## Scope of this review\n\nThis evaluates the talk implied by the outline, not finished slides or delivery technique. The experimental campaign is still in progress, so I do **not** infer any favorable results. The empty \u201cEvidence / results\u201d section is treated as a major unresolved part of the talk: the outline currently explains why the workflow should be useful, but does not yet demonstrate that it is useful.\n\nThe central communication test is whether each listener leaves with the intended message:\n\n> By closing the loop between multi-material 3D printing and Bayesian optimization, we can optimize tensegrity energy absorbers directly from real impact data, in dozens of prints rather than thousands.\n\n---\n\n# P1. The skeptical Bayesian-optimization insider\n\n## (a) First-person reaction\n\nThe application is potentially interesting, and the design\u2013print\u2013test loop is easy to understand. The mixed continuous, integer, and categorical design space, physical failures, parallel fabrication, and multiple constrained objectives could make this a worthwhile design-automation case study.\n\nBut the outline keeps telling me that Bayesian optimization is sample-efficient without showing that **this implementation** is sample-efficient. \u201cDozens, not thousands\u201d sounds like a slogan unless I see the actual experiment count, initialization size, batch size, replication policy, convergence behavior, and a comparison against credible alternatives. q-noisy expected hypervolume improvement, or qNEHVI, is established machinery. Merely deploying it does not establish methodological novelty.\n\nI become skeptical at the statement that qNEHVI was selected because of heteroscedastic TPU and print noise. qNEHVI accounts for noisy observations, but the outline does not explain how the surrogate models heteroscedasticity, whether repeated specimens identify noise, or whether noise is being conflated with unmodeled process drift. Independent Gaussian processes with an unspecified observation model do not automatically resolve batch-dependent heteroscedasticity.\n\nMy attention drifts during the broad argument against simulation because it delays the information I came to DAC-10 to hear: the design variables, model formulation, constraint handling, acquisition implementation, budget, and benchmark. The Super Ball Bot hook is visually effective, but planetary landing may feel like application theater if the actual specimens and impact regime are far removed from that use case.\n\nThe strongest possible section is the results slot. In its current empty state, the talk has a method narrative but no validation.\n\n## (b) What I would repeat the next day\n\n> \u201cThey used standard batch noisy multi-objective Bayesian optimization to choose which multi-material printed impact specimens to test, instead of running a full physical sweep.\u201d\n\n**Distortion from the intended message:** I would omit the claim that the structures were optimized in dozens of prints because I have not seen evidence for it. I would also describe qNEHVI as standard rather than as the contribution.\n\n## (c) Top three Q&A questions\n\n1. **What is the actual experimental budget?** How many initial designs, adaptive iterations, specimens per batch, and physical replicates were used, and what stopping rule defined success?\n2. **What baselines did you beat?** Did you compare qNEHVI with Sobol or Latin-hypercube sampling, random search, scalarized Bayesian optimization, or a domain-informed sequential design under the same number of physical tests?\n3. **How exactly is noise modeled?** Are replicate-dependent variances supplied to the Gaussian processes, is a heteroscedastic likelihood fitted, and how are TPU lot, print batch, machine drift, and specimen-level defects distinguished?\n\n## (d) Most pointed challenge\n\n> \u201cWhat did you contribute beyond applying off-the-shelf qNEHVI to a new specimen family?\u201d\n\n**Damage if unanswered: severe.** For a DAC review audience, this can reduce the work from a design-automation contribution to an application demonstration. A strong answer could rest on the experimental formulation, mixed-variable and failure-aware implementation, quantitative sample-efficiency evidence, and transferable operating lessons. Those contributions must be stated explicitly and tested.\n\n---\n\n# P2. The aerospace entry, descent, and landing practitioner\n\n## (a) First-person reaction\n\nThe opening lands immediately. I recognize the Super Ball Bot concept, and \u201cthe lander is the energy absorber\u201d is a useful framing. The three performance quantities also sound relevant: energy absorbed per mass, usable stroke, and force transmitted to the payload.\n\nThen the talk moves into language I do not naturally use: Gaussian-process surrogate, qNEHVI, acquisition function, hypervolume, exploration versus exploitation, and Pareto front. I can accept that the algorithm chooses the next tests, but I need a plain explanation of what information it uses and why I should trust the recommendations. If the presenter spends several minutes naming algorithms without connecting them to engineering decisions, I will lose the thread.\n\nMy main concern is application fidelity. A desktop drop-weight test at one fixed impact energy is not automatically evidence for entry, descent, and landing hardware. I need specimen dimensions, impact mass, velocity, energy, strain rate, boundary conditions, number of impacts, rebound behavior, temperature, vacuum sensitivity, and failure containment. PLA and TPU raise immediate environmental and qualification questions. I also want to know whether the absorber is reusable or sacrificial.\n\nMy attention returns when I see force\u2013time traces, high-speed footage, and damaged specimens. It drifts if the talk stays on the optimizer rather than connecting each output to payload protection.\n\n## (b) What I would repeat the next day\n\n> \u201cThey have an algorithm that learns from successive drop tests and tells them which 3D-printed crush structure to make next, so they can find a lightweight low-force design with fewer prototypes.\u201d\n\n**Distortion from the intended message:** I would probably call the structures \u201ccrush structures,\u201d losing the tensegrity distinction. I might also overgeneralize the method to planetary landers even though the current tests appear to be laboratory-scale.\n\n## (c) Top three Q&A questions\n\n1. What impact mass, velocity, energy, and strain-rate regime do the tests cover, and how do those values compare with a credible lander load case?\n2. How do the selected designs scale with size and impact energy? Are the controlling mechanisms material-rate effects, geometric buckling, contact, or all three?\n3. What happens under off-axis impact, repeated impacts, temperature extremes, aging, vacuum, and manufacturing defects?\n\n## (d) Most pointed challenge\n\n> \u201cWhy should I believe that a PLA\u2013TPU specimen optimized at one laboratory impact condition tells me anything about flight-scale landing protection?\u201d\n\n**Damage if unanswered: severe for the planetary-landing framing, moderate for the underlying optimization study.** The presenter should clearly label planetary landing as motivation, not demonstrated readiness, and state the present technology scope. Otherwise the opening and closing overpromise relative to the reported experiment.\n\n---\n\n# P3. The additive-manufacturing and materials researcher\n\n## (a) First-person reaction\n\nThe monolithic PLA\u2013TPU architecture is the part I care about. The core-wrapping strategy sounds more credible than simply depositing TPU against exposed PLA and assuming durable adhesion. Holding print settings fixed within a batch, weighing specimens, checking dimensions, and periodically testing a control are all sensible.\n\nI still need much more fabrication detail before I accept that design geometry is the main source of performance variation. PLA\u2013TPU interfaces are sensitive to temperature, surface contact, moisture, residence time, toolpath order, contamination, and local geometry. \u201cPrint defects\u201d is too broad. I want failure-mode evidence showing whether energy is absorbed through intended tension-network deformation, TPU hysteresis, strut buckling, interface peeling, or accidental damage.\n\nThe phrase \u201cno assembly\u201d catches my attention, but \u201celiminates assembly entirely\u201d may be too strong. Does the build require support removal, trimming, conditioning, fixture insertion, post-tensioning, or manual alignment? If so, the accurate claim is \u201csingle-build co-fabrication\u201d or \u201cno joining of PLA and TPU members,\u201d not zero assembly.\n\nI am a Bayesian-optimization novice. I can follow the loop if the presenter says, \u201cThe model proposes the next small batch where improvement is plausible or uncertainty is high.\u201d I will disengage if the slide foregrounds qNEHVI notation rather than the physical decisions it enables.\n\n## (b) What I would repeat the next day\n\n> \u201cThey co-print rigid PLA and flexible TPU energy absorbers, test them, and use the results to choose the next geometries instead of printing a large design-of-experiments grid.\u201d\n\n**Distortion from the intended message:** I would remember the fabrication loop more strongly than tensegrity or Bayesian optimization. I might interpret the improvement as mostly a material-interface result unless the structural mechanisms are shown.\n\n## (c) Top three Q&A questions\n\n1. How was PLA\u2013TPU bonding quantified, and where did failed specimens actually fail: within TPU, within PLA, or at the interface?\n2. How many nominally identical replicates were printed and tested, and how large were within-batch, between-batch, and printer-drift effects relative to design effects?\n3. What manual operations occur after printing, and what exactly does \u201cno assembly\u201d exclude?\n\n## (d) Most pointed challenge\n\n> \u201cHow do you know the optimizer is learning architecture rather than uncontrolled interface quality and process drift?\u201d\n\n**Damage if unanswered: severe.** If fabrication variation is not measured and separated from design effects, both the surrogate and the claimed Pareto front may be unstable. Replicates, blocking or covariates, control specimens, dimensional measurements, and failure-mode documentation are needed.\n\n---\n\n# P4. The first-year graduate student\n\n## (a) First-person reaction\n\nThe opening video and the phrase \u201cthe lander is the energy absorber\u201d give me a concrete picture. I understand the basic problem: too many designs to print, and computer models may miss messy material behavior. I also understand the loop when it is described as \u201cdesign, print, drop, learn, repeat.\u201d That story carries me.\n\nI start getting lost when several unfamiliar terms arrive together: tensegrity-inspired, compaction efficiency, Gaussian process, surrogate, acquisition function, qNEHVI, heteroscedastic noise, Pareto front, probabilistic feasibility constraint, and exploration versus exploitation. The preview says the audience already believes optimization works, but I still need one intuitive sentence explaining what Bayesian optimization does. Skipping \u201cBayesian optimization 101\u201d should not mean skipping the conceptual bridge.\n\nI am also unsure how the structure works. Are the TPU parts stretched before impact? Are the PLA struts touching? What makes this \u201ctensegrity-inspired\u201d rather than a multi-material lattice? A labeled specimen image and a short deformation sequence would help much more than a verbal definition.\n\nMy attention drifts during the repeated case against simulation because I accepted that point the first time. It returns for printing footage, the drop test, and a clear plot with a marked \u201cbetter\u201d direction.\n\n## (b) What I would repeat the next day\n\n> \u201cThey let an artificial-intelligence program learn from 3D-printed drop tests and keep suggesting better shock absorbers, so they do not have to test every design.\u201d\n\n**Distortion from the intended message:** I would probably call Bayesian optimization \u201cAI,\u201d omit the two-objective trade-off and force constraint, and assume each new design is simply better than the last. I might also miss what tensegrity contributes.\n\n## (c) Top three Q&A questions\n\n1. What makes one of these specimens a tensegrity structure rather than an ordinary 3D-printed lattice?\n2. How does the computer decide which design to print next, in plain language?\n3. Can one design maximize both energy absorption and compaction efficiency while also lowering peak force, or must those goals trade off?\n\n## (d) Most pointed challenge\n\n> \u201cI followed the loop, but I still do not understand what is physically special about the structure or what the optimizer actually learned.\u201d\n\n**Damage if unanswered: moderate.** Experts can fill in some gaps, but a talk that leaves novices with only \u201cAI plus 3D printing\u201d has failed to communicate its engineering mechanism. One specimen-mechanics visual and one plain-language optimization visual would largely solve this.\n\n---\n\n# P5. The finite-element-analysis veteran\n\n## (a) First-person reaction\n\nThe statement \u201csimulation can\u2019t be trusted for these structures\u201d puts me on the defensive before I have seen any evidence. Interfacial slip, viscoelasticity, large deformation, buckling, contact, strain-rate dependence, and manufacturing variability are difficult, but they are not intrinsically beyond finite-element analysis. Models can be calibrated, uncertainty can be propagated, and simulation can still screen designs even if it is imperfect.\n\nThe abstract is more defensible than the talk outline. It says the workflow avoids **direct dependence on calibrated finite-element simulation for objective evaluation** and acknowledges future analytical or multifidelity shortcuts. The presentation should use that language. The current outline turns a project-scope choice into a universal technical claim.\n\nI accept the economic argument that calibrating a high-fidelity model may not be worthwhile for an early design campaign. That is a stronger position: measured tests provide authoritative objective values, while the study asks whether sequential design can use a small physical budget efficiently. I would also welcome evidence that simulation errors change design rankings or miss observed failure modes. Without such evidence, the repeated attack on simulation sounds like a straw man.\n\nMy attention drifts when the talk says simulation is unreliable several times without showing a simulation\u2013experiment discrepancy. It returns when the presenter discusses physical failure modes and admits where lower-fidelity physics could later improve the loop.\n\n## (b) What I would repeat the next day\n\n> \u201cThey skipped finite-element modeling and used Bayesian optimization directly on physical tests because they judged model calibration too expensive for these multi-material specimens.\u201d\n\n**Distortion from the intended message:** I would frame the work as bypassing simulation rather than as enabling rapid experimental optimization. If the presenter is combative about simulation, that dispute may become the only thing I remember.\n\n## (c) Top three Q&A questions\n\n1. What evidence shows that a calibrated finite-element model is insufficient for design ranking, rather than merely costly to construct?\n2. Did you compare the physical-test-only workflow with a simple mechanics model or multifidelity surrogate that could reduce the experimental budget further?\n3. Which observed behaviors dominate the model discrepancy: interface failure, TPU viscoelasticity, contact, buckling imperfections, or geometry errors from printing?\n\n## (d) Most pointed challenge\n\n> \u201cYou have shown that simulation is inconvenient, not that it cannot be trusted. Why is abandoning it scientifically preferable to calibrating and validating it?\u201d\n\n**Damage if unanswered: severe and avoidable.** The current absolute wording invites a technical fight that is unnecessary to the contribution. Reframing the claim as a cost-and-dependence decision would preserve the motivation without asserting that finite-element modeling is incapable.\n\n---\n\n# P6. The friendly industry generalist\n\n## (a) First-person reaction\n\nThe loop is memorable: print a candidate, test it, update the model, and print the next informative candidate. I can imagine using that pattern for brackets, seals, lattice pads, or process settings when simulation is weak and tests are expensive. \u201cDozens rather than thousands\u201d is exactly the kind of value proposition I remember.\n\nI do not need the mathematical details of qNEHVI, but I need an operational recipe. What software is used? How many initial samples are required? How automated is the handoff from optimizer to computer-aided design and slicing? How long does one cycle take? What expertise does a team need? The abstract says candidate selection is automated but slicing, specimen handling, and testing remain manual. That qualification should appear in the talk before I infer a robotic self-driving laboratory.\n\nThe tensegrity and planetary material is interesting, but my attention will drift if it occupies too much of a 15-minute slot. The method becomes useful to me only when the presenter explicitly generalizes it and defines when it is worth using.\n\nThe ending about moving humankind forward is less memorable than a concrete deployment rule. I would prefer a final slide saying: \u201cUse this loop when tests are authoritative, each test is costly, the design space is mixed, and several performance goals conflict.\u201d\n\n## (b) What I would repeat the next day\n\n> \u201cThey showed a practical test-driven optimization loop that can find good 3D-printed designs with a few dozen prototypes instead of a giant test matrix.\u201d\n\n**Distortion from the intended message:** I may overstate automation and assume the \u201cfew dozen\u201d claim is established across applications. I will probably forget the names qNEHVI and tensegrity, but retain the workflow.\n\n## (c) Top three Q&A questions\n\n1. What parts of the workflow are automated today, and what does an engineer still do manually between recommendation and test result?\n2. What minimum experiment budget and software stack would my team need to try this on a different component?\n3. When is this approach better than a conventional design of experiments, response-surface method, or a calibrated simulation?\n\n## (d) Most pointed challenge\n\n> \u201cWhat concrete evidence tells me this saved enough prototypes and engineering time to justify the added optimization infrastructure?\u201d\n\n**Damage if unanswered: moderate to severe.** I will still like the idea, but I will not take it back to my team. A budget-matched baseline and a simple timeline or cost comparison would convert interest into action.\n\n---\n\n# Synthesis\n\n## Cross-persona themes\n\n### 1. The causal chain is understandable, but the claimed payoff is not yet demonstrated\n\nAll six personas can understand the basic design\u2013print\u2013test\u2013learn loop. The empty results section prevents them from deciding whether the loop actually found better designs, found them faster, or handled noise reliably. The phrase \u201cdozens, not thousands\u201d is currently an unsupported quantitative claim.\n\nAt minimum, the results need to show:\n\n- total number of unique designs and physical specimens;\n- initial versus adaptively selected designs;\n- batch size and number of iterations;\n- replicate policy and failure count;\n- measured uncertainty or repeatability;\n- progress under the actual experimental budget;\n- the final feasible Pareto set for **specific energy absorption and compaction efficiency subject to the force cap**;\n- a budget-matched nonadaptive or simpler optimization baseline.\n\n### 2. \u201cSimulation can\u2019t be trusted\u201d is too absolute\n\nP1 questions the evidence, P2 cares about application validity, and P5 directly rejects the premise. Even P6 needs to know when the loop is preferable to simulation. The abstract already contains better wording: physical measurements are used for objective evaluation \u201cwithout relying on calibrated finite-element simulation.\u201d\n\nThe defensible claim is not that simulation fails universally. It is that a sufficiently calibrated multiphysics model may be expensive relative to the available design campaign, and the authors therefore test whether physical-data-driven sequential optimization is useful without depending on such a model.\n\n### 3. The talk needs a two-level explanation of Bayesian optimization\n\nP1 does not want introductory Bayesian-optimization material; P2, P3, P4, and P5 need a plain conceptual bridge. These needs are compatible. Give the intuition in one sentence and one visual, then put the implementation specifics in a compact technical panel or backup slide.\n\nSuggested spoken explanation:\n\n> \u201cAfter each batch, the model estimates both expected performance and uncertainty across the design space. The acquisition rule selects a small next batch that is most likely to expand the feasible trade-off frontier, while accounting for noisy tests and print failures.\u201d\n\nThis is not \u201cBayesian optimization 101.\u201d It is the minimum explanation needed to interpret the workflow.\n\n### 4. The physical mechanism and the word \u201ctensegrity\u201d need clarification\n\nP2 may reduce the objects to crush structures, P3 may interpret the result as an interface study, and P4 may not distinguish the specimens from ordinary lattices. A labeled as-printed specimen plus a four-frame deformation sequence should show:\n\n- rigid PLA struts;\n- continuous flexible TPU network;\n- whether and how pre-tension exists;\n- load path before and during impact;\n- intended energy-dissipation mechanisms;\n- why \u201ctensegrity-inspired\u201d is the precise term.\n\n### 5. Manufacturing variability is part of the model, not background noise\n\nP1 and P3 will both press this point. Periodically retesting one control is useful but may not identify design-specific heteroscedasticity, lot effects, or confounding between iteration and material batch. The talk needs to state the replication, randomization, blocking, covariate, and drift-monitoring plan. If those steps were not used, the limitation must be explicit.\n\n### 6. The application framing outruns the demonstrated scope\n\nThe planetary hook is strong, but P2 will not equate fixed-energy laboratory tests on PLA\u2013TPU specimens with flight hardware. The closing escalates further by claiming that the next lander absorber can be \u201cproven\u201d in weeks. That wording should be narrowed unless the campaign includes relevant scaling and environmental evidence.\n\n### 7. The objective statement is not fully consistent across the outline\n\nThe task and Point 2 correctly describe two objectives, specific energy absorption and compaction efficiency, with a peak-force constraint. The candidate evidence list instead proposes \u201cSEA vs. peak transmitted force,\u201d and the abstract\u2019s expected-outcomes section similarly describes trade-offs between SEA and peak force. Those are different optimization formulations.\n\nThe talk must consistently distinguish:\n\n- **objectives:** maximize specific energy absorption and compaction efficiency;\n- **constraint:** peak transmitted force must not exceed a specified cap;\n- **feasible Pareto front:** trade-offs between the two objectives among designs satisfying that cap.\n\nIf peak force is instead treated as a third objective, the task statement and optimization formulation must be revised accordingly.\n\n### 8. The closing favors inspiration over technical precision\n\nP6 is likely to remember \u201cdozens, not thousands,\u201d but P1, P2, and P5 may hear overreach in \u201cproven,\u201d \u201cin weeks,\u201d and \u201cmoves humankind forward.\u201d A DAC-10 close should end on the measured engineering result and transfer condition. The Super Ball Bot bookend can remain, but it should not imply flight qualification.\n\n---\n\n# Three highest-priority revisions\n\n## Priority 1: Build the talk around a quantitative result, not around the availability of a workflow\n\nReplace the placeholder with a required three-slide evidence sequence:\n\n1. **\u201cThe campaign used N specimens across B adaptive batches under a fixed physical-test budget.\u201d** \n Show initialization, adaptive batches, replicates, failures, elapsed cycle time, and the force threshold. Do not use `N` or `B` in the actual talk; insert the observed values.\n\n2. **\u201cAdaptive selection improved the feasible design set faster than [predeclared baseline].\u201d** \n Plot a budget-matched performance measure across physical tests, with uncertainty across repeated runs if available. If only one physical campaign exists, avoid inferential claims that require repeated campaigns and supplement with clearly labeled retrospective resampling or simulation-based algorithm checks.\n\n3. **\u201cThese measured designs form the final feasible trade-off between specific energy absorption and compaction efficiency.\u201d** \n Show uncertainty or replicate spread, mark infeasible force-cap violations, and include specimen images or force\u2013time traces for representative Pareto designs.\n\nIf the campaign cannot support those statements, revise the main message from \u201cwe can optimize \u2026 in dozens\u201d to \u201cwe are evaluating whether noisy multi-objective Bayesian optimization can reduce the physical test budget.\u201d\n\n## Priority 2: Replace the anti-simulation premise with a scoped engineering decision\n\nReplace:\n\n> \u201cSimulation can\u2019t be trusted for these structures.\u201d\n\nwith:\n\n> \u201cFor these multi-material prints, obtaining objective values from a validated high-fidelity model would require substantial calibration of interface, rate, contact, and defect behavior. We therefore ask how far a limited budget of direct physical tests can take us without depending on that model.\u201d\n\nThen title Point 1 with a message rather than a topic:\n\n> **\u201cWhen model calibration and exhaustive testing are both costly, each physical specimen must be chosen for information value.\u201d**\n\nThis keeps the pincer structure while avoiding an unnecessary universal claim.\n\n## Priority 3: Reallocate the 15 minutes around audience decisions\n\nA concrete timing plan:\n\n- **1.5 min:** Super Ball Bot hook, specimen, and present study scope.\n- **2 min:** Why exhaustive physical search and high-fidelity calibration are costly.\n- **2.5 min:** One visual of the closed loop, including a one-sentence Bayesian-optimization explanation.\n- **2 min:** Actual design variables, objectives, force constraint, fabrication failures, and noise controls.\n- **5 min:** measured results, baseline, uncertainty, representative specimens, and failure modes.\n- **1 min:** limitations and transfer conditions.\n- **1 min:** measured conclusion and bookend.\n\nMove kernel choices, log-space acquisitions, detailed encoding, and other implementation material to backup slides unless a result depends directly on them. This follows Doumont\u2019s principle that each slide should communicate one interpreted message rather than expose the speaker\u2019s notes.\n\n---\n\n# Claims needing evidence, hedging, or backup slides\n\n| Claim or topic | What is needed in the main talk | Prepared backup material |\n|---|---|---|\n| \u201cDozens of prints, not thousands\u201d | Actual specimen count, definition of success, and budget-matched comparator | Full campaign ledger; sensitivity to initialization and stopping rule |\n| \u201cSimulation can\u2019t be trusted\u201d | Replace with scoped calibration-cost language unless direct validation evidence exists | Simulation\u2013experiment traces, ranking errors, or documented failure modes a tested model missed |\n| \u201cqNEHVI handles heteroscedastic noise\u201d | Specify the actual observation-noise model and replication strategy | Likelihood formulation, fixed versus inferred noise, residual diagnostics, replicate variance by design or batch |\n| qNEHVI \u201cover standard expected improvement\u201d | Use a technically appropriate comparison; ordinary expected improvement is not a like-for-like multi-objective batch baseline | Comparisons with random/Sobol, scalarization, qEHVI or another relevant noisy/constrained baseline |\n| \u201cThe Pareto front is the design deliverable\u201d | Show the two stated objectives and force-cap feasibility consistently | Hypervolume reference point, normalization, uncertainty, constraint treatment |\n| Peak-force cap | State its numerical value and engineering rationale | Sensitivity of selected designs to alternative cap values |\n| Probabilistic print-feasibility constraint | Report failures and demonstrate that the classifier or constraint model is identifiable at the available sample size | Failure labels, calibration, confusion or reliability metrics, acquisition formula |\n| \u201cNo assembly\u201d / \u201celiminates assembly entirely\u201d | List any post-print operations; use \u201csingle-build co-fabrication\u201d if manual steps remain | Fabrication workflow photographs and labor-time accounting |\n| \u201cCandidate goes to tested specimen in hours\u201d | Median or representative print-to-result time and what remains manual | Time breakdown for design, slicing, printing, conditioning, inspection, and testing |\n| \u201cDramatically faster\u201d | Define faster relative to a measured or estimated comparator | Cost and wall-clock accounting with assumptions |\n| Transfer to other architectures | Present as a conditional hypothesis, not a demonstrated result | Applicability checklist: test cost, dimensionality, noise, throughput, and constraint structure |\n| Planetary-landing relevance | Clearly state that this is motivation unless scale and environment are tested | Impact similitude, energy and velocity regime, environmental gaps, technology-readiness discussion |\n| PLA\u2013TPU interface robustness | Failure-mode and repeatability evidence | Microscopy or fracture images, process parameters, conditioning and moisture controls |\n| \u201cTensegrity-inspired\u201d | Show the load path and identify which tensegrity features are retained | Geometric definition, pre-tension status, comparison with a conventional lattice/control |\n| Independent Gaussian processes | State why cross-objective correlation is ignored and whether that choice matters | Residual correlations and sensitivity to multi-output alternatives |\n| Mixed categorical variables | Briefly state how candidates are represented and optimized | Kernel or encoding details; treatment of invalid combinations |\n\n---\n\n# Fit to the six-person audience\n\n## Best served: P6, the friendly industry generalist\n\nThe outline has a clear problem\u2013method\u2013application arc, a memorable loop, and an attractive efficiency promise. P6 can understand the proposed value without needing the mathematics. The danger is that this persona may accept unsupported claims and overestimate the system\u2019s automation and maturity.\n\nP4 also benefits from the story structure, but the unexplained terminology and absent physical-mechanism visual will cause substantial loss of detail.\n\n## Worst served: P1, the skeptical Bayesian-optimization insider\n\nP1 is the most likely to judge the current talk as an off-the-shelf acquisition function applied to a new artifact. The outline anticipates this concern by saying not to teach Bayesian optimization, but it does not yet supply the evidence P1 needs: novelty positioning, rigorous baseline, budget, noise identification, constraint diagnostics, and measured sample efficiency.\n\nP5 is nearly as poorly served because the opening motivation directly challenges finite-element modeling more strongly than the abstract supports.\n\n## Is that the right trade-off for DAC-10?\n\nNo. A DAC-10 presentation should remain accessible to P2\u2013P6, but it cannot underserve P1. The design-automation expert is likely to shape the technical discussion and the perceived contribution. Accessibility should come from a plain-language loop and strong visuals, not from omitting methodological validation. The best target is a two-layer talk: every listener can follow the engineering decision, while experts can see the experimental budget, comparator, noise model, and constraint formulation.\n\n---\n\n# Predicted reception\n\n**Current outline: 5/10.** The loop and application are memorable, but without measured results, a budget-matched baseline, and narrower simulation and planetary claims, the talk reads as a polished motivation for an experiment rather than a completed DAC-10 contribution.\n\nIf the results establish repeatability and budgeted improvement, and the three priority revisions are made, the same structure could plausibly move into the **7\u20138/10** range. That conditional estimate is a judgment about presentation readiness, not a prediction of unobserved experimental outcomes.\n\n---\n\n# Discretionary analytical decisions\n\n- Treated the unfinished evidence/results section as missing evidence rather than assuming that the planned campaign succeeds.\n- Evaluated the artifact as a competent 15-minute delivery from the outline, not as a line edit of the outline or an assessment of slides that do not yet exist.\n- Weighted technical credibility and evidentiary support more heavily than visual polish because the target is the DAC-10 design-optimization audience.\n- Used the submitted abstract to resolve technical intent when the outline was ambiguous, while flagging inconsistencies rather than silently harmonizing them.\n- Interpreted the stated optimization problem as two objectives, specific energy absorption and compaction efficiency, subject to a peak-force constraint; flagged language that instead treats peak force as an objective.\n- Assigned qualitative damage levels of moderate, moderate-to-severe, or severe based on how directly an unanswered challenge would undermine each persona\u2019s acceptance of the central claim.\n- Chose P6 as the best-served persona because the present outline emphasizes an intuitive workflow and practical efficiency promise; chose P1 as the worst-served because the DAC-specific validation and novelty case is not yet developed.\n- Scored predicted reception on the current incomplete outline, not on a hypothetical final talk with successful results.') [id=call_zSHcqZDq4Rrb984ui6lpQugL]", + "answer": "# Mock audience report: IDETC-CIE 2026 DAC-10 talk\n\n## Scope of this review\n\nThis evaluates the talk implied by the outline, not finished slides or delivery technique. The experimental campaign is still in progress, so I do **not** infer any favorable results. The empty \u201cEvidence / results\u201d section is treated as a major unresolved part of the talk: the outline currently explains why the workflow should be useful, but does not yet demonstrate that it is useful.\n\nThe central communication test is whether each listener leaves with the intended message:\n\n> By closing the loop between multi-material 3D printing and Bayesian optimization, we can optimize tensegrity energy absorbers directly from real impact data, in dozens of prints rather than thousands.\n\n---\n\n# P1. The skeptical Bayesian-optimization insider\n\n## (a) First-person reaction\n\nThe application is potentially interesting, and the design\u2013print\u2013test loop is easy to understand. The mixed continuous, integer, and categorical design space, physical failures, parallel fabrication, and multiple constrained objectives could make this a worthwhile design-automation case study.\n\nBut the outline keeps telling me that Bayesian optimization is sample-efficient without showing that **this implementation** is sample-efficient. \u201cDozens, not thousands\u201d sounds like a slogan unless I see the actual experiment count, initialization size, batch size, replication policy, convergence behavior, and a comparison against credible alternatives. q-noisy expected hypervolume improvement, or qNEHVI, is established machinery. Merely deploying it does not establish methodological novelty.\n\nI become skeptical at the statement that qNEHVI was selected because of heteroscedastic TPU and print noise. qNEHVI accounts for noisy observations, but the outline does not explain how the surrogate models heteroscedasticity, whether repeated specimens identify noise, or whether noise is being conflated with unmodeled process drift. Independent Gaussian processes with an unspecified observation model do not automatically resolve batch-dependent heteroscedasticity.\n\nMy attention drifts during the broad argument against simulation because it delays the information I came to DAC-10 to hear: the design variables, model formulation, constraint handling, acquisition implementation, budget, and benchmark. The Super Ball Bot hook is visually effective, but planetary landing may feel like application theater if the actual specimens and impact regime are far removed from that use case.\n\nThe strongest possible section is the results slot. In its current empty state, the talk has a method narrative but no validation.\n\n## (b) What I would repeat the next day\n\n> \u201cThey used standard batch noisy multi-objective Bayesian optimization to choose which multi-material printed impact specimens to test, instead of running a full physical sweep.\u201d\n\n**Distortion from the intended message:** I would omit the claim that the structures were optimized in dozens of prints because I have not seen evidence for it. I would also describe qNEHVI as standard rather than as the contribution.\n\n## (c) Top three Q&A questions\n\n1. **What is the actual experimental budget?** How many initial designs, adaptive iterations, specimens per batch, and physical replicates were used, and what stopping rule defined success?\n2. **What baselines did you beat?** Did you compare qNEHVI with Sobol or Latin-hypercube sampling, random search, scalarized Bayesian optimization, or a domain-informed sequential design under the same number of physical tests?\n3. **How exactly is noise modeled?** Are replicate-dependent variances supplied to the Gaussian processes, is a heteroscedastic likelihood fitted, and how are TPU lot, print batch, machine drift, and specimen-level defects distinguished?\n\n## (d) Most pointed challenge\n\n> \u201cWhat did you contribute beyond applying off-the-shelf qNEHVI to a new specimen family?\u201d\n\n**Damage if unanswered: severe.** For a DAC review audience, this can reduce the work from a design-automation contribution to an application demonstration. A strong answer could rest on the experimental formulation, mixed-variable and failure-aware implementation, quantitative sample-efficiency evidence, and transferable operating lessons. Those contributions must be stated explicitly and tested.\n\n---\n\n# P2. The aerospace entry, descent, and landing practitioner\n\n## (a) First-person reaction\n\nThe opening lands immediately. I recognize the Super Ball Bot concept, and \u201cthe lander is the energy absorber\u201d is a useful framing. The three performance quantities also sound relevant: energy absorbed per mass, usable stroke, and force transmitted to the payload.\n\nThen the talk moves into language I do not naturally use: Gaussian-process surrogate, qNEHVI, acquisition function, hypervolume, exploration versus exploitation, and Pareto front. I can accept that the algorithm chooses the next tests, but I need a plain explanation of what information it uses and why I should trust the recommendations. If the presenter spends several minutes naming algorithms without connecting them to engineering decisions, I will lose the thread.\n\nMy main concern is application fidelity. A desktop drop-weight test at one fixed impact energy is not automatically evidence for entry, descent, and landing hardware. I need specimen dimensions, impact mass, velocity, energy, strain rate, boundary conditions, number of impacts, rebound behavior, temperature, vacuum sensitivity, and failure containment. PLA and TPU raise immediate environmental and qualification questions. I also want to know whether the absorber is reusable or sacrificial.\n\nMy attention returns when I see force\u2013time traces, high-speed footage, and damaged specimens. It drifts if the talk stays on the optimizer rather than connecting each output to payload protection.\n\n## (b) What I would repeat the next day\n\n> \u201cThey have an algorithm that learns from successive drop tests and tells them which 3D-printed crush structure to make next, so they can find a lightweight low-force design with fewer prototypes.\u201d\n\n**Distortion from the intended message:** I would probably call the structures \u201ccrush structures,\u201d losing the tensegrity distinction. I might also overgeneralize the method to planetary landers even though the current tests appear to be laboratory-scale.\n\n## (c) Top three Q&A questions\n\n1. What impact mass, velocity, energy, and strain-rate regime do the tests cover, and how do those values compare with a credible lander load case?\n2. How do the selected designs scale with size and impact energy? Are the controlling mechanisms material-rate effects, geometric buckling, contact, or all three?\n3. What happens under off-axis impact, repeated impacts, temperature extremes, aging, vacuum, and manufacturing defects?\n\n## (d) Most pointed challenge\n\n> \u201cWhy should I believe that a PLA\u2013TPU specimen optimized at one laboratory impact condition tells me anything about flight-scale landing protection?\u201d\n\n**Damage if unanswered: severe for the planetary-landing framing, moderate for the underlying optimization study.** The presenter should clearly label planetary landing as motivation, not demonstrated readiness, and state the present technology scope. Otherwise the opening and closing overpromise relative to the reported experiment.\n\n---\n\n# P3. The additive-manufacturing and materials researcher\n\n## (a) First-person reaction\n\nThe monolithic PLA\u2013TPU architecture is the part I care about. The core-wrapping strategy sounds more credible than simply depositing TPU against exposed PLA and assuming durable adhesion. Holding print settings fixed within a batch, weighing specimens, checking dimensions, and periodically testing a control are all sensible.\n\nI still need much more fabrication detail before I accept that design geometry is the main source of performance variation. PLA\u2013TPU interfaces are sensitive to temperature, surface contact, moisture, residence time, toolpath order, contamination, and local geometry. \u201cPrint defects\u201d is too broad. I want failure-mode evidence showing whether energy is absorbed through intended tension-network deformation, TPU hysteresis, strut buckling, interface peeling, or accidental damage.\n\nThe phrase \u201cno assembly\u201d catches my attention, but \u201celiminates assembly entirely\u201d may be too strong. Does the build require support removal, trimming, conditioning, fixture insertion, post-tensioning, or manual alignment? If so, the accurate claim is \u201csingle-build co-fabrication\u201d or \u201cno joining of PLA and TPU members,\u201d not zero assembly.\n\nI am a Bayesian-optimization novice. I can follow the loop if the presenter says, \u201cThe model proposes the next small batch where improvement is plausible or uncertainty is high.\u201d I will disengage if the slide foregrounds qNEHVI notation rather than the physical decisions it enables.\n\n## (b) What I would repeat the next day\n\n> \u201cThey co-print rigid PLA and flexible TPU energy absorbers, test them, and use the results to choose the next geometries instead of printing a large design-of-experiments grid.\u201d\n\n**Distortion from the intended message:** I would remember the fabrication loop more strongly than tensegrity or Bayesian optimization. I might interpret the improvement as mostly a material-interface result unless the structural mechanisms are shown.\n\n## (c) Top three Q&A questions\n\n1. How was PLA\u2013TPU bonding quantified, and where did failed specimens actually fail: within TPU, within PLA, or at the interface?\n2. How many nominally identical replicates were printed and tested, and how large were within-batch, between-batch, and printer-drift effects relative to design effects?\n3. What manual operations occur after printing, and what exactly does \u201cno assembly\u201d exclude?\n\n## (d) Most pointed challenge\n\n> \u201cHow do you know the optimizer is learning architecture rather than uncontrolled interface quality and process drift?\u201d\n\n**Damage if unanswered: severe.** If fabrication variation is not measured and separated from design effects, both the surrogate and the claimed Pareto front may be unstable. Replicates, blocking or covariates, control specimens, dimensional measurements, and failure-mode documentation are needed.\n\n---\n\n# P4. The first-year graduate student\n\n## (a) First-person reaction\n\nThe opening video and the phrase \u201cthe lander is the energy absorber\u201d give me a concrete picture. I understand the basic problem: too many designs to print, and computer models may miss messy material behavior. I also understand the loop when it is described as \u201cdesign, print, drop, learn, repeat.\u201d That story carries me.\n\nI start getting lost when several unfamiliar terms arrive together: tensegrity-inspired, compaction efficiency, Gaussian process, surrogate, acquisition function, qNEHVI, heteroscedastic noise, Pareto front, probabilistic feasibility constraint, and exploration versus exploitation. The preview says the audience already believes optimization works, but I still need one intuitive sentence explaining what Bayesian optimization does. Skipping \u201cBayesian optimization 101\u201d should not mean skipping the conceptual bridge.\n\nI am also unsure how the structure works. Are the TPU parts stretched before impact? Are the PLA struts touching? What makes this \u201ctensegrity-inspired\u201d rather than a multi-material lattice? A labeled specimen image and a short deformation sequence would help much more than a verbal definition.\n\nMy attention drifts during the repeated case against simulation because I accepted that point the first time. It returns for printing footage, the drop test, and a clear plot with a marked \u201cbetter\u201d direction.\n\n## (b) What I would repeat the next day\n\n> \u201cThey let an artificial-intelligence program learn from 3D-printed drop tests and keep suggesting better shock absorbers, so they do not have to test every design.\u201d\n\n**Distortion from the intended message:** I would probably call Bayesian optimization \u201cAI,\u201d omit the two-objective trade-off and force constraint, and assume each new design is simply better than the last. I might also miss what tensegrity contributes.\n\n## (c) Top three Q&A questions\n\n1. What makes one of these specimens a tensegrity structure rather than an ordinary 3D-printed lattice?\n2. How does the computer decide which design to print next, in plain language?\n3. Can one design maximize both energy absorption and compaction efficiency while also lowering peak force, or must those goals trade off?\n\n## (d) Most pointed challenge\n\n> \u201cI followed the loop, but I still do not understand what is physically special about the structure or what the optimizer actually learned.\u201d\n\n**Damage if unanswered: moderate.** Experts can fill in some gaps, but a talk that leaves novices with only \u201cAI plus 3D printing\u201d has failed to communicate its engineering mechanism. One specimen-mechanics visual and one plain-language optimization visual would largely solve this.\n\n---\n\n# P5. The finite-element-analysis veteran\n\n## (a) First-person reaction\n\nThe statement \u201csimulation can\u2019t be trusted for these structures\u201d puts me on the defensive before I have seen any evidence. Interfacial slip, viscoelasticity, large deformation, buckling, contact, strain-rate dependence, and manufacturing variability are difficult, but they are not intrinsically beyond finite-element analysis. Models can be calibrated, uncertainty can be propagated, and simulation can still screen designs even if it is imperfect.\n\nThe abstract is more defensible than the talk outline. It says the workflow avoids **direct dependence on calibrated finite-element simulation for objective evaluation** and acknowledges future analytical or multifidelity shortcuts. The presentation should use that language. The current outline turns a project-scope choice into a universal technical claim.\n\nI accept the economic argument that calibrating a high-fidelity model may not be worthwhile for an early design campaign. That is a stronger position: measured tests provide authoritative objective values, while the study asks whether sequential design can use a small physical budget efficiently. I would also welcome evidence that simulation errors change design rankings or miss observed failure modes. Without such evidence, the repeated attack on simulation sounds like a straw man.\n\nMy attention drifts when the talk says simulation is unreliable several times without showing a simulation\u2013experiment discrepancy. It returns when the presenter discusses physical failure modes and admits where lower-fidelity physics could later improve the loop.\n\n## (b) What I would repeat the next day\n\n> \u201cThey skipped finite-element modeling and used Bayesian optimization directly on physical tests because they judged model calibration too expensive for these multi-material specimens.\u201d\n\n**Distortion from the intended message:** I would frame the work as bypassing simulation rather than as enabling rapid experimental optimization. If the presenter is combative about simulation, that dispute may become the only thing I remember.\n\n## (c) Top three Q&A questions\n\n1. What evidence shows that a calibrated finite-element model is insufficient for design ranking, rather than merely costly to construct?\n2. Did you compare the physical-test-only workflow with a simple mechanics model or multifidelity surrogate that could reduce the experimental budget further?\n3. Which observed behaviors dominate the model discrepancy: interface failure, TPU viscoelasticity, contact, buckling imperfections, or geometry errors from printing?\n\n## (d) Most pointed challenge\n\n> \u201cYou have shown that simulation is inconvenient, not that it cannot be trusted. Why is abandoning it scientifically preferable to calibrating and validating it?\u201d\n\n**Damage if unanswered: severe and avoidable.** The current absolute wording invites a technical fight that is unnecessary to the contribution. Reframing the claim as a cost-and-dependence decision would preserve the motivation without asserting that finite-element modeling is incapable.\n\n---\n\n# P6. The friendly industry generalist\n\n## (a) First-person reaction\n\nThe loop is memorable: print a candidate, test it, update the model, and print the next informative candidate. I can imagine using that pattern for brackets, seals, lattice pads, or process settings when simulation is weak and tests are expensive. \u201cDozens rather than thousands\u201d is exactly the kind of value proposition I remember.\n\nI do not need the mathematical details of qNEHVI, but I need an operational recipe. What software is used? How many initial samples are required? How automated is the handoff from optimizer to computer-aided design and slicing? How long does one cycle take? What expertise does a team need? The abstract says candidate selection is automated but slicing, specimen handling, and testing remain manual. That qualification should appear in the talk before I infer a robotic self-driving laboratory.\n\nThe tensegrity and planetary material is interesting, but my attention will drift if it occupies too much of a 15-minute slot. The method becomes useful to me only when the presenter explicitly generalizes it and defines when it is worth using.\n\nThe ending about moving humankind forward is less memorable than a concrete deployment rule. I would prefer a final slide saying: \u201cUse this loop when tests are authoritative, each test is costly, the design space is mixed, and several performance goals conflict.\u201d\n\n## (b) What I would repeat the next day\n\n> \u201cThey showed a practical test-driven optimization loop that can find good 3D-printed designs with a few dozen prototypes instead of a giant test matrix.\u201d\n\n**Distortion from the intended message:** I may overstate automation and assume the \u201cfew dozen\u201d claim is established across applications. I will probably forget the names qNEHVI and tensegrity, but retain the workflow.\n\n## (c) Top three Q&A questions\n\n1. What parts of the workflow are automated today, and what does an engineer still do manually between recommendation and test result?\n2. What minimum experiment budget and software stack would my team need to try this on a different component?\n3. When is this approach better than a conventional design of experiments, response-surface method, or a calibrated simulation?\n\n## (d) Most pointed challenge\n\n> \u201cWhat concrete evidence tells me this saved enough prototypes and engineering time to justify the added optimization infrastructure?\u201d\n\n**Damage if unanswered: moderate to severe.** I will still like the idea, but I will not take it back to my team. A budget-matched baseline and a simple timeline or cost comparison would convert interest into action.\n\n---\n\n# Synthesis\n\n## Cross-persona themes\n\n### 1. The causal chain is understandable, but the claimed payoff is not yet demonstrated\n\nAll six personas can understand the basic design\u2013print\u2013test\u2013learn loop. The empty results section prevents them from deciding whether the loop actually found better designs, found them faster, or handled noise reliably. The phrase \u201cdozens, not thousands\u201d is currently an unsupported quantitative claim.\n\nAt minimum, the results need to show:\n\n- total number of unique designs and physical specimens;\n- initial versus adaptively selected designs;\n- batch size and number of iterations;\n- replicate policy and failure count;\n- measured uncertainty or repeatability;\n- progress under the actual experimental budget;\n- the final feasible Pareto set for **specific energy absorption and compaction efficiency subject to the force cap**;\n- a budget-matched nonadaptive or simpler optimization baseline.\n\n### 2. \u201cSimulation can\u2019t be trusted\u201d is too absolute\n\nP1 questions the evidence, P2 cares about application validity, and P5 directly rejects the premise. Even P6 needs to know when the loop is preferable to simulation. The abstract already contains better wording: physical measurements are used for objective evaluation \u201cwithout relying on calibrated finite-element simulation.\u201d\n\nThe defensible claim is not that simulation fails universally. It is that a sufficiently calibrated multiphysics model may be expensive relative to the available design campaign, and the authors therefore test whether physical-data-driven sequential optimization is useful without depending on such a model.\n\n### 3. The talk needs a two-level explanation of Bayesian optimization\n\nP1 does not want introductory Bayesian-optimization material; P2, P3, P4, and P5 need a plain conceptual bridge. These needs are compatible. Give the intuition in one sentence and one visual, then put the implementation specifics in a compact technical panel or backup slide.\n\nSuggested spoken explanation:\n\n> \u201cAfter each batch, the model estimates both expected performance and uncertainty across the design space. The acquisition rule selects a small next batch that is most likely to expand the feasible trade-off frontier, while accounting for noisy tests and print failures.\u201d\n\nThis is not \u201cBayesian optimization 101.\u201d It is the minimum explanation needed to interpret the workflow.\n\n### 4. The physical mechanism and the word \u201ctensegrity\u201d need clarification\n\nP2 may reduce the objects to crush structures, P3 may interpret the result as an interface study, and P4 may not distinguish the specimens from ordinary lattices. A labeled as-printed specimen plus a four-frame deformation sequence should show:\n\n- rigid PLA struts;\n- continuous flexible TPU network;\n- whether and how pre-tension exists;\n- load path before and during impact;\n- intended energy-dissipation mechanisms;\n- why \u201ctensegrity-inspired\u201d is the precise term.\n\n### 5. Manufacturing variability is part of the model, not background noise\n\nP1 and P3 will both press this point. Periodically retesting one control is useful but may not identify design-specific heteroscedasticity, lot effects, or confounding between iteration and material batch. The talk needs to state the replication, randomization, blocking, covariate, and drift-monitoring plan. If those steps were not used, the limitation must be explicit.\n\n### 6. The application framing outruns the demonstrated scope\n\nThe planetary hook is strong, but P2 will not equate fixed-energy laboratory tests on PLA\u2013TPU specimens with flight hardware. The closing escalates further by claiming that the next lander absorber can be \u201cproven\u201d in weeks. That wording should be narrowed unless the campaign includes relevant scaling and environmental evidence.\n\n### 7. The objective statement is not fully consistent across the outline\n\nThe task and Point 2 correctly describe two objectives, specific energy absorption and compaction efficiency, with a peak-force constraint. The candidate evidence list instead proposes \u201cSEA vs. peak transmitted force,\u201d and the abstract\u2019s expected-outcomes section similarly describes trade-offs between SEA and peak force. Those are different optimization formulations.\n\nThe talk must consistently distinguish:\n\n- **objectives:** maximize specific energy absorption and compaction efficiency;\n- **constraint:** peak transmitted force must not exceed a specified cap;\n- **feasible Pareto front:** trade-offs between the two objectives among designs satisfying that cap.\n\nIf peak force is instead treated as a third objective, the task statement and optimization formulation must be revised accordingly.\n\n### 8. The closing favors inspiration over technical precision\n\nP6 is likely to remember \u201cdozens, not thousands,\u201d but P1, P2, and P5 may hear overreach in \u201cproven,\u201d \u201cin weeks,\u201d and \u201cmoves humankind forward.\u201d A DAC-10 close should end on the measured engineering result and transfer condition. The Super Ball Bot bookend can remain, but it should not imply flight qualification.\n\n---\n\n# Three highest-priority revisions\n\n## Priority 1: Build the talk around a quantitative result, not around the availability of a workflow\n\nReplace the placeholder with a required three-slide evidence sequence:\n\n1. **\u201cThe campaign used N specimens across B adaptive batches under a fixed physical-test budget.\u201d** \n Show initialization, adaptive batches, replicates, failures, elapsed cycle time, and the force threshold. Do not use `N` or `B` in the actual talk; insert the observed values.\n\n2. **\u201cAdaptive selection improved the feasible design set faster than [predeclared baseline].\u201d** \n Plot a budget-matched performance measure across physical tests, with uncertainty across repeated runs if available. If only one physical campaign exists, avoid inferential claims that require repeated campaigns and supplement with clearly labeled retrospective resampling or simulation-based algorithm checks.\n\n3. **\u201cThese measured designs form the final feasible trade-off between specific energy absorption and compaction efficiency.\u201d** \n Show uncertainty or replicate spread, mark infeasible force-cap violations, and include specimen images or force\u2013time traces for representative Pareto designs.\n\nIf the campaign cannot support those statements, revise the main message from \u201cwe can optimize \u2026 in dozens\u201d to \u201cwe are evaluating whether noisy multi-objective Bayesian optimization can reduce the physical test budget.\u201d\n\n## Priority 2: Replace the anti-simulation premise with a scoped engineering decision\n\nReplace:\n\n> \u201cSimulation can\u2019t be trusted for these structures.\u201d\n\nwith:\n\n> \u201cFor these multi-material prints, obtaining objective values from a validated high-fidelity model would require substantial calibration of interface, rate, contact, and defect behavior. We therefore ask how far a limited budget of direct physical tests can take us without depending on that model.\u201d\n\nThen title Point 1 with a message rather than a topic:\n\n> **\u201cWhen model calibration and exhaustive testing are both costly, each physical specimen must be chosen for information value.\u201d**\n\nThis keeps the pincer structure while avoiding an unnecessary universal claim.\n\n## Priority 3: Reallocate the 15 minutes around audience decisions\n\nA concrete timing plan:\n\n- **1.5 min:** Super Ball Bot hook, specimen, and present study scope.\n- **2 min:** Why exhaustive physical search and high-fidelity calibration are costly.\n- **2.5 min:** One visual of the closed loop, including a one-sentence Bayesian-optimization explanation.\n- **2 min:** Actual design variables, objectives, force constraint, fabrication failures, and noise controls.\n- **5 min:** measured results, baseline, uncertainty, representative specimens, and failure modes.\n- **1 min:** limitations and transfer conditions.\n- **1 min:** measured conclusion and bookend.\n\nMove kernel choices, log-space acquisitions, detailed encoding, and other implementation material to backup slides unless a result depends directly on them. This follows Doumont\u2019s principle that each slide should communicate one interpreted message rather than expose the speaker\u2019s notes.\n\n---\n\n# Claims needing evidence, hedging, or backup slides\n\n| Claim or topic | What is needed in the main talk | Prepared backup material |\n|---|---|---|\n| \u201cDozens of prints, not thousands\u201d | Actual specimen count, definition of success, and budget-matched comparator | Full campaign ledger; sensitivity to initialization and stopping rule |\n| \u201cSimulation can\u2019t be trusted\u201d | Replace with scoped calibration-cost language unless direct validation evidence exists | Simulation\u2013experiment traces, ranking errors, or documented failure modes a tested model missed |\n| \u201cqNEHVI handles heteroscedastic noise\u201d | Specify the actual observation-noise model and replication strategy | Likelihood formulation, fixed versus inferred noise, residual diagnostics, replicate variance by design or batch |\n| qNEHVI \u201cover standard expected improvement\u201d | Use a technically appropriate comparison; ordinary expected improvement is not a like-for-like multi-objective batch baseline | Comparisons with random/Sobol, scalarization, qEHVI or another relevant noisy/constrained baseline |\n| \u201cThe Pareto front is the design deliverable\u201d | Show the two stated objectives and force-cap feasibility consistently | Hypervolume reference point, normalization, uncertainty, constraint treatment |\n| Peak-force cap | State its numerical value and engineering rationale | Sensitivity of selected designs to alternative cap values |\n| Probabilistic print-feasibility constraint | Report failures and demonstrate that the classifier or constraint model is identifiable at the available sample size | Failure labels, calibration, confusion or reliability metrics, acquisition formula |\n| \u201cNo assembly\u201d / \u201celiminates assembly entirely\u201d | List any post-print operations; use \u201csingle-build co-fabrication\u201d if manual steps remain | Fabrication workflow photographs and labor-time accounting |\n| \u201cCandidate goes to tested specimen in hours\u201d | Median or representative print-to-result time and what remains manual | Time breakdown for design, slicing, printing, conditioning, inspection, and testing |\n| \u201cDramatically faster\u201d | Define faster relative to a measured or estimated comparator | Cost and wall-clock accounting with assumptions |\n| Transfer to other architectures | Present as a conditional hypothesis, not a demonstrated result | Applicability checklist: test cost, dimensionality, noise, throughput, and constraint structure |\n| Planetary-landing relevance | Clearly state that this is motivation unless scale and environment are tested | Impact similitude, energy and velocity regime, environmental gaps, technology-readiness discussion |\n| PLA\u2013TPU interface robustness | Failure-mode and repeatability evidence | Microscopy or fracture images, process parameters, conditioning and moisture controls |\n| \u201cTensegrity-inspired\u201d | Show the load path and identify which tensegrity features are retained | Geometric definition, pre-tension status, comparison with a conventional lattice/control |\n| Independent Gaussian processes | State why cross-objective correlation is ignored and whether that choice matters | Residual correlations and sensitivity to multi-output alternatives |\n| Mixed categorical variables | Briefly state how candidates are represented and optimized | Kernel or encoding details; treatment of invalid combinations |\n\n---\n\n# Fit to the six-person audience\n\n## Best served: P6, the friendly industry generalist\n\nThe outline has a clear problem\u2013method\u2013application arc, a memorable loop, and an attractive efficiency promise. P6 can understand the proposed value without needing the mathematics. The danger is that this persona may accept unsupported claims and overestimate the system\u2019s automation and maturity.\n\nP4 also benefits from the story structure, but the unexplained terminology and absent physical-mechanism visual will cause substantial loss of detail.\n\n## Worst served: P1, the skeptical Bayesian-optimization insider\n\nP1 is the most likely to judge the current talk as an off-the-shelf acquisition function applied to a new artifact. The outline anticipates this concern by saying not to teach Bayesian optimization, but it does not yet supply the evidence P1 needs: novelty positioning, rigorous baseline, budget, noise identification, constraint diagnostics, and measured sample efficiency.\n\nP5 is nearly as poorly served because the opening motivation directly challenges finite-element modeling more strongly than the abstract supports.\n\n## Is that the right trade-off for DAC-10?\n\nNo. A DAC-10 presentation should remain accessible to P2\u2013P6, but it cannot underserve P1. The design-automation expert is likely to shape the technical discussion and the perceived contribution. Accessibility should come from a plain-language loop and strong visuals, not from omitting methodological validation. The best target is a two-layer talk: every listener can follow the engineering decision, while experts can see the experimental budget, comparator, noise model, and constraint formulation.\n\n---\n\n# Predicted reception\n\n**Current outline: 5/10.** The loop and application are memorable, but without measured results, a budget-matched baseline, and narrower simulation and planetary claims, the talk reads as a polished motivation for an experiment rather than a completed DAC-10 contribution.\n\nIf the results establish repeatability and budgeted improvement, and the three priority revisions are made, the same structure could plausibly move into the **7\u20138/10** range. That conditional estimate is a judgment about presentation readiness, not a prediction of unobserved experimental outcomes.\n\n---\n\n# Discretionary analytical decisions\n\n- Treated the unfinished evidence/results section as missing evidence rather than assuming that the planned campaign succeeds.\n- Evaluated the artifact as a competent 15-minute delivery from the outline, not as a line edit of the outline or an assessment of slides that do not yet exist.\n- Weighted technical credibility and evidentiary support more heavily than visual polish because the target is the DAC-10 design-optimization audience.\n- Used the submitted abstract to resolve technical intent when the outline was ambiguous, while flagging inconsistencies rather than silently harmonizing them.\n- Interpreted the stated optimization problem as two objectives, specific energy absorption and compaction efficiency, subject to a peak-force constraint; flagged language that instead treats peak force as an objective.\n- Assigned qualitative damage levels of moderate, moderate-to-severe, or severe based on how directly an unanswered challenge would undermine each persona\u2019s acceptance of the central claim.\n- Chose P6 as the best-served persona because the present outline emphasizes an intuitive workflow and practical efficiency promise; chose P1 as the worst-served because the DAC-specific validation and novelty case is not yet developed.\n- Scored predicted reception on the current incomplete outline, not on a hypothetical final talk with successful results.", + "done": true, + "nb_state": { + "nbformat": 4, + "nbformat_minor": 5, + "metadata": { + "kernelspec": { + "name": "python", + "display_name": "Python 3 (ipykernel)", + "language": "python" + } + }, + "cells": [] + }, + "nb_runtime_errors": [] + }, + "info": { + "language": "python", + "problem": "You are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "workspace_dir": "/workspace", + "work_dir": "/workspace", + "input_data": [], + "output_data": [ + { + "entry_id": "4949af58-f08a-4541-ad1c-1f0a95c6f07d-memory-her-b6tg", + "filename": "4949af58-f08a-4541-ad1c-1f0a95c6f07d_memory_heron_c0_20260718T175417754536.jsonl", + "file_size": 106342 + }, + { + "entry_id": "4949af58-f08a-4541-ad1c-1f0a95c6f07d-ipynb-qj70", + "filename": "4949af58-f08a-4541-ad1c-1f0a95c6f07d.ipynb", + "file_size": 184 + } + ] + } + }, + "supplemental_data": null, + "created_at": "2026-07-18T17:57:28.916967Z" + }, + "metadata": null, + "deployment_config": null, + "failure_reason": null, + "task": "You are simulating a mock conference audience for a ~15-minute technical talk at\nIDETC-CIE 2026 (ASME International Design Engineering Technical Conferences),\nDesign Automation Conference track (DAC-10: design optimization applications).\n\nDocuments (inlined below):\n1. doumont-presentation-template.md \u2014 the presentation outline to evaluate,\n structured per Jean-luc Doumont's opening/body/closing template. This is the\n artifact under review. Evaluate the talk a competent presenter would deliver\n from this outline (slides not yet made; an \"Evidence/results\" slot is a\n placeholder while the experimental campaign is in progress).\n2. idetc-abstract.tex \u2014 the submitted conference abstract, for technical\n grounding of the project's claims (closed-loop Bayesian optimization of\n multi-material 3D-printed tensegrity-inspired energy absorbers, optimized\n directly from physical impact tests; qNEHVI; objectives = specific energy\n absorption and compaction efficiency subject to a peak transmitted-force cap).\n3. doumont-video-notes.md \u2014 the presenter's notes on Doumont's presentation\n principles (messages not words, adapt to audience, one message per slide,\n signal-to-noise). Use these as part of the evaluation rubric.\n\nSimulate the following SIX audience personas, chosen to span familiarity with\nthe base techniques (Bayesian optimization, tensegrity structures, additive\nmanufacturing), professional background, and level of skepticism:\n\nP1. \"The skeptical BO insider\" \u2014 design-automation professor, 15 years in\n surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO,\n moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks:\n default assumption is \"off-the-shelf qNEHVI applied to yet another\n application.\" Will probe methodological novelty, experiment budget,\n baselines, and noise handling.\nP2. \"The aerospace practitioner\" \u2014 senior EDL (entry, descent, landing)\n engineer from industry. Deep expertise in impact attenuation and lander\n hardware; NO familiarity with BO or ML jargon; knows tensegrity from the\n Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity\n regimes, scaling from desktop specimens to flight hardware, TRL,\n qualification.\nP3. \"The AM/materials researcher\" \u2014 mid-career researcher in multi-material\n FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity.\n NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print\n repeatability, batch-to-batch variation, and whether \"no assembly\" holds.\nP4. \"The first-year grad student\" \u2014 new to ALL three base techniques.\n ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose\n a novice, which jargon lands unexplained, does the story arc carry them?\nP5. \"The FEA veteran\" \u2014 computational structural mechanics researcher, 25\n years of finite-element work, expert in simulation, low familiarity with\n BO. DEFENSIVE AND SKEPTICAL of the claim \"simulation can't be trusted for\n these structures\": will push back that models can be calibrated, and ask\n why the authors didn't try harder before abandoning simulation.\nP6. \"The friendly industry generalist\" \u2014 design engineer attending to scout\n useful methods. Moderate familiarity with optimization and AM, none with\n tensegrity. SUPPORTIVE, low skepticism, limited attention: tests\n memorability and the practical so-what (\"could my team use this Monday?\").\n\nFor EACH persona produce:\n(a) a first-person reaction to the talk as outlined: what landed, what confused\n or lost them, where their attention drifted;\n(b) the main message as they would repeat it to a colleague the next day, in\n their own words \u2014 noting any distortion from the intended message (\"By\n closing the loop between multi-material 3D printing and Bayesian\n optimization, we can optimize tensegrity energy absorbers directly from\n real impact data \u2014 in dozens of prints, not thousands\");\n(c) their top 3 Q&A questions, in character;\n(d) their single most pointed objection or challenge, and how damaging it is if\n the presenter has no good answer.\n\nThen produce a SYNTHESIS section:\n- cross-persona themes (what multiple personas stumbled on or attacked);\n- the 3 highest-priority revisions to the outline, concretely worded;\n- claims in the outline that need evidence, hedging, or a prepared backup slide\n for Q&A;\n- which persona the current outline serves best and worst, and whether that is\n the right trade-off for the DAC-10 audience;\n- predicted overall reception on a 1-10 scale with one-sentence rationale.\n\nWrite the whole result as a well-structured markdown report.\n\n\n---\n\nDOCUMENTS\n\n===== BEGIN doumont-presentation-template.md =====\n# IDETC Presentation \u2014 Doumont Presentation Structure Template\n\nTranscribed from Jean-luc Doumont's presentation-structure template\n([TM&Th-3.2-template.pdf](https://principiae.be/pdfs/TM&Th-3.2-template.pdf)),\nadapted from *Trees, maps, and theorems* (Principiae, 2009). \u00a9 2009 by Principiae.\nAll rights reserved. Can be downloaded from .\n\n> To design your presentation, write down your ideas for each component below.\n> If useful, you can then further develop your body afterwards (the \"back of the sheet\").\n\n**Draft 2** \u2014 transcribed from @me-madsen's\n[Presentation Outline Draft 1 (7-17-2026).pdf](https://github.com/user-attachments/files/30140592/Presentation.Outline.Draft.1.7-17-2026.pdf)\nwith the review feedback from PR #84 applied. A summary of what changed from\nDraft 1 is at the [bottom of this file](#changes-from-draft-1).\n\n## Opening\n\n### Attention getter\n\n*A way to lead the audience to the need efficiently*\n\nTensegrity provides robust solutions to issues posed by planetary landings \u2014\nand to problems closer to home.\n\nOne or two sentences of setup, then let the Super Ball Bot image (or a short\nclip) do the work:\n\n> Landing a payload on another planet means surviving an impact in conditions\n> where parachutes and retrorockets struggle \u2014 thin atmospheres, rough\n> terrain. NASA's Super Ball Bot concept answers this with a tensegrity\n> structure: the lander *is* the energy absorber.\n\nCandidate Super Ball Bot videos to show (verified links):\n\n- [Super Ball Bot](https://www.youtube.com/watch?v=ZBSRdGlAh5s) \u2014 NASA Video\n (official channel); the canonical overview.\n- [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA)\n \u2014 Wall Street Journal; prototype drop footage.\n- [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE)\n \u2014 NASA Video; mission-concept animation (Titan descent) \u2014 strong hook material.\n\n\n\n### Need\n\n*A difference between actual and desired situations*\n\n**Actual situation** (a two-sided pincer):\n\n- Simulation can't be trusted for these structures: FDM interfacial defects\n and TPU rate-dependence make calibrated finite-element models expensive and\n unreliable.\n- The design space is too big to brute-force physically: even modest parameter\n resolutions yield thousands of candidates, each costing print and test time.\n- Idealized cable\u2013strut tensegrities are also difficult to assemble by hand.\n\n**Desired situation:**\n\n- Trustworthy performance data without hand assembly (easy creation of\n tensegrity structures).\n- Quick iteration and testing \u2014 find good designs in dozens of experiments,\n not thousands.\n\n### Task\n\n*What I decided/was asked to do to address the need*\n\nBuild a closed-loop design\u2013print\u2013test workflow that optimizes a parametric\nfamily of tensegrity-inspired energy absorbers directly from real, measured\nimpact data \u2014 maximizing energy absorbed per gram (specific energy\nabsorption) and compaction efficiency while capping the peak force\ntransmitted to the payload.\n\n### Main message\n\n*The one sentence I want my audience to remember*\n\nBy closing the loop between multi-material 3D printing and Bayesian\noptimization, we can optimize tensegrity energy absorbers directly from real\nimpact data \u2014 in dozens of prints, not thousands.\n\n### Preview\n\n*A map of the body (ideally three points, max. five)*\n\n1. Simulation-first design of printed tensegrity structures is unreliable, and\n physical trial-and-error is intractable \u2014 so the measured experiment must\n be the source of truth.\n2. Bayesian optimization makes physical experimentation affordable: it handles\n noisy measurements, small batches, and the multi-objective trade-off\n between energy absorption and transmitted force.\n3. Multi-material additive manufacturing closes the loop: monolithic\n PLA\u2013TPU prints turn each proposed design into test data in hours, with no\n assembly.\n\n## Body\n\n### Point 1\n\nSimulation-first design of printed tensegrity structures is unreliable, and\nphysical trial-and-error is intractable.\n\n- Why current methods fall short (not just *that* they do): high-fidelity FE\n simulation struggles with FDM interfacial slip and TPU rate-dependence, so\n simulated objectives can't be trusted for these builds.\n- The alternative \u2014 sweeping the design space physically \u2014 fails on cost:\n thousands of candidate designs, each needing print + test time.\n- Therefore: treat the physical experiment as the source of truth, and be\n ruthless about *which* experiments to run.\n\n### Transition\n\nIf every data point must be a real experiment, we need a method that extracts\nthe most from every specimen \u2014 we need to make the process radically more\nsample-efficient.\n\n### Point 2\n\nBayesian optimization makes physical experimentation affordable, even with\nlimited, noisy data.\n\n- (DAC-10 audience already believes optimization works \u2014 spend the time on\n what's specific here, not BO 101.)\n- Noisy physical measurements: TPU batch-to-batch variation and print defects\n motivate noise-aware acquisition (qNEHVI) over standard expected\n improvement.\n- Small parallel batches: the surrogate proposes the next batch of specimens\n to print, balancing exploration and exploitation.\n- Multi-objective by construction: maximize specific energy absorption and\n compaction efficiency subject to a peak transmitted-force bound \u2014 the\n Pareto front *is* the design deliverable.\n\n### Transition\n\nThe optimizer is no longer the bottleneck \u2014 the limit is now providing real\ndata quickly enough.\n\n### Point 3\n\nMulti-material additive manufacturing supplies that data fast \u2014 and\neliminates assembly entirely.\n\n- Monolithic PLA\u2013TPU co-printing: rigid struts and flexible tension network\n in a single build \u2014 no hand assembly, which closes the \"easy creation\" gap\n from the Need.\n- Rapid iteration: each BO-proposed candidate goes from parameter vector to\n tested specimen (quasi-static compression + instrumented drop-weight impact)\n in hours.\n- Now we have the data we need to complete the loop.\n\n### Evidence / results\n\n*What we found \u2014 ground the conclusion in shown data*\n\n\n\n## Closing\n\n### Review\n\n*A recap of the body, leading into the conclusion*\n\nUsing real, measured impact data from additively manufactured\ntensegrity-inspired structures \u2014 parametric designs proposed by a Bayesian\noptimization routine \u2014 we rapidly iterate toward better energy absorbers.\nThis feedback loop makes tensegrity optimization dramatically faster: dozens\nof prints instead of thousands of candidates.\n\n### Conclusion\n\n*What the above means to the audience in the end*\n\nBecause the loop runs on physical measurements, it works precisely where\nsimulation fails \u2014 and the same closed-loop pattern transfers to any\nadditively manufactured architecture whose performance is dominated by\nhard-to-simulate effects. Faster, trustworthy optimization of tensegrity\nstructures brings them within reach for payload protection on other planets\nand for safety hardware here on Earth (e.g., the crutch tip, as future work).\n\n### Close\n\n*A way to end the presentation clearly and elegantly*\n\nReturn to the opening image: the next planetary lander's energy absorber\ndoesn't have to take years of hand-built prototypes \u2014 with this loop, it can\nbe designed, printed, and proven against real impacts in weeks. What limits\nour ventures is rarely whether we can do them, but how quickly \u2014 and every\nmethod that compresses that time moves humankind forward.\n\n---\n\n## Changes from Draft 1\n\nFeedback from the PR #84 review, applied above:\n\n1. **Main message** re-centered on speed + real data (\"robust\" \u2192 the\n dozens-not-thousands claim); body and message now carry the same story.\n2. **\"Combining real and simulated data\" removed** (Review section) \u2014 the\n manuscript's positioning (#75/#76) is that the physical experiment is the\n source of truth, explicitly avoiding calibrated FE simulation.\n3. **\"Generative design\" \u2192 \"parametric designs\"** to match the manuscript's\n parametric design space and avoid an over-claim to a design-automation\n audience.\n4. **Objectives made explicit** (Task, Point 2, Review): maximize specific\n energy absorption and compaction efficiency, cap peak transmitted force.\n5. **Need sharpened into the two-sided pincer**: simulation can't be trusted\n *and* the space is too big to brute-force \u2014 which makes BO + AM the\n inevitable answer.\n6. **Point 1 given its own content** (the *why* behind the inefficiency)\n instead of restating the Need.\n7. **Point 2 adapted to the DAC-10 audience**: skips BO advocacy, focuses on\n the specifics (noise-aware acquisition, small batches, multi-objective).\n8. **Point 3 explicitly closes the \"easy assembly\" loop** from the Need\n (monolithic printing = no assembly).\n9. **Evidence/results slot added** before the Closing, so the Conclusion is\n grounded in shown data rather than three hedged \"may\"s.\n10. **Attention getter compressed** to 1\u20132 sentences of setup plus the Super\n Ball Bot visual; verified video links included.\n11. **Close bookends the opening** (the next lander's absorber, designed in\n weeks) before the broader humankind line.\n12. **Spelling**: Doumont (was \"Duomont\").\n\n===== END doumont-presentation-template.md =====\n\n===== BEGIN idetc-abstract.tex =====\n\\documentclass[10pt]{article}\n\n% --- Packages ---\n\\usepackage[letterpaper,margin=0.6in]{geometry}\n\\usepackage[hidelinks]{hyperref}\n\\usepackage{parskip}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{titlesec}\n\\usepackage{wrapfig}\n\\usepackage[numbers,sort&compress]{natbib}\n\n% --- Formatting ---\n\\setlength{\\parskip}{0.2em}\n\\setlength{\\parindent}{0em}\n\\titleformat*{\\section}{\\large\\bfseries}\n\\titleformat*{\\subsection}{\\normalsize\\bfseries\\itshape}\n\\titlespacing*{\\section}{0pt}{0.4em}{0.1em}\n\\titlespacing*{\\subsection}{0pt}{0.25em}{0.05em}\n\\setlength{\\emergencystretch}{2em}\n\\setlength{\\intextsep}{0.2em}\n\\bibliographystyle{abbrvnat}\n\n\\begin{document}\n\n\\begin{center}\n{\\Large\\textbf{Closed-Loop Bayesian Optimization of Multi-Material\\\\\n3D-Printed Tensegrity-Inspired Energy Absorbers}}\\\\[0.4em]\n\\textit{Extended Abstract --- ASME IDETC-CIE 2026}\\\\\n\\textit{Track DAC-10: Design of Engineering Materials and Structures}\n\\end{center}\n\n\\vspace{0.4em}\n\n\\section*{Abstract}\nTensegrity-inspired architectures---rigid struts suspended within a\ncontinuous flexible network---can exhibit tunable nonlinear\nforce--displacement responses and favorable energy absorption per unit\nmass, motivating their use in lightweight energy-absorbing and protective\nstructures\n\\citep{skelton2009tensegrity,pajunen2019design}.\nMulti-material fused deposition modeling (FDM) can co-print rigid (PLA)\nstruts and flexible (TPU) elements in a single build\n\\citep{ye2023multimaterial,khatri2024energy}, but the resulting design\nspace---strut geometry, tension-element cross-section, connectivity\ntopology, and unit-cell tiling---is too large to explore by trial and\nerror. We present a closed-loop experimental campaign that uses\nBayesian optimization (BO) \\citep{shahriari2016taking}\nto drive a design--print--test workflow operating directly on physical\nmeasurements, without relying on calibrated finite-element simulation\nfor objective evaluation.\n\n\\section{Introduction and Motivation}\n\n\\begin{wrapfigure}{r}{0.65\\textwidth}\n\\centering\n\\vspace{-0.4em}\n\\includegraphics[width=0.64\\textwidth]{figures/overview-updated.png}\n\\captionsetup{font=small}\n\\caption{Closed-loop, BO-driven design--print--test workflow.\nCandidate tensegrity-inspired unit cells are instantiated from a\nparameter vector, fabricated by multi-material FDM, and tested under\nquasi-static compression and drop-weight impact. Measured peak force,\nspecific energy absorption (SEA), and compaction efficiency update a\nGaussian-process surrogate that proposes the next batch of designs.}\n\\label{fig:overview}\n\\vspace{-0.4em}\n\\end{wrapfigure}\n\nTensegrity structures combine compression-only members with a\npre-tensioned tension network to achieve stiffness without rigid\njoinery~\\citep{skelton2009tensegrity}. Their\nnonlinear, often load-limiting force--displacement response makes them\nattractive for impact mitigation, packaging, and wearable protective\ngear~\\citep{pajunen2019design}. Idealized\ncable--strut tensegrities are difficult to manufacture at scale, but\n\\emph{tensegrity-inspired} architectures fabricated by single-material\n3D printing reproduce key tensegrity behaviors---including\npost-buckling stability and load-limiting force--displacement\nresponse~\\citep{pajunen2019design}---motivating our extension to\nmulti-material FDM for monolithic co-fabrication of rigid and flexible\nelements. PLA provides rigid struts; TPU provides flexible,\nrate-dependent tension elements whose viscoelasticity is expected to\ncontribute hysteretic energy dissipation under impact loading,\nconsistent with the energy-absorbing behavior reported for rigid--TPU\nmulti-material prints~\\citep{khatri2024energy}.\n\nThe challenge is that small geometric and topological changes can\nproduce large changes in the measured response, and high-fidelity\nsimulation of FDM specimens is computationally expensive and often\nstruggles to capture physical defects such as interfacial slip and\nTPU rate-dependence~\\citep{mo2023accelerated}. Pure\ntrial-and-error sweeps over the joint design space are also infeasible:\neven modest parameter resolutions yield thousands of candidates, each\nrequiring print and test time. We instead treat the physical\nexperiment as the source of truth and use BO to select which specimens\nare most informative to fabricate next---an approach used in related\nclosed-loop materials and structures\ncampaigns~\\citep{mo2023accelerated,low2024evolution}.\nUnlike prior tensegrity-inspired studies that evaluate a fixed design\nset~\\citep{pajunen2019design,khatri2024energy}, we adaptively select\ndesigns using parallel, noisy multi-objective BO driven by measured\n$F_{\\mathrm{peak}}$, SEA, and $\\eta$.\n\n\\section{Approach}\n\\subsection{Closed-Loop Workflow}\nFigure~\\ref{fig:overview} summarizes the campaign. Each iteration\nproceeds in four steps: (i)~a parameterized unit-cell design is\ninstantiated from a candidate vector $\\mathbf{x}$; (ii)~the specimen\nis sliced and printed on a multi-material FDM system; (iii)~it is\ntested under quasi-static compression and instrumented drop-weight\nimpact; and (iv)~the resulting performance metrics update a\nGaussian-process (GP) surrogate, which a batch acquisition function\nqueries to recommend the next set of candidates. Surrogate updating\nand experiment selection are automated; specimen handling, slicing,\nand test setup remain manual.\n\n\\subsection{Design Space}\nWe parameterize a family of unit cells using a core-wrapping\narchitecture in which rigid PLA struts are enclosed by continuous TPU\nskins; the wrapping rationale follows multi-material FDM strategies in\nwhich rigid panels are wrapped by stretchable soft material to prevent\ndelamination and improve interfacial bonding at the rigid--flexible\ninterface~\\citep{ye2023multimaterial}. The design vector $\\mathbf{x}$\ncomprises continuous geometric variables (strut diameter and length,\ntension-element width and thickness), an integer strut count per unit\ncell, and two categorical variables (connectivity topology, unit-cell\ntiling pattern). Categorical choices are encoded explicitly rather\nthan embedded in a continuous metric, and bounds on the continuous\nvariables are chosen to remain within the printer's resolution and\noverhang limits and to keep specimens in a common form factor for the\ntest fixtures.\n\n\\subsection{Surrogate and Acquisition}\nThe GP surrogate is trained on three measured performance metrics:\npeak transmitted force $F_\\mathrm{peak}$, specific energy absorption\n(SEA), and compaction efficiency $\\eta$. We use independent GPs per\nobjective with Mat\\'ern kernels; hyperparameters are fit by marginal\nlikelihood with priors to avoid pathological length scales at small $n$.\nHeteroscedastic noise from TPU batch-to-batch variation and FDM\ninterfacial defects motivates our use of q-noisy expected hypervolume\nimprovement (qNEHVI), which explicitly accounts for observation noise\nin parallel multi-objective BO~\\citep{daulton2021nehvi}, over standard\nexpected-improvement variants. Print failures are treated as a\nprobabilistic feasibility constraint: the acquisition score for each\ncandidate is its qNEHVI improvement weighted by the GP-estimated\nprobability of successful fabrication, following recent constrained\nmulti-objective practice in self-driving labs~\\citep{low2024evolution}.\nWhere applicable, we use numerically stable log-space variants of\nimprovement-based acquisitions to mitigate vanishing-gradient\npathologies as data and constraints\naccumulate~\\citep{ament2023logei}.\n\n\\section{Methods}\n\\subsection{Fabrication}\nSpecimens are fabricated on a multi-material FDM system capable of\nco-printing PLA and TPU within a single build~\\citep{ye2023multimaterial}.\nPrint parameters (temperature, line width, infill, retraction) are\nheld fixed within a batch to isolate the effect of the design\nvariables. Each specimen is weighed and dimensionally inspected prior\nto testing so that mass and geometric deviations are available as\ncovariates.\n\n\\subsection{Mechanical Testing}\nQuasi-static compression is performed on a screw-driven load frame at\na fixed nominal strain rate, with force--displacement recorded to\ndensification. Drop-weight impact testing uses an instrumented tup at\na fixed impact energy; transmitted force is captured at high sample\nrate, and specimens are imaged before and after impact to document\nfailure modes. From each test we extract $F_\\mathrm{peak}$, SEA per\nunit mass, and compaction efficiency $\\eta$, defined consistently\nacross the campaign. A control specimen from an earlier batch is\nretested periodically to monitor TPU batch-to-batch and printer drift.\n\n\\section{Expected Outcomes and Discussion}\nWe will report how BO-selected designs evolve across iterations, how\nGP surrogate predictions and uncertainties change as data accumulate,\nand which Pareto-efficient trade-offs emerge between SEA and peak\ntransmitted force within the explored design space. We will also\ndiscuss practical lessons for operating the closed-loop workflow,\nincluding handling of print failures, batch-to-batch variability in\nTPU response, the influence of the rigid--flexible interface on\nenergy absorption, and the exploration--exploitation balance of the\nacquisition function under realistic experimental noise. We expect\nthe campaign to clarify where physical experimentation is genuinely\nrequired versus where simpler analytical or multi-fidelity surrogate\nshortcuts~\\citep{mo2023accelerated} could be\nsubstituted in future work.\n\n\\section{Conclusion}\nThis work demonstrates a partially automated, experiment-driven\nclosed-loop pipeline for designing multi-material 3D-printed\ntensegrity-inspired energy absorbers, with surrogate updating and\ncandidate selection automated and fabrication and testing kept manual.\nBy updating a GP surrogate directly from physical measurements, the\nworkflow avoids direct dependence on calibrated finite-element\nsimulation for objective evaluation while still providing principled,\nmulti-objective design recommendations. The same closed-loop pattern is\nexpected to transfer to other multi-material additively manufactured\narchitectures whose performance is dominated by hard-to-simulate\neffects.\n\n{\\tiny\n\\renewcommand{\\bibsection}{\\section*{\\normalsize References}\\vspace{-0.5em}}\n\\setlength{\\bibsep}{-0.05em plus 0.1ex}\n% Render DOIs as clickable doi.org hyperlinks for easy spot-checking;\n% keep \\url{} as a clickable hyperlink (default natbib behavior).\n\\providecommand{\\doi}[1]{}\n\\renewcommand{\\doi}[1]{\\href{https://doi.org/#1}{doi:\\,#1}}\n\\bibliography{references}\n}\n\n\\end{document}\n\n===== END idetc-abstract.tex =====\n\n===== BEGIN doumont-video-notes.md =====\n# Notes on Jean-luc Doumont \u2014 \"Creating Effective Slides\"\n\nVideo: (assigned in #83)\n\nTranscribed from @me-madsen's handwritten notes\n([Notes on Doumont's Presentation.pdf](https://github.com/user-attachments/files/30102186/Notes.on.Duomont.s.Presentation.pdf),\ndated 2026-07-15).\n\n## Overview\n\nA presentation is all about the right content, displayed the right way, at the\nright time.\n\n- Verbal communication is sequential.\n- Message is *interpretation* of the information.\n - Ensure the audience can get the most out of what you're doing.\n - Don't just give them information \u2014 help them understand it, interpret it,\n and internalize it.\n\n## Doumont's suggested rules for a presentation\n\n0. Ensure you have **messages** for your audience, not just words.\n1. **Adapt to your audience.**\n - Think like the audience, not the speaker.\n2. **Maximize signal-to-noise ratio.**\n - Reduce noise (animations, laser pointers, anything that pulls attention\n away from your message).\n3. **Use effective redundancy.**\n - Tell your message in word *and* on slide: someone deaf should understand\n your messages just as well as someone blind.\n - Ineffective redundancy is clutter appearing on every slide (e.g., the\n date).\n\n## When creating slides\n\n- If the audience doesn't need it, why put it on the slides?\n- People shouldn't have to count the items on a slide to know how many are\n there.\n- Don't use your slides as your \"presentation notes.\"\n - Don't show your notes to the audience.\n - **A bad slide is worse than no slide at all.**\n- **One message per slide** is a good rule of thumb.\n - Prioritize the *so what* (or *therefore what*) of your message.\n- Make it easy to interpret the information on the slide:\n - 3\u20135 items at most.\n - Align the title left.\n - Do not clutter slides with repetitive or unnecessary information: logos,\n affiliation, date, slide numbers.\n- Doumont's suggested workflow:\n 1. **Start with the title.**\n - Use a full sentence in the title.\n - The title is the main message of the slide.\n - Make it easy to read: left-aligned, with intentional line breaks.\n 2. **Do the visual next.** Optimize for viewing / taking in the message.\n- Try printing the slideshow as a handout, 6 per page, to test visibility.\n\n## General preparation\n\n- Planning for audience, content, and structure matters more than materials\n (i.e., slides).\n- Try using one sheet of paper for presentation preparation.\n- It's most important that your presentation is:\n - well planned, structured, and delivered;\n - **if you don't have a lot of time to prepare, don't focus on making\n slides.**\n- Practice the presentation a few times. Practice at least once *without* your\n slideshow!\n- If doing a handout, keep it separate from the slides you're preparing.\n- Impress the audience with your body.\n - Use gestures, not laser pointers.\n - \"Control\" the audience with your body \u2014 keep their attention on your\n presentation.\n\n## Impressions / what to apply to the IDETC presentation\n\n\n\n## Questions / things to discuss\n\n\n\n===== END doumont-video-notes.md =====\n", + "started_at": "2026-07-18T17:54:15.111220Z", + "crow": "job-futurehouse-data-analysis-crow-high", + "notification_enabled": false, + "notification_type": null, + "continued_trajectory_id": null, + "is_trajectory_owner": true, + "gcloud_operation_name": "sandbox://4949af58-f08a-4541-ad1c-1f0a95c6f07d/sandbox-claim-ff2b11d8/job-futurehouse-data-analysis-crow-high-agent-warmpool-mw6d7", + "task_summary": "Closed-Loop Bayesian Optimization and Multi-Material 3D Printing to Rapidly Optimize Tensegrity-Inspired Impact Energy Absorbers", + "id": "4949af58-f08a-4541-ad1c-1f0a95c6f07d" +} \ No newline at end of file diff --git a/presentation/edison-mock-audience/trajectory-files.json b/presentation/edison-mock-audience/trajectory-files.json new file mode 100644 index 00000000..bdcc138b --- /dev/null +++ b/presentation/edison-mock-audience/trajectory-files.json @@ -0,0 +1,82 @@ +{ + "data": [ + { + "id": 25056311, + "data_storage_id": "e778509b-d983-4bd9-b187-9031457dcd51", + "operation": "create", + "actor_id": "4949af58-f08a-4541-ad1c-1f0a95c6f07d", + "actor_type": "trajectory", + "timestamp": "2026-07-18T17:57:27.257750Z", + "data_storage": { + "name": "4949af58-f08a-4541-ad1c-1f0a95c6f07d.ipynb", + "description": "", + "is_collection": false, + "status": "active", + "content": null, + "embedding": null, + "user_id": "UxTMERa8SbeYkryBDMlwEgoJNF53", + "label": "a52770c895d3", + "parent_id": null, + "dataset_id": "fa7bc6f1-47f0-42e0-8b72-38c56221529d", + "project_id": null, + "share_status": "private", + "short_alias": null, + "id": "e778509b-d983-4bd9-b187-9031457dcd51", + "path": "a52770c895d3", + "created_at": "2026-07-18T17:57:27.184372Z", + "modified_at": "2026-07-18T17:57:28.642701Z", + "tags": null, + "file_path": "/workspace/notebooks/4949af58-f08a-4541-ad1c-1f0a95c6f07d.ipynb", + "metadata": { + "trajectory_id": "4949af58-f08a-4541-ad1c-1f0a95c6f07d", + "crow_name": "data analysis crow", + "job_data": [], + "size": 184 + }, + "trajectory_id": null, + "project_name": null, + "project_description": null, + "is_kosmos_project": null + } + }, + { + "id": 25056308, + "data_storage_id": "6632709d-0013-44de-b9b0-2f364dc3db9d", + "operation": "create", + "actor_id": "4949af58-f08a-4541-ad1c-1f0a95c6f07d", + "actor_type": "trajectory", + "timestamp": "2026-07-18T17:57:25.648098Z", + "data_storage": { + "name": "4949af58-f08a-4541-ad1c-1f0a95c6f07d_memory_heron_c0_20260718T175417754536.jsonl", + "description": "", + "is_collection": false, + "status": "active", + "content": null, + "embedding": null, + "user_id": "UxTMERa8SbeYkryBDMlwEgoJNF53", + "label": "1cdd10d8a229", + "parent_id": null, + "dataset_id": "07c2d4ef-d5ee-451b-961f-98080e4696b7", + "project_id": null, + "share_status": "private", + "short_alias": null, + "id": "6632709d-0013-44de-b9b0-2f364dc3db9d", + "path": "1cdd10d8a229", + "created_at": "2026-07-18T17:57:25.580512Z", + "modified_at": "2026-07-18T17:57:27.054713Z", + "tags": null, + "file_path": "/workspace/memory/4949af58-f08a-4541-ad1c-1f0a95c6f07d_memory_heron_c0_20260718T175417754536.jsonl", + "metadata": { + "trajectory_id": "4949af58-f08a-4541-ad1c-1f0a95c6f07d", + "crow_name": "data analysis crow", + "job_data": [], + "size": 106342 + }, + "trajectory_id": null, + "project_name": null, + "project_description": null, + "is_kosmos_project": null + } + } + ] +} \ No newline at end of file diff --git a/presentation/edison_mock_audience_job.py b/presentation/edison_mock_audience_job.py new file mode 100644 index 00000000..35afd219 --- /dev/null +++ b/presentation/edison_mock_audience_job.py @@ -0,0 +1,193 @@ +"""Submit the IDETC presentation outline to Edison Scientific (analysis job) +for simulated mock-audience feedback, poll until done, and save all artifacts. + +Run from the repo root. Requires EDISON_API_KEY in the environment. +""" + +import json +import os +import sys +import time +from pathlib import Path + +from edison_client import EdisonClient, JobNames +from edison_client.models import RuntimeConfig, TaskRequest + +REPO = Path(__file__).resolve().parents[1] +OUTDIR = REPO / "presentation" / "edison-mock-audience" +OUTDIR.mkdir(parents=True, exist_ok=True) + +FILES = [ + str(REPO / "presentation" / "doumont-presentation-template.md"), + str(REPO / "presentation" / "doumont-video-notes.md"), + str(REPO / "idetc-abstract.tex"), +] + +QUERY = """\ +You are simulating a mock conference audience for a ~15-minute technical talk at +IDETC-CIE 2026 (ASME International Design Engineering Technical Conferences), +Design Automation Conference track (DAC-10: design optimization applications). + +Attached files: +1. doumont-presentation-template.md — the presentation outline to evaluate, + structured per Jean-luc Doumont's opening/body/closing template. This is the + artifact under review. Evaluate the talk a competent presenter would deliver + from this outline (slides not yet made; an "Evidence/results" slot is a + placeholder while the experimental campaign is in progress). +2. idetc-abstract.tex — the submitted conference abstract, for technical + grounding of the project's claims (closed-loop Bayesian optimization of + multi-material 3D-printed tensegrity-inspired energy absorbers, optimized + directly from physical impact tests; qNEHVI; objectives = specific energy + absorption and compaction efficiency subject to a peak transmitted-force cap). +3. doumont-video-notes.md — the presenter's notes on Doumont's presentation + principles (messages not words, adapt to audience, one message per slide, + signal-to-noise). Use these as part of the evaluation rubric. + +Simulate the following SIX audience personas, chosen to span familiarity with +the base techniques (Bayesian optimization, tensegrity structures, additive +manufacturing), professional background, and level of skepticism: + +P1. "The skeptical BO insider" — design-automation professor, 15 years in + surrogate-based and Bayesian optimization; reviews for DAC. Expert in BO, + moderate on AM, low on tensegrity. HIGHLY SKEPTICAL of applied-BO talks: + default assumption is "off-the-shelf qNEHVI applied to yet another + application." Will probe methodological novelty, experiment budget, + baselines, and noise handling. +P2. "The aerospace practitioner" — senior EDL (entry, descent, landing) + engineer from industry. Deep expertise in impact attenuation and lander + hardware; NO familiarity with BO or ML jargon; knows tensegrity from the + Super Ball Bot era. MODERATELY SKEPTICAL, practicality-focused: velocity + regimes, scaling from desktop specimens to flight hardware, TRL, + qualification. +P3. "The AM/materials researcher" — mid-career researcher in multi-material + FDM and elastomer printing. Expert in AM/TPU, novice in BO and tensegrity. + NEUTRAL/CURIOUS but will probe PLA-TPU interfacial bonding, print + repeatability, batch-to-batch variation, and whether "no assembly" holds. +P4. "The first-year grad student" — new to ALL three base techniques. + ENTHUSIASTIC, low skepticism. Tests followability: where does the talk lose + a novice, which jargon lands unexplained, does the story arc carry them? +P5. "The FEA veteran" — computational structural mechanics researcher, 25 + years of finite-element work, expert in simulation, low familiarity with + BO. DEFENSIVE AND SKEPTICAL of the claim "simulation can't be trusted for + these structures": will push back that models can be calibrated, and ask + why the authors didn't try harder before abandoning simulation. +P6. "The friendly industry generalist" — design engineer attending to scout + useful methods. Moderate familiarity with optimization and AM, none with + tensegrity. SUPPORTIVE, low skepticism, limited attention: tests + memorability and the practical so-what ("could my team use this Monday?"). + +For EACH persona produce: +(a) a first-person reaction to the talk as outlined: what landed, what confused + or lost them, where their attention drifted; +(b) the main message as they would repeat it to a colleague the next day, in + their own words — noting any distortion from the intended message ("By + closing the loop between multi-material 3D printing and Bayesian + optimization, we can optimize tensegrity energy absorbers directly from + real impact data — in dozens of prints, not thousands"); +(c) their top 3 Q&A questions, in character; +(d) their single most pointed objection or challenge, and how damaging it is if + the presenter has no good answer. + +Then produce a SYNTHESIS section: +- cross-persona themes (what multiple personas stumbled on or attacked); +- the 3 highest-priority revisions to the outline, concretely worded; +- claims in the outline that need evidence, hedging, or a prepared backup slide + for Q&A; +- which persona the current outline serves best and worst, and whether that is + the right trade-off for the DAC-10 audience; +- predicted overall reception on a 1-10 scale with one-sentence rationale. + +Write the whole result as a well-structured markdown report. +""" + + +def main() -> None: + api_key = os.environ.get("EDISON_PLATFORM_API_KEY") or os.environ["EDISON_API_KEY"] + client = EdisonClient(api_key=api_key) + + # Official upload flow per + # https://docs.edisonscientific.com/edison-client/file-management#upload: + # store each file first, then reference the returned data_storage ids via + # runtime_config.environment_config["data_storage_uris"]. (Passing + # files=... to create_task is NOT the documented path and failed twice + # with a dead sandbox and no failure reason.) + storage_uris = [] + for path in FILES: + resp = client.store_file_content( + name=Path(path).name, + file_path=path, + description="Input document for IDETC mock-audience analysis", + ) + storage_uris.append(f"data_entry:{resp.data_storage.id}") + print(f"uploaded {Path(path).name} -> {resp.data_storage.id}", flush=True) + + task_data = TaskRequest( + name=JobNames.ANALYSIS, + query=QUERY, + runtime_config=RuntimeConfig( + environment_config={"data_storage_uris": storage_uris}, + ), + ) + task_ids = client.create_task(task_data) + task_id = task_ids[0] if isinstance(task_ids, (list, tuple)) else task_ids + task_id = str(task_id) + (OUTDIR / "task-id.txt").write_text(task_id + "\n") + print(f"submitted task {task_id}", flush=True) + + time.sleep(600) # initial wait per repo guidance + deadline = time.time() + 45 * 60 + while True: + try: + status = str(client.get_task(task_id).status).lower() + except Exception as exc: # transient API hiccups shouldn't kill the poll + print(f"poll error: {exc}", flush=True) + status = "unknown" + print(f"status: {status}", flush=True) + if any(s in status for s in ("success", "fail", "cancel", "error")): + break + if time.time() > deadline: + print("timed out waiting for task", flush=True) + break + time.sleep(300) + + verbose = client.get_task(task_id, verbose=True, history=True) + payload = verbose.model_dump(mode="json") + (OUTDIR / "task-response.json").write_text(json.dumps(payload, indent=2, default=str)) + + # Pull out the human-readable answer wherever the frame put it. + answer = None + frame = payload.get("environment_frame") or {} + + def hunt(node): + found = [] + if isinstance(node, dict): + for k, v in node.items(): + if k in ("answer", "formatted_answer", "final_answer") and isinstance(v, str) and len(v) > 200: + found.append(v) + found.extend(hunt(v)) + elif isinstance(node, list): + for v in node: + found.extend(hunt(v)) + return found + + candidates = hunt(frame) + hunt({k: v for k, v in payload.items() if k != "environment_frame"}) + if candidates: + answer = max(candidates, key=len) + (OUTDIR / "mock-audience-feedback.md").write_text(answer) + print("answer saved", flush=True) + else: + print("no answer field found; inspect task-response.json", flush=True) + + # Fetch any trajectory artifacts. + try: + files = client.list_files(task_id) + (OUTDIR / "trajectory-files.json").write_text(json.dumps(files, indent=2, default=str)) + print(f"trajectory files listed: {files}", flush=True) + except Exception as exc: + print(f"list_files failed: {exc}", flush=True) + + print("done", flush=True) + + +if __name__ == "__main__": + main() diff --git a/presentation/emc-bo-block.pptx b/presentation/emc-bo-block.pptx new file mode 100644 index 00000000..75dd5762 Binary files /dev/null and b/presentation/emc-bo-block.pptx differ diff --git a/presentation/emc2026-bare-template.pptx b/presentation/emc2026-bare-template.pptx new file mode 100644 index 00000000..fe229a5d Binary files /dev/null and b/presentation/emc2026-bare-template.pptx differ diff --git a/presentation/emc2026-idetc-demo-previews/slide-1.png b/presentation/emc2026-idetc-demo-previews/slide-1.png new file mode 100644 index 00000000..2dfab2e6 Binary files /dev/null and b/presentation/emc2026-idetc-demo-previews/slide-1.png differ diff --git a/presentation/emc2026-idetc-demo-previews/slide-2.png b/presentation/emc2026-idetc-demo-previews/slide-2.png new file mode 100644 index 00000000..e840230b Binary files /dev/null and b/presentation/emc2026-idetc-demo-previews/slide-2.png differ diff --git a/presentation/emc2026-idetc-demo-previews/slide-3.png b/presentation/emc2026-idetc-demo-previews/slide-3.png new file mode 100644 index 00000000..ce978c20 Binary files /dev/null and b/presentation/emc2026-idetc-demo-previews/slide-3.png differ diff --git a/presentation/emc2026-idetc-demo.pptx b/presentation/emc2026-idetc-demo.pptx new file mode 100644 index 00000000..55df092a Binary files /dev/null and b/presentation/emc2026-idetc-demo.pptx differ diff --git a/presentation/feedback-video-2026-08-18.md b/presentation/feedback-video-2026-08-18.md new file mode 100644 index 00000000..6434df3c --- /dev/null +++ b/presentation/feedback-video-2026-08-18.md @@ -0,0 +1,616 @@ +# Feedback video walkthrough: idetc-2026.pptx (2026-08-18) + +Source: ["Feedback on idetc-2026.pptx"](https://youtu.be/gtaxKa_zZIo), 59 min 31 s, +unlisted, uploaded 2026-08-18 by Sterling Baird. Sterling and Marcus review the +IDETC deck slide by slide, making many edits live in desktop PowerPoint while +narrating. They state up front that the recording will "be parsed later into +kind of a spec sheet with Claude": this document is that spec sheet. + +How it was made: the video and its auto-generated transcript were downloaded +through the Raspberry Pi (YouTube blocks GitHub runners; the transcript needed a +~35 minute wait to finish processing). Screenshots were extracted at every +actionable moment and cross-read against the transcript, because the spoken +comments often refer to whatever the mouse is pointing at. Quotes below are +corrected for voice-to-text errors; the raw wording is preserved in the +[appendix](#appendix-voice-to-text-corrections) where the correction is not +obvious. Screenshots live in +[`presentation/feedback-video-2026-08-18/`](feedback-video-2026-08-18/), named +`NN-SSSS-*.jpg` where `SSSS` is the video timestamp in seconds. + +Two caveats: + +- **Slide numbers shifted during the session** (a new slide 2 was inserted at + [06:44](https://youtu.be/gtaxKa_zZIo?t=404), another after the assembly slide, + and five pulled-in slides landed mid-deck around + [40:00](https://youtu.be/gtaxKa_zZIo?t=2400)). Slides are therefore referenced + by title, not number. +- The auto-captions miss stretches of quiet editing, so timestamps for a few + items are the surrounding spoken context rather than the exact edit moment. + +## Open action items (quick list) + +Status column updated 2026-08-18 (evening) after the Claude-owned items were +applied to `idetc-2026.pptx` through the web editor and verified against the +re-downloaded stored file. Details of what changed are in +[Implementation update](#implementation-update-2026-08-18) below. + +| # | Item | Owner | Detail | Status | +|---|---|---|---|---| +| [2](#2-note-to-claude-check-the-title-against-the-symposium) | Check the final title against the symposium/session the abstract was submitted to, for audience fit | Claude | 05:23, 09:04 | Done; verdict below | +| [5](#5-the-concept-video-is-titan-not-mars) | Speaker note clarifying the concept video is a Titan mission, not Mars | Marcus/Claude | 10:35 | Done | +| [6](#6-credit-everything-in-the-speaker-notes) | Source link in speaker notes for every borrowed asset; missing for the planetary-lander video | Marcus/Claude | 11:49 | Done; one TODO left (side-table photo source) | +| [7](#7-titan-video-trim-and-title) | Trim the Titan video to just the fall; decide on removing the on-slide title | Marcus | pre-existing note, discussed 05:56 | Done (trimmed 20 s clip swapped in; slide has no title) | +| [9](#9-anatomy-slide-get-our-own-model) | Replace the Wikipedia tensegrity model with a photo/render of our own | Marcus | 13:58 | Open | +| [11](#11-unique-properties-slide-what-to-show) | Decide the visual for the hidden "unique properties" slide (throw video vs. no-grinding-flexure vs. impact) | Marcus + Sterling | 19:08 | Open | +| [12](#12-assembly-slide-keep-one-video) | Keep one assembly video, not two in sequence; play-once, silent, timelapsed | Marcus | 21:29 | Open | +| [16](#16-prior-work-slide-say-the-gap-out-loud) | Marcus to read the Pajunen paper and the Filipe material closely enough to present them correctly | Marcus | 32:14 | Open | +| [22](#22-24-the-two-data-slides-for-claude) | Rewrite the two data-slide titles (current ones "read like AI slop") | Claude | 58:26 | Done | +| [23](#22-24-the-two-data-slides-for-claude) | Cut the information density of those two slides | Claude | 58:46 | Done | +| [24](#22-24-the-two-data-slides-for-claude) | Attenuation slide: plot filtered data, not the raw comparison | Claude | 59:00 | Done | +| [24b](#22-24-the-two-data-slides-for-claude) | Axis labels: never "thousandths of a second (ms)"; write "time (ms)" | Claude | PR comment, 2026-08-18 | Done | +| [25](#25-speaker-notes-need-curation) | Curate speaker notes; Marcus to find his own voice | Marcus | 56:56 | Open | + +Everything else below was **resolved live during the recording** and is +documented so the next editor knows what changed and why. + +## Implementation update (2026-08-18) + +Applied to `idetc-2026.pptx` via the Office web editor (co-authoring, so the +edits merged with the live session) and verified by re-downloading the stored +file: + +- **Item 2, title vs. symposium.** The abstract was submitted to Track + **DAC-10: Design of Engineering Materials and Structures** (see + [idetc-cie-2026-tracks-topics.csv](../idetc-cie-2026-tracks-topics.csv) and + the header of [idetc-abstract.tex](../idetc-abstract.tex)). Its invited + topics include data-driven design of materials and structures, machine + learning methods, integrated design of products, fabrication processes, and + materials, and new experimental techniques that support design. The new + title ("Discovering multi-material, tensegrity-inspired energy absorbers via + closed-loop Bayesian optimization") fits that list well. Two small flags: + the title dropped "3D-printed", which the submitted abstract title contains + and which speaks directly to the process-integration topic, so consider + restoring it if the program prints both side by side. And the session + description leads with modeling and simulation, so expect a + simulation-minded audience; that shapes Q&A prep more than the title. +- **Items 22/23/24/24b, the data slides.** Titles are now "We measure the + jolt that reaches the top and how quickly the ringing fades" (both copies of + the two-part-story slide) and "Every recording goes through the same + crash-test filter (SAE J211)". All three figures were regenerated from the + committed 60 in validation campaigns (specimens 7xadt6 and 9GMQYQ, channel + map per the PR #86 branch docs: CH5 = base-plate input, CH2/CH3/CH4 = + top-vertex tri-axis) by + [regen_data_slide_figures.py](regen_data_slide_figures.py), with the + annotation layers cut to a legend and a one-line caption, and every time + axis labeled "time (ms) after the plate lands". The attenuation slide now + shows CFC-filtered traces for two specimens under identical conditions + instead of the raw two-sensor dump. Note for the next editor: the + two-part-story slide exists **twice** (slides 16 and 17, identical after + this pass); one of them should eventually be deleted. +- **Items 5/6/7, the opening videos.** The lander slide's video was replaced + with the trimmed 20 s fall clip + ([media/clip-titan-descent.mp4](media/clip-titan-descent.mp4), byte-exact + upload), placed full-bleed. Its speaker note now carries the NASA source + link, the Titan-not-Mars clarification, and the hook narration. Source + links were added to the speaker notes of every borrowed asset found in the + deck audit: the hook-slide media (Tandem Second Spine, NASA Super Ball Bot + poster), the Steve Mould 2D clip, both TensoLogic assembly videos, the + Pajunen figure, the Filipe photos (permission noted, no public link), the + Baird & Sparks Matter (2022) BO figures, and the future-applications images + (Al Sabouni-Zawadzka et al. 2025, Gu et al. 2026). One TODO remains: the + tensegrity side-table photo on the hook slide has no identified original + source yet. + +--- + +## Title + +### 1. Title reframed to match the submitted abstract + +[[00:16](https://youtu.be/gtaxKa_zZIo?t=16) to +[05:07](https://youtu.be/gtaxKa_zZIo?t=302)] +The deck opened with the Doumont-style title "Let's build better tensegrity +structures faster / by 3D printing multi-material, tensegrity-inspired +structures". Marcus: "I don't know if it's entirely accurate... we could frame +the presentation as hopes to eventually 3D print [true tensegrity] structures, +or as taking inspiration from tensegrity structures to build energy absorption +devices for other uses." They pulled up the submitted abstract title +("Closed-loop optimization of multi-material 3D-printed tensegrity-inspired +energy absorbers") and noted people will have read the published abstract. + +Sterling tried several rewrites directly on the **slide master** (in this +template the talk title lives on the master, not the slide): "Closed-loop...", +then "Leveraging AI & Automation to build...", before landing on +**"Discovering multi-material, tensegrity-inspired energy absorbers via +closed-loop Bayesian optimization"** ("better" was tried and dropped). Marcus at +[05:02](https://youtu.be/gtaxKa_zZIo?t=302): "Seems very accurate." + +![Original title slide](feedback-video-2026-08-18/01-0020-title-slide-before.jpg) +*[00:20] The deck as the session opened, with the old title.* + +![Master edit, closed-loop attempt](feedback-video-2026-08-18/02-0140-master-title-edit-closed-loop.jpg) +*[02:20] Editing the title on the slide master: "Closed-loop" attempt.* + +![Master edit, AI and automation attempt](feedback-video-2026-08-18/03-0240-master-title-edit-ai-automation.jpg) +*[04:00] "Leveraging AI & Automation" attempt, later discarded.* + +![Final title](feedback-video-2026-08-18/04-0342-title-final.jpg) +*[05:42] The title that stuck.* + +### 2. Note to Claude: check the title against the symposium + +[[05:23](https://youtu.be/gtaxKa_zZIo?t=323), restated at +[09:04](https://youtu.be/gtaxKa_zZIo?t=544)] +"Make a note to Claude to look back at the symposium that it was sent to... +check the title against the symposium and the potential audience for that, for +fit." **Open item for Claude**: verify the new title reads well for the specific +IDETC-CIE session (DAC) the abstract was submitted to, and flag any mismatch. + +--- + +## Opening videos + +### 3. NASA 360 "baby toy" clip added on a new slide before the Titan video + +[[08:17](https://youtu.be/gtaxKa_zZIo?t=497) to +[10:30](https://youtu.be/gtaxKa_zZIo?t=630)] +Scrolling the Super Ball Bot video table in PR #84, Sterling: "This one, I +think... I love that." He inserted a new slide just before the planetary-lander +concept video for a clip of [NASA 360 Talks - Super Ball +Bot](https://www.youtube.com/watch?v=0eC4A2PXM-U): "just this clip of one of the +scientists there talking about the inspiration for the Super Ball Bot", from +about **0:10 to 0:29**, the researcher who explains they make tensegrities as +baby toys, throws one on the ground, and says "hey, that's a landing robot." +Requirements stated: **video full screen, no title, take up the entire slide.** + +Status: done live. Sterling downloaded the video, inserted it on the new slide, +put the YouTube link in the speaker notes, and trimmed it with PowerPoint's Trim +Video dialog ([16:51](https://youtu.be/gtaxKa_zZIo?t=1011) to +[18:44](https://youtu.be/gtaxKa_zZIo?t=1124): "Got that added in. Claude, you +don't need to add that in now."). Worth one later check that the video frame is +truly full-bleed. + +![PR video table](feedback-video-2026-08-18/07-0500-pr84-video-table.jpg) +*[08:20] Picking the clip from the verified video table in PR #84.* + +![The toy moment](feedback-video-2026-08-18/08-0585-nasa360-toy-moment.jpg) +*[09:45] The moment being clipped: researcher holding the baby-toy tensegrity.* + +![Link in notes](feedback-video-2026-08-18/09-0622-new-slide2-youtube-link-in-notes.jpg) +*[10:22] New slide with the YouTube link recorded in the speaker notes.* + +![Trim dialog](feedback-video-2026-08-18/16-1075-nasa-clip-trim-dialog.jpg) +*[17:55] Trimming the inserted clip in PowerPoint.* + +![Clip inserted](feedback-video-2026-08-18/17-1122-slide2-nasa-clip-inserted.jpg) +*[18:42] The clip in place on the new slide.* + +### 4. Cap on passive video time + +[[18:04](https://youtu.be/gtaxKa_zZIo?t=1084)] Marcus, on showing the clip: +"I wouldn't do more than maybe a minute or so of them just watching a video." +Keep total played-video time in the talk to roughly a minute. + +### 5. The concept video is Titan, not Mars + +[[05:45](https://youtu.be/gtaxKa_zZIo?t=345) and +[10:35](https://youtu.be/gtaxKa_zZIo?t=635)] +The Aug 7 exchange feedback asked "Why are you passing Saturn on the way to +Mars?" Both presenters had assumed Mars. Resolution: the NIAC animation is for a +mission concept to **Titan, a moon of Saturn**. "For [this] slide, we just need +to make sure in the speaker notes it's clear that this is not a Mars [mission]; +the concept video is for a moon of Saturn as part of some mission, I think the +Titan mission. And don't put that anywhere on the slide." **Open**: that speaker +note still needs to be written. + +![Titan slide with notes](feedback-video-2026-08-18/05-0356-titan-slide-and-notes.jpg) +*[05:56] The concept-video slide; existing notes already say "Remove title?" and "Replace this video with a downloaded and trimmed version of just the fall."* + +![Titan landing frame](feedback-video-2026-08-18/06-0378-titan-video-scrubbed-to-landing.jpg) +*[06:18] Scrubbing to the landing: the tensegrity lander bouncing on the surface.* + +### 6. Credit everything in the speaker notes + +[[11:03](https://youtu.be/gtaxKa_zZIo?t=663) to +[12:06](https://youtu.be/gtaxKa_zZIo?t=726)] +Debate on how to credit borrowed video without distracting from the hook. +Decision: "let's just make sure that the credit is in the speaker notes, +specifically the YouTube link. And I'm noticing it's not there for this +planetary lander one, for example. **Everything that was sourced from somewhere +at least needs the link in the speaker notes.**" A short on-slide credit (like +"Titan mission animation, NASA") was discussed and left optional. **Open**: the +planetary-lander video slide still lacks its link. + +![No title tooltip](feedback-video-2026-08-18/10-0662-titan-slide-no-title.jpg) +*[11:02] The lander-video slide during the credit discussion.* + +### 7. Titan video: trim and title + +Pre-existing presenter notes on the lander slide (visible at +[05:56](https://youtu.be/gtaxKa_zZIo?t=356)): "Remove title?" and "Replace this +video with a downloaded and trimmed version of just the fall." Still open; +discussed but not executed during the session. + +--- + +## Hook slide ("Tensegrity provides robust, reusable, solutions...") + +### 8. Second-spine and crutch examples stay + +[[12:13](https://youtu.be/gtaxKa_zZIo?t=733) to +[13:36](https://youtu.be/gtaxKa_zZIo?t=816)] +Sterling asked about the middle image (the Tandem Second Spine, a +tensegrity-based exoskeleton someone raised after meeting with one of the +professors; not on the market yet). Marcus: "We don't have to use that, but I +thought it would be good to talk about ways tensegrity is used besides +[landers]." Sterling: "No, this seems pretty good," with a side thought about +whether to show the research crutch tips earlier. No change made; keep the +three-example layout (tensegrity table, Tandem Second Spine, Super Ball Bot). + +![Hook slide](feedback-video-2026-08-18/11-0722-hook-slide-second-spine.jpg) +*[12:02] The hook slide under discussion.* + +--- + +## Anatomy slide ("Tensegrity structures are defined by rigid struts...") + +### 9. Anatomy slide: get our own model + +[[13:43](https://youtu.be/gtaxKa_zZIo?t=823) to +[14:29](https://youtu.be/gtaxKa_zZIo?t=869)] +Prompted by exchange feedback about making the structure easier to understand: +"We could either find a better model..." and, on the credit, "having Wikipedia +listed on a presentation [is not great]." The slide's own note already says +"Getting a model of our own, or not from Wikipedia, could be good lol." +Two fixes were made live: the "Cables are in tension" / "Struts are in +compression" labels were **recolored red and green to match the model** (the +colored-legend request from the exchange feedback), and the "Model courtesy of +Wikipedia" credit moved off the slide into the speaker notes. **Open**: replace +the Wikipedia render with a photo or render of one of our own structures. + +![Anatomy before](feedback-video-2026-08-18/12-0820-anatomy-slide-wikipedia-note.jpg) +*[13:40] Anatomy slide before the edits, Wikipedia credit on-slide.* + +![Anatomy after](feedback-video-2026-08-18/13-0872-anatomy-labels-color-coded.jpg) +*[14:32] Labels color-matched to the model; credit moved to the notes.* + +### 10. The 2D teaching clip is now in the deck + +[[14:43](https://youtu.be/gtaxKa_zZIo?t=883) to +[16:58](https://youtu.be/gtaxKa_zZIo?t=1018)] +"There is a video here that I thought would be good to add... a snippet from one +of the videos about learning about tensegrity, and it had a 2D [model]." Then, +finding the prepared snips unused: "This blew my mind a little. Come on, I even +gave you snippets and you didn't use them, like, the Claude, for some of these. +These videos really need to be added, and these can be just direct-downloaded +from the Box link or pulled from YouTube. I'll go ahead and just download this +one and bring it in myself." He pulled `youtube-0onncd0_0-o.mp4` (the 34 s Steve +Mould 2D-tensegrity snip) from the Box `tensegrity` folder and placed it on its +own slide right after the anatomy slide. + +![Box download](feedback-video-2026-08-18/14-0958-box-2d-snip-download.jpg) +*[15:58] Saving the 2D snip from the Box folder.* + +![On the slide](feedback-video-2026-08-18/15-1012-2d-clip-on-slide6.jpg) +*[16:52] The Steve Mould 2D model clip landed on its own slide (thumbnail panel).* + +--- + +## Unique-properties slide ("Tensegrity's unique properties make it ideal...") + +### 11. Unique-properties slide: what to show + +[[19:08](https://youtu.be/gtaxKa_zZIo?t=1148) to +[21:04](https://youtu.be/gtaxKa_zZIo?t=1264)] +Sterling: "Do we have a video of us throwing it?" Marcus: there is a hallway +throw video "but it's not a very pretty video"; he could instead film a close-up +throw against a wall. Sterling's counter: "It might be helpful instead to show +how, when it flexes, there are no mechanical parts grinding against each other. +I guess it depends on what we want to show. We can show the impact..." The +slide's existing note also suggests a video of throwing the basement specimen. +No resolution; Sterling **hid the slide for now** via right-click > Hide Slide. +**Open**: pick the visual (throw video, flexing close-up, or impact) and unhide. + +![Slide with throw notes](feedback-video-2026-08-18/18-1180-slide7-throwing-video-notes.jpg) +*[19:40] The slide and its presenter note proposing a throwing video.* + +![Hide slide](feedback-video-2026-08-18/19-1259-slide7-hide-slide-menu.jpg) +*[20:59] Hiding the slide until the visual question is settled.* + +--- + +## Assembly slide ("Currently, tensegrity design and assembly is slow...") + +### 12. Assembly slide: keep one video + +[[21:29](https://youtu.be/gtaxKa_zZIo?t=1289) to +[22:42](https://youtu.be/gtaxKa_zZIo?t=1362)] +Marcus: "I found these videos because I wanted a way to show how complex it gets +to assemble a structure... there's two videos on that slide, one shows right +after the other. I'm fine deleting [one]." Sterling: "I think we can probably +get rid of this [one] for now and rely a little more on this one." The slide's +own note already asks for a **downloaded, timelapsed version set to play once, +silent**, and to consider a static image of the iteration spinner "so that it +doesn't become distracting, because it's fun to watch it spin." + +Related, at [47:49](https://youtu.be/gtaxKa_zZIo?t=2869): "I can put a version +of the video that doesn't [have the] speed[-up] on it." A **12x-speed** version +of the TensoLogic assembly video now sits on its own slide next to the 48x one, +captioned "Tensegrity design and assembly is slow and tedious." One of the two +slides should ultimately go. + +![Assembly slide notes](feedback-video-2026-08-18/20-1244-slide8-assembly-video-notes.jpg) +*[20:44] The assembly slide with its play-once/silent/timelapse note.* + +![12x version](feedback-video-2026-08-18/32-2872-slide9-12x-speed.jpg) +*[47:52] The added 12x-speed variant.* + +--- + +## Campaign structure + +### 13. Walk the loop stage by stage (implemented by pulling SDL slides) + +[[24:20](https://youtu.be/gtaxKa_zZIo?t=1460) to +[26:12](https://youtu.be/gtaxKa_zZIo?t=1572)] +Marcus's structural idea for the methods block: "We wanted to talk about how we +initialize the campaign... we could isolate each aspect: here's how we +initialized it, here's how we're suggesting the next experiments, here's how +we're actually making them and testing it." Sterling: "I think that'd be a great +way to break it up," noting the difficulty that the pieces interrelate. Plan: if +done, "introduce that earlier on" with a roadmap, then walk each stage; +"introduce it between slides 10 and 11" (numbering at that moment). + +Implemented at [38:16](https://youtu.be/gtaxKa_zZIo?t=2296) onward ("I think I'm +going to pull a slide from somewhere else"): Sterling opened his other decks and +pulled in the SDL block: "Combining AI and automation accelerates scientific +discovery...", the design-make-test loop slide (retitled "The loop of +scientific discovery is closed through design-make-test cycles"), and the +per-stage slides ("To initialize means to define your materials discovery +task...", "During the design phase, one 'acquires' new designs to run...", "To +make means to perform all synthesis and processing steps..."). The existing +loop-diagram slide was retitled **"We built a closed-loop system to optimize +energy and shock response as a function of geometry and 3D print processing +parameters."** + +![Loop diagram slide](feedback-video-2026-08-18/21-1462-closed-loop-diagram-slide.jpg) +*[24:22] The closed-loop diagram slide the discussion started from; its note says to use it to introduce Bayesian optimization as the missing step in the loop.* + +![Opening Draft 2](feedback-video-2026-08-18/28-2300-open-draft2-dialog.jpg) +*[38:20] Hunting for source slides in other decks.* + +![AI and automation slide](feedback-video-2026-08-18/29-2500-ai-automation-slide-pulled.jpg) +*[41:40] Pulled slide: AI maximizes value of experiments; automation minimizes burden.* + +![SDL loop slides](feedback-video-2026-08-18/30-2640-sdl-loop-slides-pulled.jpg) +*[44:00] The loop slide being retitled, with the per-stage slides visible below.* + +--- + +## Prior-work slide + +### 14. Permissions for the prior-art photos + +[[27:08](https://youtu.be/gtaxKa_zZIo?t=1628) to +[27:47](https://youtu.be/gtaxKa_zZIo?t=1667)] +The dual-filament red/orange tensegrity photos are from work that was never +published as a paper; the author was contacted and "was okay with this," asking +to be credited a specific way, now on the slide: "Image courtesy of Filipe +Amarante dos Santos, NOVA University Lisbon, Portugal." Keep that credit intact. + +### 15. Citation format fixed, and the citation-style rule + +[[27:47](https://youtu.be/gtaxKa_zZIo?t=1667) to +[29:25](https://youtu.be/gtaxKa_zZIo?t=1765)] +The mono-filament image's citation read "Pajunen, et. al." with no venue or +year. (The transcript mangles this badly; see the appendix.) Sterling: "'et. +al.' is really weird, it's not actually an abbreviation." They looked up the +journal (Extreme Mechanics Letters, ISO 4 abbreviation "Extreme Mech. Lett.") +and set the citation to **"Pajunen et al. *Extreme Mech. Lett.* (2021)"**. +Style rule stated at [29:10](https://youtu.be/gtaxKa_zZIo?t=1750): "This is my +preference for how we do citations: good that it's in gray, kind of +deemphasized," on the slide next to the item it credits. Also noted at +[27:53](https://youtu.be/gtaxKa_zZIo?t=1673), scoping: "Actually, most of these +I think we're just going to make in real time. I probably won't have Claude do +too much with it." + +![Pajunen search](feedback-video-2026-08-18/23-1692-pajunen-google-search.jpg) +*[28:12] Chasing down the Pajunen citation.* + +![EML abbreviation](feedback-video-2026-08-18/24-1734-eml-abbreviation-search.jpg) +*[28:54] Confirming the ISO 4 journal abbreviation.* + +![Gray citation](feedback-video-2026-08-18/25-1792-citation-gray-italic.jpg) +*[29:52] The corrected, deemphasized citation in place.* + +### 16. Prior-work slide: say the gap out loud + +[[29:31](https://youtu.be/gtaxKa_zZIo?t=1771) to +[38:16](https://youtu.be/gtaxKa_zZIo?t=2296)] +The longest discussion of the session. Key points, in order: + +- The dual-filament example is "pre-tensioned multi-material," but its parts are + **printed separately and then assembled**; the intent behind it was building + toward lattice structures. The mono-filament example is printed as one piece. + "One of the differences with ours is we're printing it together as one + object... so it's kind of like a combination of those two" + ([35:57](https://youtu.be/gtaxKa_zZIo?t=2157)). +- Marcus's framing for the slide: tensegrity's unique characteristics are ideal + but hard to use in all cases, which led to tensegrity-inspired devices that + keep some characteristics and drop others, yet still offer real uses + (deliver a UPS parcel intact, crutch tips). +- Sterling pushed for the explicit Doumont move: "We're going to say why this + was necessary relative to prior work... what's the need, what have people done + before, and how does our work address the gap?" The crisp gap statement: + "**No prior work has printed a multi-material [tensegrity] structure as [one + object]. That one very specific one we found is multi-material, but it's not + printed together, it's assembled**" ([37:05](https://youtu.be/gtaxKa_zZIo?t=2225)). +- On novelty, reporting the advisor's view: "he seems pretty adamant that... + no one cares about T3 prisms; [the] T3 prism [itself] is [not the] novel + part... part of the novelty is the whole optimization process with it as + well" ([37:50](https://youtu.be/gtaxKa_zZIo?t=2270)). +- They opened the actual [Pajunen et al. 2021 + paper](https://daraio.caltech.edu/publications/Pajunen_EML_2021.pdf) to + describe it correctly (laser vibrometer, longitudinal wave excitation, + transmissibility, precompression-induced tunability). Marcus at + [32:14](https://youtu.be/gtaxKa_zZIo?t=1934): "I need to read through this and + double-check that I understand what they're doing correctly." **Open item.** + +The slide ended the session as: title "3D-printed tensegrity and +tensegrity-inspired structures offer unique, tunable mechanical properties"; +captions "Mono-filament tensegrity inspired-structure with tunable wave +propagation characteristics" and "Two-material tensegrity structure for modular +lattice structures." + +![Pajunen figure](feedback-video-2026-08-18/26-2052-pajunen-paper-fig4.jpg) +*[34:12] Reading the Pajunen paper's transmissibility figure before describing it on the slide.* + +![Final prior-work slide](feedback-video-2026-08-18/27-2192-prior-work-slide-final.jpg) +*[36:32] The prior-work slide after the rewrite.* + +--- + +## Proposal figure and printing slides + +### 17. MRG proposal figure pulled in + +[[45:00](https://youtu.be/gtaxKa_zZIo?t=2700) to +[45:44](https://youtu.be/gtaxKa_zZIo?t=2744)] +"[This] might be a good spot for the proposal... one of the figures from the +proposal." One figure "did make it in here," and a raw version exists as well. +Sterling opened `mrg-jeff-sterling-2026.pptx` and took the workflow figure +(design-space thumbnails, BO surrogate panel, the loop, dual-nozzle +multi-material printing, validation and mechanical-testing panels). + +![Proposal figure](feedback-video-2026-08-18/31-2748-mrg-proposal-figure.jpg) +*[45:48] The proposal workflow figure being lifted from the MRG deck.* + +### 18. Dual-nozzle slide (context) + +The slide "Dual-nozzle printing allows us to take a step towards pre-assembled, +3D-printed tensegrity structures" carries the caption "Tensegrity-inspired T3 +prisms (not pre-tensioned)" and a note about the lack of purely tensioned +members being future work. It was touched during the session (title line break, +caption) but its message was not challenged. + +![Dual-nozzle slide](feedback-video-2026-08-18/22-1650-dual-nozzle-slide.jpg) +*[27:30] The dual-nozzle printing slide.* + +--- + +## Testing slides + +### 19. Drop-tower slide uses the wide-angle photo + +[[55:00](https://youtu.be/gtaxKa_zZIo?t=3300) to +[55:15](https://youtu.be/gtaxKa_zZIo?t=3315)] +Marcus: "I also have wide angle..." Sterling: "...this one. I see. I think this +is [good]." The slide "We use a drop tower to measure the mechanical shock +response" now shows the wide room shot, title set over the photo. Its speaker +note explains the test in plain terms (60 inch drop onto felt, two sensors, +1.25 M readings/s) and ends with the bridge "→ Introducing Bayesian +Optimization." + +![Drop tower slide](feedback-video-2026-08-18/33-3312-drop-tower-slide.jpg) +*[55:12] The wide-angle drop-tower slide.* + +### 20. Keep the sound on the slow-motion drop video + +[[54:44](https://youtu.be/gtaxKa_zZIo?t=3284)] +"You want to keep the sound?" "Yeah, I think so." The drop clip on "We use +accelerometers and slow-motion capture to gather real data on these 3D-printed +specimens" plays with audio. (Session-room AV check accordingly; the exchange +feedback separately asked to mute stray computer audio, so intentional sound +needs a deliberate volume check.) + +![Slow-mo slide](feedback-video-2026-08-18/34-3302-accel-slomo-slide-sound.jpg) +*[55:02] The accelerometers + slow-motion slide with the clip playing.* + +### 21. "Accelerometers" label added to the specimen photo + +[[~56:00](https://youtu.be/gtaxKa_zZIo?t=3360)] +A text label pointing out the accelerometers was added over the instrumented +specimen photo, answering the exchange-feedback request to label parts of the +test setup. The photo's red arrows mark the sensor positions. + +![Label added](feedback-video-2026-08-18/35-3366-accelerometer-label-added.jpg) +*[56:06] Adding the "Accelerometers" label to the photo.* + +--- + +## The two data slides (explicitly "for Claude") + +### 22-24. The two data slides: for Claude + +[[58:26](https://youtu.be/gtaxKa_zZIo?t=3506) to +[59:28](https://youtu.be/gtaxKa_zZIo?t=3568)] +Sterling, wrapping up: "I might stop here and just end with these two slides, +like, for Claude." The slides are **"Each drop tells a two-part story: the jolt +going in, and the ringing that follows."** and **"Every recording gets the same +standard treatment, and each drop boils down to one score."** Four specific +fixes: + +1. **"First off, the titles read like AI slop."** Rewrite both as plain message + titles a person would say out loud. +2. **"The two slides here are incredibly information-dense and not easily + parsable. There's just too much on these."** Cut annotation layers and text; + one message per slide. +3. From Sterling's [follow-up PR comment](https://github.com/vertical-cloud-lab/tensegrity-optimization/pull/84#issuecomment-5334026194) + minutes after this task started: "don't use 'thousandths of a second (ms)'. + Ridiculous." Both plots label their time axes "thousandths of a second (ms) + after the plate lands"; the over-explained unit has to go. "time (ms) after + the plate lands", or just "time (ms)", is enough. Sweep the deck and the + plotting scripts for the same phrasing before regenerating these figures. +4. The third slide, attenuation ("The data we obtain helps us understand how + each structure attenuates a shock..."): "you have to look at it for a little + while and it doesn't really say much, to me at least." Marcus: it was pulled + in earlier just to have some data on there. Sterling: use "one that's already + gone through a filter", i.e. plot CFC-filtered traces rather than the raw + two-sensor comparison. + +Context from a few minutes earlier +([55:39](https://youtu.be/gtaxKa_zZIo?t=3339)): "Isn't it terrible... AI just +has a really hard time. It wants to tell you everything it can... to present +[all] his work to everyone." That is the failure mode to design against on +these slides. + +![Two-part story slide](feedback-video-2026-08-18/36-3508-two-part-story-slide.jpg) +*[58:28] The "two-part story" slide named in the critique; the second offender is visible below it in the thumbnail rail.* + +![Attenuation slide](feedback-video-2026-08-18/37-3546-attenuation-slide.jpg) +*[59:06] The attenuation slide: raw traces that "don't say much"; replace with filtered data.* + +### 25. Speaker notes need curation + +[[56:56](https://youtu.be/gtaxKa_zZIo?t=3416) to +[57:22](https://youtu.be/gtaxKa_zZIo?t=3442)] +Marcus asked about reading a long note aloud; Sterling: "I haven't done any +curation really on the speaker notes, so that's either from you, a slide note +for me, or Claude... you will want to find your own voice of what you want to +say." Open item for Marcus across the whole deck. + +--- + +## Appendix: voice-to-text corrections + +The YouTube auto-transcript garbles several technical terms. Corrections +applied throughout this document: + +| Raw transcript | Intended | +|---|---| +| "tenseity", "tensgity", "tense integrity", "tensity", "tens inspired" | tensegrity, tensegrity-inspired | +| "loopation optimization of multimaterial 3D printed integrity inspired energy absorbers" | "Closed-loop optimization of multi-material 3D-printed tensegrity-inspired energy absorbers" (the submitted abstract title) | +| "Oh, hunan at all at is really weird. It's not actually an abbreviation." | "Pajunen 'et. al.' is really weird; 'et. al.' is not actually an abbreviation." | +| "We'll put in Pokemon extreme." | "We'll put in Pajunen... Extreme [Mechanics Letters]." | +| "claw", "cla", "cloud" | Claude | +| "that would have been a much rudder awakening later" | "a much ruder awakening later" | +| "either topology or something about free strand" | "either topology or something about pre-strain" | +| "having Wikipedia listed on a presation" | "on a presentation" | +| "second spine" | the Tandem Second Spine (tensegrity exoskeleton product) | +| "tight titan mission" | the Titan mission concept | +| "so we've got our like a hook expens" | "we've got our hook..." 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Slide content for issue #94 — opening up the drop-tower analysis + +Issue [#94](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/94) +asks for the drop-tower analysis to stop being a black box: *"it was surprising +to me that you went from CFC-180 to suggesting something else … it would be best +for us to spot-check your work,"* plus tutorial-style notebooks and links to +source materials. + +This file is the talk-facing answer. Six slides (**30–35** of +`Slide Decks/IDETC Supplement Slides (BO block + gap + video + accel).pptx`) turn +that request into content the IDETC audience can also use, and the last section +lists what is still owed to #94 that a slide cannot deliver. + +Rebuild the slides with `python presentation/build_supplement_deck.py`. + +--- + +## The six slides + +### 30 — "One drop becomes one row of data: three numbers and the scatter around them." + +A four-box pipeline: **raw** (1.25 MHz, 4 channels) → **baseline** (pre-trigger +median) → **J211 filter** (CFC-180 and CFC-1000) → **metrics** (peak transmitted +force, SEA, compaction efficiency). Sub-line: replicate drops give the noise +model the optimizer needs — a single drop is never one point. + +The point of the slide is that only two steps involve judgement, steps 2 and 3 — +and both of them are what bit us. Slides 31–34 are those two steps. + +### 31 — "SAE J211 sets the filter, not us — and the class you pick changes the peak by a factor of two." + +Figure: [`media/fig-baseline-and-cfc.png`](media/fig-baseline-and-cfc.png), +generated from **specimen `bpx68c`, Signal 11** of the polyurethane sweep +(`data/drop-tests/pu-configs/raw/half-in.zip` on the +`copilot/add-drop-test-protocol-again` branch). + +* Left panel — the first 1.2 ms of the raw CH5 record, unshifted. The trigger + crossing is at **0.41 ms**, so the export *does* contain pre-trigger data. The + two candidate baselines are drawn: pre-trigger median **20.0 G** versus + full-record median **−0.5 G**. +* Right panel — the same impact at three filter settings: + **raw 567 G · CFC-1000 344 G · CFC-180 245 G**. + +Spoken: a Channel Frequency Class number times 1.65 is the −3 dB corner, so +CFC-180 is 300 Hz and CFC-1000 is 1650 Hz. Implementation is a two-pole +Butterworth applied forward and backward (zero phase), exactly as in +`cfc_filter()` in +`scripts/analysis/drop_test_60in_5felts_analysis.py`. + +### 32 — "We report both bands because they answer two different questions." + +| | CFC-180 · 300 Hz | CFC-1000 · 1650 Hz | +|---|---|---| +| question | what does the payload feel? | what is the structure doing? | +| use | smooth pulse, stable peak, the number in the force constraint | keeps the 500–550 Hz specimen mode — the part that differs between designs | + +This is the direct answer to the "why did you move off CFC-180?" question in +#94: **we did not abandon it**, we stopped using it as the *only* band. At a +300 Hz corner the specimen's own first mode (measured at **519–549 Hz** across +the ringdown analyses in this repo) is filtered away, so every design looks +alike. The force constraint stays CFC-180; the discrimination diagnostics are +CFC-1000. + +### 33 — "Our sensors lied to us first" (existing slide, kept here in the arc) + +The PR #74 calibration story: a mis-entered sensitivity made CH5 read +0.953 × CH4, and CH1 was clipping at the highest drops without it being visible. +Fix: regress every channel against every other, every campaign. + +### 34 — "We paid an adversary to break our own analysis, and it broke it." + +Figure: [`media/fig-baseline-flip.png`](media/fig-baseline-flip.png) — published +versus corrected CFC-180 transmissibility for the four polyurethane arrangements. + +| arrangement | as published | corrected (pre-trigger baseline) | +|---|--:|--:| +| A — 1/4 in | 1.022 | 1.037 | +| B — 1/2 in | 0.996 | 1.063 | +| C — 1/4 over 1/2 | 0.986 | 1.050 | +| D — 1/2 over 1/4 | 0.989 | 1.094 | + +Consequences: **no arrangement attenuates** (every T > 1), B is no longer the +most repeatable CFC-180 arrangement, and the "T falls monotonically with pulse +duration" relation disappears (Spearman ρ = 0.40, p = 0.60). Verdict adopted: +**none of the four — that sweep could not decide**; rerun it as a randomized +two-geometry crossover with ≥ 2 ms pre-trigger capture. + +Source: Edison Scientific adversarial review, task `d9092c5a`, committed at +`edison-trajectories/pu-configs/report/adversarial-review.md` on the +`copilot/add-drop-test-protocol-again` branch, with the recomputation +reproduced independently in-repo. + +Delivery note: no drama. We asked for the analysis to be attacked, four of our +grounds fell, and the document was marked **superseded** rather than quietly +patched. + +### 35 — "Every number on these slides can be re-derived from committed raw data." + +Backup slide for Q&A. Raw CSVs and the analysis script live beside each campaign; +the filter is a published standard; the adversarial re-analysis, its recomputed +tables and its notebook are committed too. This is where the Colab link goes once +the tutorial notebook exists. + +--- + +## Source materials (the second half of what #94 asked for) + +### Standards + +| Standard | What it fixes | Where it shows up here | +|---|---|---| +| **SAE J211-1**, *Instrumentation for Impact Test — Part 1: Electronic Instrumentation* | the CFC filter classes; −3 dB corner ≈ CFC × 1.65 Hz | slides 31–32; `cfc_filter()` | +| **ISO 6487** | the equivalent road-vehicle measurement-technique filter definition | slide 35 | +| **ISO 5348:2021**, *Mechanical mounting of accelerometers* | stud/wax/adhesive mounting and its usable bandwidth | slide 28 (key-seat + wax mount) | + +⚠️ The adversarial review corrected a citation we had been repeating: the +mounting standard is **ISO 5348**, not ISO 5347. Fix that anywhere it survives +before the talk. + +### In-repo primary sources (branch `copilot/add-drop-test-protocol-again`) + +| Asset | Path | +|---|---| +| filter + metric implementation | `scripts/analysis/drop_test_60in_5felts_analysis.py` (`cfc_filter`, `windowed_peak`) | +| the analysis under review in #94 | `scripts/analysis/drop_test_pu_configs_analysis.py` | +| the superseded write-up, with its banner | `docs/drop-test-pu-configs-analysis.md` | +| the adversarial review | `edison-trajectories/pu-configs/report/adversarial-review.md` | +| independent recomputation | `edison-trajectories/pu-configs/report/independent_per_drop_metrics.csv`, `…/independent_arrangement_summary.csv` | +| raw data used on slide 31 | `data/drop-tests/pu-configs/raw/half-in.zip` → `bpx68c_Signal11.csv` | +| protocol and known failure modes | `docs/drop-test-protocol.md` | +| video-side corroboration of the ~1.6 ms pulse | `docs/drop-test-60in-5felts-analysis.md`, `data/drop-tests/60in-5felts-validation/video/README.md` | + +### Video sources + +| Video | Use | +|---|---| +| our print timelapse, | slide 20 (embedded; also opens the background addendum) | +| TP4 DAQ training walkthrough, | background for anyone re-running the analysis | +| drop-test SOP, | background | +| drop shorts `Nab3hfuF4Dw` / `zkum2JlHpYk` | the YouTube copies of the `7xadt6` / `9GMQYQ` clips used on slide 25 | + +--- + +## Still owed to #94 (a slide cannot do these) + +1. **The tutorial Colab notebook.** Auto-download one campaign's raw CSVs from + this repo, walk through baseline → CFC filter → peak/Δv with the equations + and plots inline, and end by reproducing the slide-31 numbers + (567 / 344 / 245 G). Slide 35 has the slot for its link. +2. **Re-run the two sibling analyses.** `drop_test_pu_vs_felt_analysis.py` and + `drop_test_print_defects_analysis.py` share the full-record-median baseline. + The print-defect study matters most: its between-specimen differences are + ~2 %, the same order as the baseline shift. +3. **The randomized crossover** the review prescribes — two geometries × + arrangements A and B, 5 drops per cell, interleaved in randomized order, one + common trigger level, ≥ 2 ms pre-trigger and 50–100 ms post-impact capture, + outcomes prespecified. +4. **An Edison cross-check of the standards claims** on slides 31–32 before the + deck freezes, since they are stated on stage as fact. diff --git a/presentation/media/clip-drop-afar.mp4 b/presentation/media/clip-drop-afar.mp4 new file mode 100644 index 00000000..d081bc30 Binary files /dev/null and b/presentation/media/clip-drop-afar.mp4 differ diff --git a/presentation/media/clip-drop-highspeed.mp4 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00000000..89a76f32 --- /dev/null +++ b/presentation/regen_data_slide_figures.py @@ -0,0 +1,303 @@ +"""Regenerate the three data-slide figures in idetc-2026.pptx. + +Feedback items 22-24b from presentation/feedback-video-2026-08-18.md: the two +data slides were too dense, and the attenuation slide plotted raw traces. These +figures are the replacements: fewer annotation layers, axis labels that say +"time (ms)" instead of "thousandths of a second (ms)", and filtered data on the +attenuation comparison. + +Data: the 60 in validation campaigns committed at the root of the main branch +(Marcus_1.zip = specimen 7xadt6, jin_1.zip = specimen 9GMQYQ; TP4 exports, +200 ms at 125 kHz). Channel map per docs/drop-test-60in-5felts-analysis.md on +the PR #86 branch: CH2/CH3/CH4 = top-vertex tri-axis (X/Y/Z), CH5 = single-axis +input sensor on the base plate (also the trigger). Both campaigns ran +back-to-back on the same 4 felt + 1 cardboard stack on 2026-07-20. + +Usage: python regen_data_slide_figures.py --data-dir +e.g. populated via: git show "origin/main:Marcus_1.zip" > /Marcus_1.zip +""" + +import argparse +import io +import zipfile +from pathlib import Path + +import numpy as np +import pandas as pd +from scipy.signal import filtfilt +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt + +import build_search_space_figure as ssf + +# Colors: EMC-adjacent blue/orange, adjusted to pass the palette validator +# (chroma floor and 3:1 surface contrast) while staying near the deck theme. +BLUE = "#1878B8" # bottom (input) sensor, CH5 +ORANGE = "#D96A24" # top-vertex sensor, CH4 vertical / CH2 lateral +GRAY = "#9a9a9a" # raw recording +GUIDE_GRAY = "#8c8c8c" # schematic leader lines +INK = "#333333" +INK2 = "#595959" +MUTED = "#707070" + +plt.rcParams.update({ + "font.size": 22, + "axes.titlesize": 25, + "axes.labelsize": 23, + "xtick.labelsize": 20, + "ytick.labelsize": 20, + "legend.fontsize": 21, + "axes.edgecolor": "#cccccc", + "axes.labelcolor": INK, + "text.color": INK, + "xtick.color": MUTED, + "ytick.color": MUTED, + "axes.grid": True, + "grid.color": "#e6e6e6", + "grid.linewidth": 0.8, + "axes.axisbelow": True, + "figure.facecolor": "white", +}) + + +def load_tp4(source): + """Parse a TP4 CSV (path or bytes) -> (t seconds, {channel: G}, fs).""" + if isinstance(source, (str, Path)): + df = pd.read_csv(source, skiprows=8) + else: + df = pd.read_csv(io.BytesIO(source), skiprows=8) + df.columns = [c.strip().rstrip(",").strip() for c in df.columns] + df = df.dropna(axis=1, how="all") + t = df.iloc[:, 0].values.astype(float) + fs = 1.0 / np.median(np.diff(t)) + chans = {} + for c in df.columns[1:]: + x = df[c].values.astype(float) + x -= np.median(x[: int(0.0002 * fs)]) # baseline: first 0.2 ms + chans[c.split()[0]] = x + return t, chans, fs + + +def cfc(x, fs, cls): + """SAE J211-1 Appendix C channel-class filter: the standard's 2-pole + coefficients (single-pass corner at 2.0775x the class number), run + forward and backward (phaseless) so the double pass lands at the + class's -3 dB point. A plain Butterworth at 1.65x the class and then + filtfilt ends up about 20 percent narrow (the issue #94 finding).""" + wa = np.tan(np.pi * cls * 2.0775 / fs) + den = 1.0 + np.sqrt(2.0) * wa + wa * wa + a0 = wa * wa / den + b1 = -2.0 * (wa * wa - 1.0) / den + b2 = (-1.0 + np.sqrt(2.0) * wa - wa * wa) / den + return filtfilt(np.array([a0, 2.0 * a0, a0]), + np.array([1.0, -b1, -b2]), x) + + +def windowed_peak(tm, x, half_ms=1.5): + """|peak| within +/-1.5 ms of the largest excursion, as the campaign + analysis does (drop_test_60in_5felts_analysis.py windowed_peak); + returns (peak_abs, t_peak_ms, signed value at the peak).""" + i_imp = int(np.argmax(np.abs(x))) + m = np.abs(tm - tm[i_imp]) <= half_ms + seg, seg_t = x[m], tm[m] + j = int(np.argmax(np.abs(seg))) + return abs(seg[j]), seg_t[j], seg[j] + + +def contact_time(t, input_filtered, frac=0.2): + """First time the filtered input trace reaches frac of its own peak.""" + idx = np.argmax(np.abs(input_filtered) > frac * np.max(np.abs(input_filtered))) + return t[idx] + + +def caption(fig, text): + fig.text(0.5, 0.014, text, ha="center", va="bottom", fontsize=16, + color=MUTED) + + +def rel_ms(t, ch, fs): + return (t - contact_time(t, cfc(ch["CH5"], fs, 1000))) * 1000.0 + + +def fig_jolt_and_ringing(t, ch, fs, out): + """Slides 16/17: the impact pulse (both sensors) and the ring-down (top).""" + bottom = cfc(ch["CH5"], fs, 1000) + top_z = cfc(ch["CH4"], fs, 1000) + top_x = cfc(ch["CH2"], fs, 1000) + tm = rel_ms(t, ch, fs) + + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12.4, 5.6), dpi=200) + + m = (tm > -0.3) & (tm < 2.0) + ax1.plot(tm[m], bottom[m], color=BLUE, lw=2.5, label="bottom sensor") + ax1.plot(tm[m], top_z[m], color=ORANGE, lw=2.5, label="top sensor") + # Panel deliberately untitled (me-madsen, PR #84, 2026-08-20). + ax1.set_xlabel("time (ms) after the plate lands") + ax1.set_ylabel("acceleration (G)") + ax1.legend(frameon=False) + ax1.axhline(0, color="#bbbbbb", lw=1) + + m2 = (tm > 2.0) & (tm < 20.0) + ax2.plot(tm[m2], top_x[m2], color=ORANGE, lw=1.6) + ax2.set_title("The ringing that follows") + ax2.set_xlabel("time (ms) after the plate lands") + ax2.set_ylabel("acceleration (G)") + ax2.axhline(0, color="#bbbbbb", lw=1) + + caption(fig, "One drop, 60 in onto the felt stack (specimen 7xadt6); SAE J211 " + "CFC-1000 filter.\nRinging shown on the top sensor's side-to-side " + "axis.") + fig.tight_layout(rect=(0, 0.09, 1, 1)) + fig.savefig(out) + plt.close(fig) + + +def fig_standard_filter(t, ch, fs, out): + """Slide 18: the raw recording vs the same trace after the J211 filter.""" + raw = ch["CH5"] + filt = cfc(raw, fs, 180) + tm = rel_ms(t, ch, fs) + + fig, ax = plt.subplots(figsize=(12.4, 5.6), dpi=200) + m = (tm > -0.3) & (tm < 2.2) + ax.plot(tm[m], raw[m], color=GRAY, lw=0.9, label="raw recording") + ax.plot(tm[m], filt[m], color=BLUE, lw=3.0, + label="after the standard filter (SAE J211, CFC-180)") + ax.set_xlabel("time (ms) after the plate lands") + ax.set_ylabel("acceleration (G)") + ax.legend(frameon=False, loc="upper right") + ax.axhline(0, color="#bbbbbb", lw=1) + + caption(fig, "Bottom (input) sensor, same drop as the previous slide; the " + "same filter is applied to every drop.") + fig.tight_layout(rect=(0, 0.05, 1, 1)) + fig.savefig(out) + plt.close(fig) + + +def draw_sensor_schematic(ax, fig): + """Left panel: where the two sensors sit on the drop stack (mid-range + T3 prism from the search-space figure, so the styles match).""" + params = dict(ssf.MID) + cu, cv = ssf.struct_center(params) + # Extra room below the structure for the base plate and impact arrow. + ppmm = ssf.setup_axes(ax, fig, (cu, cv - 16.0), 128.0, 168.0) + ssf.draw_structure(ax, params, ppmm) + + bot, top = ssf.nodes(params) + v_bot = min(ssf.project(p)[1] for p in bot) + plate_top = v_bot - 6.0 + ax.add_patch(plt.Rectangle((cu - 46.0, plate_top - 8.0), 92.0, 8.0, + facecolor="#b8b8b8", edgecolor="none", zorder=2)) + + # Sensor dots: blue input sensor on the base plate, orange tri-axis + # sensor at a top vertex (the nearest one, so the dot reads in front). + u_top, v_top, _ = max((ssf.project(p) for p in top), key=lambda q: q[2]) + v_rim = max(ssf.project(p)[1] for p in top) + for (u, v, color) in [(cu - 36.0, plate_top - 4.0, BLUE), + (u_top, v_top, ORANGE)]: + ax.plot([u], [v], marker="o", ms=13, color=color, + markeredgecolor="white", markeredgewidth=2, zorder=6) + ax.text(cu - 36.0, plate_top - 16.0, "bottom sensor\n(input)", + ha="center", va="top", fontsize=18, color=INK) + ax.annotate("top sensor", xy=(u_top, v_top + 4.0), + xytext=(u_top, v_rim + 13.0), ha="center", va="bottom", + fontsize=18, color=INK, zorder=6, + arrowprops=dict(arrowstyle="-", lw=1.4, color=GUIDE_GRAY, + shrinkA=2, shrinkB=6)) + + ax.annotate("", xy=(cu + 30.0, plate_top - 10.0), + xytext=(cu + 30.0, plate_top - 30.0), + arrowprops=dict(arrowstyle="-|>", lw=3.5, color=INK2, + mutation_scale=28), zorder=6) + ax.text(cu + 30.0, plate_top - 36.0, "impact", ha="center", va="top", + fontsize=18, color=INK2) + + +def fig_attenuation(drops, out): + """The attenuation slide: same drop conditions for two specimens, peak + markers on both sensors, and the metric the campaign computes, + T = peak top acceleration / peak bottom acceleration + (drop_test_60in_5felts_analysis.py: t_ch5 = top CFC-180 tri-axis + resultant peak / CH5 CFC-180 peak, each within +/-1.5 ms of impact).""" + fig = plt.figure(figsize=(13.2, 7.0), dpi=200) + gs = fig.add_gridspec(1, 3, width_ratios=[0.92, 1.5, 1.5], + left=0.045, right=0.985, top=0.875, bottom=0.245, + wspace=0.24) + ax_s = fig.add_subplot(gs[0]) + draw_sensor_schematic(ax_s, fig) + + axes = [fig.add_subplot(gs[1])] + axes.append(fig.add_subplot(gs[2], sharey=axes[0])) + for ax, (label, (t, ch, fs)) in zip(axes, drops.items()): + bottom = cfc(ch["CH5"], fs, 180) + top = np.sqrt(sum(cfc(ch[c], fs, 180) ** 2 + for c in ("CH2", "CH3", "CH4"))) + tm = rel_ms(t, ch, fs) + m = (tm > -1) & (tm < 8) + ax.plot(tm[m], bottom[m], color=BLUE, lw=2.2, label="bottom sensor") + ax.plot(tm[m], top[m], color=ORANGE, lw=2.2, label="top sensor") + ax.set_title(label, fontsize=20, color=MUTED, loc="left", pad=10) + ax.axhline(0, color="#bbbbbb", lw=1) + + pk_b, t_b, y_b = windowed_peak(tm[m], bottom[m]) + pk_t, t_t, y_t = windowed_peak(tm[m], top[m]) + for t_pk, y_pk, pk, color, name, dy in [ + (t_t, y_t, pk_t, ORANGE, "top", 60.0), + (t_b, y_b, pk_b, BLUE, "bottom", -60.0)]: + ax.plot([t_pk], [y_pk], marker="o", ms=9, color=color, + markeredgecolor="white", markeredgewidth=1.5, zorder=5) + ax.plot([t_pk, 7.8], [y_pk, y_pk], ls=(0, (4, 3)), lw=1.4, + color=color, zorder=4) + ax.text(7.7, y_pk + dy, + rf"$\hat{{a}}_\mathrm{{{name}}}$ = {pk:.0f} G", + ha="right", va="center", fontsize=19, color=INK) + ax.text(3.5, 880.0, + rf"$T = \hat{{a}}_\mathrm{{top}} \,/\, " + rf"\hat{{a}}_\mathrm{{bottom}}$ = {pk_t / pk_b:.2f}", + ha="center", va="top", fontsize=21, color=INK) + ax.set_ylim(-170, 900) + + axes[0].set_ylabel("acceleration (G)") + plt.setp(axes[1].get_yticklabels(), visible=False) + fig.text(0.63, 0.145, "time (ms) after the plate lands", ha="center", + fontsize=23, color=INK) + handles, labels = axes[0].get_legend_handles_labels() + fig.legend(handles, labels, ncol=2, frameon=False, loc="upper center", + bbox_to_anchor=(0.63, 1.005)) + caption(fig, "Two printed specimens, 60 in onto the same felt stack, " + "same day; SAE J211 CFC-180 filter,\n" + "the class the campaign metric uses. The top trace combines " + "the top sensor's three axes;\n" + "peaks are the largest excursion within 1.5 ms of impact. " + "T > 1 amplified, T < 1 attenuated.") + fig.savefig(out) + plt.close(fig) + + +def main(): + p = argparse.ArgumentParser() + p.add_argument("--data-dir", default="/tmp/drops") + p.add_argument("--out-dir", default=str(Path(__file__).parent / "media")) + args = p.parse_args() + data, out = Path(args.data_dir), Path(args.out_dir) + out.mkdir(exist_ok=True) + + def first_drop(zname, index=1): + z = zipfile.ZipFile(data / zname) + return load_tp4(z.read(sorted(z.namelist())[index])) + + t, ch, fs = first_drop("Marcus_1.zip") + fig_jolt_and_ringing(t, ch, fs, out / "fig-jolt-and-ringing.png") + fig_standard_filter(t, ch, fs, out / "fig-standard-filter.png") + + drops = {"specimen 7xadt6": (t, ch, fs), + "specimen 9GMQYQ": first_drop("jin_1.zip")} + fig_attenuation(drops, out / "fig-attenuation-filtered.png") + print("wrote 3 figures to", out) + + +if __name__ == "__main__": + main() diff --git a/presentation/search-space-slide-screenshot.png b/presentation/search-space-slide-screenshot.png new file mode 100644 index 00000000..32faa7ab Binary files /dev/null and b/presentation/search-space-slide-screenshot.png differ diff --git a/presentation/set_presenter.py b/presentation/set_presenter.py new file mode 100644 index 00000000..75518f72 --- /dev/null +++ b/presentation/set_presenter.py @@ -0,0 +1,99 @@ +"""Stamp the IDETC presenter identity onto every deck and every build source. + +Marcus Madsen presents the talk, so his name and title belong on the title +slide — and, because the EMC template keeps its title text on the *slide master* +and its presenter block on the *Title Slide layout* rather than on a slide, on +those two parts as well. Otherwise any slide created from the Title Slide layout +silently shows Sterling's EMC 2026 title and affiliation (that is exactly what +Draft 1 slide 14 was doing). + +The build sources are stamped too, so a rebuild of the supplement deck or the +background addendum does not reintroduce the EMC text. + +Run after any deck rebuild; it is idempotent. + + python presentation/set_presenter.py +""" + +from pathlib import Path + +from pptx import Presentation + +HERE = Path(__file__).parent + +PRESENTER = "Marcus Madsen" +ROLE = "Research Assistant" +VENUE = "ASME IDETC-CIE 2026" +WHEN = "August 2026" + +# The master title is four runs: two title lines, a spacer, and a gray tagline. +TITLE_RUNS = [ + "\tLet’s build better tensegrity structures", + "\tfaster, using real drop-test data", + "\t", + "+ closed-loop BO and multi-material printing", +] + +TARGETS = [ + HERE / "Slide Decks" / "IDETC Tensegrity Slides Draft 1.pptx", + HERE / "Slide Decks" / "IDETC Supplement Slides (BO block + gap + video + accel).pptx", + HERE / "emc2026-idetc-demo.pptx", + HERE / "emc-bo-block.pptx", # build source for the supplement deck + HERE / "emc2026-bare-template.pptx", # bare EMC theme, no slides +] + +STALE_TITLE = "high-performing alloys" +STALE_PRESENTERS = ("Sterling Baird", "[Presenter Name]", PRESENTER) + + +def runs_of(shape): + """Every run in a shape, in reading order — paragraph breaks included.""" + return [r for p in shape.text_frame.paragraphs for r in p.runs] + + +def set_runs(runs, texts): + """Replace run text in place so font, size, and color survive.""" + for run, text in zip(runs, texts): + run.text = text + + +def stamp(path): + prs = Presentation(str(path)) + hits = [] + + for master in prs.slide_masters: + for shape in master.shapes: + if shape.has_text_frame and STALE_TITLE in shape.text_frame.text: + set_runs(runs_of(shape), TITLE_RUNS) + hits.append("master title") + for layout in master.slide_layouts: + if layout.name != "Title Slide": + continue + for shape in layout.shapes: + if not shape.has_text_frame: + continue + if not any(n in shape.text_frame.text for n in STALE_PRESENTERS): + continue + runs = runs_of(shape) + set_runs(runs[:2], [PRESENTER, ROLE]) + if len(runs) >= 4: + set_runs(runs[2:4], [VENUE, WHEN]) + hits.append("Title Slide layout") + + # Draft 1 puts the presenter block on the slide itself, as a plain text box. + for slide in prs.slides: + for shape in slide.shapes: + if shape.has_text_frame and PRESENTER in shape.text_frame.text: + runs = runs_of(shape) + if len(runs) >= 2: + set_runs(runs[:2], [PRESENTER, ROLE]) + hits.append("slide presenter block") + + prs.save(str(path)) + print(f"{path.name}: {', '.join(hits) if hits else 'nothing to stamp'}") + + +if __name__ == "__main__": + for target in TARGETS: + if target.exists(): + stamp(target) diff --git a/presentation/slide-references-presenter-notes.md b/presentation/slide-references-presenter-notes.md new file mode 100644 index 00000000..b5ea78f0 --- /dev/null +++ b/presentation/slide-references-presenter-notes.md @@ -0,0 +1,76 @@ +# Presenter notes for the slide reference list (internal) + +Internal to-dos and provenance notes for +[slide-references.md](slide-references.md), kept out of the public page per +@me-madsen's request (2026-08-24). Resolve these before the reference list is +called complete. Updated 2026-08-25 after auditing the public list against the +active (non-hidden) slides of the stored `idetc-2026.pptx`. + +## Open to-dos + +- The 48x assembly clip was recorded in the deck's notes as "hexahedron, exact + URL TODO". The frames match TensoLogic's "12 Dowel Structure" video (same + studio, kit boxes, and finished structure), so that link is used in the + public list. Confirm by eye before the talk, and replace the TODO text in + that slide's speaker notes with the confirmed link so no TODO shows in + presenter view. +- The tensegrity side table photo still has no identified original. Replace it + with a photo of one of our own models, or drop it, before the reference list + is called complete. +- The kit-ball image on the compact-and-deploy slide carries an "AI-generated + content" watermark (visible bottom-left on screen), and that slide's notes + still hold the "insert picture of assembled ball" placeholder. Replace it + with a real photo of the Tensegrity Adventures kit ball; if it stays, it + needs no external credit but should be labeled AI-generated. +- The anatomy slide's speaker notes say only "Model courtesy of Wikipedia". + CC BY-SA requires naming the author: use the public list's exact credit + line, "Cmglee, CC BY-SA 3.0, via Wikimedia Commons". +- The future-applications slide's speaker notes still credit + Al Sabouni-Zawadzka et al. (2025) for the lattice image, but the lattice + image now on that slide is our own render + (`presentation/media/fig-lattice-concept.png`). Update the speaker note, or + restore the paper's photo if that is preferred. +- The references QR code is now embedded on the closing References slide + (verified by decoding the stored deck, 2026-08-25). It points to this + file's sibling on the working branch, so the link dies if the branch is + deleted after merge. Before the deck freezes: merge PR #84 (or otherwise + land `presentation/slide-references.md` on main), regenerate with + `python presentation/build_references_qr.py --branch main`, and swap the QR + image on the closing slide. The second QR on that slide points to the + repository root and needs no change. + +## Entries removed 2026-08-25 (asset no longer on an active slide) + +Per @me-madsen's instruction that the public list reference nothing that is +not in the active deck. Restore the entry if the slide comes back. + +- **Al Sabouni-Zawadzka et al. (2025)**, modular tensegrity-like lattice + photo: now appears only on the hidden variant of the future-applications + slide. Citation for restoring: A. Al Sabouni-Zawadzka, A. Micheletti, + M. Kolodziejczak, A. Zawadzki, "Design and fabrication of modular + tensegrity-like lattices with auxetic properties", *Materials & Design* + 258, 114513 (2025), DOI 10.1016/j.matdes.2025.114513. +- **Pajunen et al. (2019)**, spherically-jointed impact cell (our re-render + of their Geometry #3): now appears only on a hidden backup variant of the + "traditional structures" slide. Citation for restoring: K. Pajunen, + P. Johanns, R. K. Pal, J. J. Rimoli, C. Daraio, "Design and impact response + of 3D-printable tensegrity-inspired structures", *Materials & Design* 182, + 107966 (2019), DOI 10.1016/j.matdes.2019.107966 (open access). +- The **fold-and-release clip** of the kit ball now sits on a hidden slide in + the motivation section. The public list keeps the kit identification and + the YouTube link, reframed as our own material about the physical demo + model rather than as an in-deck asset. + +## Provenance notes (no action needed) + +- The seven-model tensegrity gallery on the "traditional structures" slide is + our own render: `figures/tensegrity_models_extended_preview_shaded.png` + from PR #22 (commit 52ce670). The configurations it depicts are classic + published designs (Geiger cable-dome, biotensegrity spine, SUPERball, + Tibert/Pellegrino mast, Knight et al. patent antenna, bistable + double-prism, cuboctahedron tessellation); the labeled original, + `figures/tensegrity_models_extended_preview.png`, names them if anyone asks. +- The Pareto front figures on the active results slides carry a "PROTOTYPE: + round-2 outcomes are synthetic" annotation. Swap in the real round-2 + figures (the deck notes point at PR #102) before the deck freezes, or keep + the label and say it aloud. diff --git a/presentation/slide-references.md b/presentation/slide-references.md new file mode 100644 index 00000000..6abc54ac --- /dev/null +++ b/presentation/slide-references.md @@ -0,0 +1,84 @@ +# References and Media Credits + +**Talk:** Discovering multi-material, tensegrity-inspired energy absorbers via +closed-loop Bayesian optimization +**Presenter:** Marcus Madsen +**Authors:** Marcus Madsen, Audrey Christiansen, Jinkwan Han, Jeffrey R. Hill, +and Sterling G. Baird (BYU Vertical Cloud Lab) +**Venue:** ASME IDETC-CIE 2026, session DAC-10 + +## Contributions + +- **3D printing:** Audrey Christiansen and Marcus Madsen. +- **Drop tower testing:** Jinkwan Han and Marcus Madsen. Drop tower courtesy + of the Smash Lab (Dr. Jeff Hill). +- **Bayesian optimization:** run by Dr. Sterling Baird. Our campaign script + was scaffolded with [Honegumi](https://honegumi.readthedocs.io/), an + open-source template generator for advanced Bayesian optimization scripts, + and runs on Meta's state-of-the-art [Ax](https://ax.dev/) platform. + +## Videos by others + +| What is shown | Source | Credit | +|---|---|---| +| Super Ball Bot mission concept animation: the lander falling to the surface of Titan (a moon of Saturn), bouncing, and coming to rest. A still from the same animation closes the talk. | [HET2 SUPERball Bot Task NIAC Mission concept](https://www.youtube.com/watch?v=1wce-mB69mE) | NASA (NASA Video channel) | +| NASA researcher tossing a tensegrity baby toy and calling it a landing robot | [NASA 360 Talks - Super Ball Bot](https://www.youtube.com/watch?v=0eC4A2PXM-U) | NASA (NASA eClips archive channel) | +| 2D tensegrity teaching model, pushed and springing back | [Tensegrity Explained](https://www.youtube.com/watch?v=0onncd0_0-o) | Steve Mould | +| Hand assembly of a T3 prism from a kit, shown at 12x speed | [3 Dowel Tensegrity Structure by TensoLogic](https://www.youtube.com/watch?v=-Y_eU84h32k) | TensoLogic ([tensologic.com](https://tensologic.com/)) | +| Hand assembly of a larger 12-strut structure, shown at 48x speed | [12 Dowel Structure by TensoLogic](https://www.youtube.com/watch?v=bayaW9W68xg) | TensoLogic ([tensologic.com](https://tensologic.com/)) | +| Second Spine exosuit render, rotating, and the exosuit product photo beside it | [Tandem Exosuits](https://tandemexosuits.com/) | Tandem Exosuits (TANDEM) | + +## Images by others + +| What is shown | Source | Credit | +|---|---|---| +| Simplest tensegrity structure, a three-strut prism with green struts and red tendons | [Tensegrity simple 3.gif, Wikimedia Commons](https://commons.wikimedia.org/wiki/File:Tensegrity_simple_3.gif) | Cmglee, [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/), via Wikimedia Commons | +| Super Ball Bot prototype poster image | [Super Ball Bot, nasa.gov image article](https://www.nasa.gov/image-article/super-ball-bot/) | NASA | +| Tensegrity side table photo | Widely shared web image; original source not yet identified | Unattributed | + +## Figures from publications + +- **Pajunen et al. (2021).** Mono-filament tensegrity-inspired structure with + tunable wave propagation characteristics. K. Pajunen, P. Celli, C. Daraio, + "Prestrain-induced bandgap tuning in 3D-printed tensegrity-inspired lattice + structures", *Extreme Mechanics Letters* 44, 101236 (2021). DOI: + [10.1016/j.eml.2021.101236](https://doi.org/10.1016/j.eml.2021.101236). + [PDF from the Daraio group](https://daraio.caltech.edu/publications/Pajunen_EML_2021.pdf). +- **Baird and Sparks (2022).** The Bayesian optimization explainer figures: + the benchmark against traditional design of experiments, the surrogate + model, the acquisition function, and the optimization loop animation. + S. G. Baird, T. D. Sparks, "What is a minimal working example for a + self-driving laboratory?", *Matter* 5(12), 4170 to 4178 (2022). DOI: + [10.1016/j.matt.2022.11.007](https://doi.org/10.1016/j.matt.2022.11.007). +- **Gu et al. (2026).** Tensegrity crutch tip images on the + future-applications slide. J. Gu, J. Spyra, A. Walski, L. Elsaesser, + S. Bierner, D. Dotov, "Tensegrity crutches with compliance from a + pre-stressed self-tensile module improve ground reaction force profiles, + speed, effort, comfort, and perceived stability", preprint, + [arXiv:2605.02710](https://arxiv.org/abs/2605.02710). +- **Filipe Amarante dos Santos (NOVA University Lisbon, Portugal).** + Two-material tensegrity structure photos, provided directly by the author + and used with permission; no public link. + +## Our own materials (BYU Vertical Cloud Lab) + +Everything not listed above is our own: specimen and lab photos, the drop +tower photo, the drop test videos, the print timelapse, the high-speed impact +footage, the geometry-parameter animation and structure renders, the +accelerometer and transmissibility figures, the Pareto front plots, and the +workflow diagram. Selected public links: + +- Project repository (code, data, and the scripts that generate every + figure): +- The physical ball model we use to demonstrate compacting and deploying is + the + [Icosahedron Tensegrity Kit by Tensegrity Adventures](https://www.tensegrityadventures.com/shop/p/icosahedron-tensegrity), + a 6-bar tensegrity icosahedron, the same configuration as NASA's SUPERball + bot. Our clip of it + [folding together and releasing](https://www.youtube.com/watch?v=y8KjBRH4Wis) + is on the BYU Vertical Cloud Lab YouTube channel. +- [Multi-material print timelapse of a T3 specimen](https://www.youtube.com/watch?v=nQNmi-NiL5I), + BYU Vertical Cloud Lab. +- Drop test statistics quoted on the drop tower slide (about 3,660 drops + across about 50 specimens in about 3 months): + [tensegrity-optimization issue #101](https://github.com/vertical-cloud-lab/tensegrity-optimization/issues/101). diff --git a/presentation/slide-walkthrough-2026-08-20.md b/presentation/slide-walkthrough-2026-08-20.md new file mode 100644 index 00000000..5d510bb7 --- /dev/null +++ b/presentation/slide-walkthrough-2026-08-20.md @@ -0,0 +1,823 @@ +# Slide walkthrough, 2026-08-20: spoken script, change notes, and to-do list + +Transcribed from Marcus's recorded walkthrough of `idetc-2026.pptx` +("Slide Review and Notes - 8-20-2026.mp4", 45 min 00 s): + +- YouTube: +- Box: + +Each section below covers one slide (or slide group) in the order visited in +the video. The **spoken script** blocks keep Marcus's own words, lightly +cleaned: voice-to-text mishaps corrected for intent (see the +[appendix](#appendix-voice-to-text-corrections)), filler removed, sentences +joined. Suggestions from Claude sit in separate *Suggestion* notes so the +script itself stays in Marcus's voice. Timestamps link into the YouTube video. +Screenshots live in +[`slide-walkthrough-2026-08-20/`](slide-walkthrough-2026-08-20/). + +One numbering caveat: during the recording PowerPoint showed "of 38" slides, +while the stored deck as of this transcription has 46 (hidden slides were +added at the end by other sessions). Slide references below use the current +stored deck's titles, which are unambiguous, plus the current stored slide +number where useful. + +--- + +## Marcus's to-do checklist + +Collected from every "let's make a note" moment in the video, grouped. Each +item links to the moment it came from. + +### Slide and deck changes + +- [ ] Decide the hook ordering: either keep NASA toy-throw clip then Titan + clip, or move the Titan clip so it plays *during* the "you have a package" + narration, timed so the fall (which starts about 8 s into the clip) lands + on "what if you could just drop it onto the surface" + ([4:12](https://youtu.be/-0qPEmmgSBA?t=252), + [4:49](https://youtu.be/-0qPEmmgSBA?t=289)) +- [ ] Reorder the prior-work slide: show the two-material structure (Filipe + Amarante dos Santos image) first, then the mono-filament Pajunen structure + ([17:44](https://youtu.be/-0qPEmmgSBA?t=1064)) +- [ ] Set the Steve Mould clip (and check the other embedded clips) to play + automatically when the slide opens + ([12:41](https://youtu.be/-0qPEmmgSBA?t=761)) +- [ ] Redo the jolt/ringing data slides: add marker lines from the top of + each peak (top-sensor peak and bottom-sensor peak), show the ratio, + possibly as an equation ([31:55](https://youtu.be/-0qPEmmgSBA?t=1915), + [33:17](https://youtu.be/-0qPEmmgSBA?t=1997)) +- [ ] Delete the duplicate jolt slide. The stored deck currently has the + same "We measure the jolt..." slide twice (slides 20 and 21) plus an + untitled leftover with the old figure (slide 19, axis still reading + "thousandths of a second (ms)") + ([33:34](https://youtu.be/-0qPEmmgSBA?t=2014)) +- [ ] Omit the hidden "Tensegrity's unique properties" slide for good and + cover its content verbally (agreed with Sterling, "it's just such an + awkward slide") ([26:38](https://youtu.be/-0qPEmmgSBA?t=1598)) +- [ ] Consider adding a 3D-printing / "make materials" slide between the + drop-tower block and the data block; decided against a repeated + mini-loop-diagram navigation graphic (do it vocally instead) + ([34:37](https://youtu.be/-0qPEmmgSBA?t=2077), + [34:57](https://youtu.be/-0qPEmmgSBA?t=2097)) +- [ ] Decide placement of the closed-loop workflow slide (maybe move it + toward the Bayesian optimization side) and of the search-space slide; + wants feedback on this ([28:32](https://youtu.be/-0qPEmmgSBA?t=1712)) +- [ ] Fix the "By connecting these ideas" slide graphics: the drop-tower + photo is "a little janky" and the right-hand icon is a random + neural-network symbol. Note already in the slide: replace the printing + icon with a printed tensegrity image, testing with a drop-tower image, + and the BO icon with a black box + ([42:49](https://youtu.be/-0qPEmmgSBA?t=2569)) +- [ ] Future-applications slide: find a better lattice-structure example or + read the cited paper (on-slide credit: Al Sabouni-Zawadzka et al. 2025) + ([43:29](https://youtu.be/-0qPEmmgSBA?t=2609)) +- [ ] Add an acknowledgments element crediting Chenquan (spelling to + confirm), Audrey, Sterling, and Dr. Hill, whether or not it is spoken + aloud ([44:26](https://youtu.be/-0qPEmmgSBA?t=2666)) +- [ ] Replace the closing VCL + BYU logo image, "too pixelated, replace + with higher quality" (note already typed into the slide's notes) + ([44:55](https://youtu.be/-0qPEmmgSBA?t=2695)) + +### Things to look up or review before presenting + +- [ ] A good word for "another celestial body" in the hook (the Titan clip + is a moon, not a planet) ([0:30](https://youtu.be/-0qPEmmgSBA?t=30)); + Titan is confirmed to be Saturn's largest moon +- [ ] What the Tandem exosuit ("exo spine") actually does, beyond being + marketed to construction workers for back relief + ([7:53](https://youtu.be/-0qPEmmgSBA?t=473)) +- [ ] Write out an expanded definition of tensegrity to review beforehand, + including the tensegrity-inspired distinction (see + [terms](#terms-to-clarify-and-where-to-look) below) + ([15:55](https://youtu.be/-0qPEmmgSBA?t=955)) +- [ ] The Pajunen structure's material (a polyamide with a grade number); + decide whether to name it on stage + ([16:50](https://youtu.be/-0qPEmmgSBA?t=1010)) +- [ ] Double-check the full name of TPU (it is thermoplastic polyurethane) + and its damping properties ([18:52](https://youtu.be/-0qPEmmgSBA?t=1132)) +- [ ] Work out how to explain why the dual-nozzle work matters: the + novelty framing (print-in-place dual-filament tensegrity topology is + itself uncommon, but the intentional closed-loop optimization on these + structures is the real novelty). "This is the part I'm currently + stumbling over most" ([19:33](https://youtu.be/-0qPEmmgSBA?t=1173)) +- [ ] Look up the definition of metamaterial structures + ([24:52](https://youtu.be/-0qPEmmgSBA?t=1492)) +- [ ] Establish earlier in the talk that the T3 prism + 3D printing is a + stepping stone (a proxy) toward future work: metamaterial lattices, + parcel-delivery energy absorbers, crutch and walking-stick tips + ([24:30](https://youtu.be/-0qPEmmgSBA?t=1470)) +- [ ] Look into the acrylic mounting plate: how well does acrylic transmit + a shock, given the recollection that magnesium (then aluminum) is + preferred for shock transmission + ([30:15](https://youtu.be/-0qPEmmgSBA?t=1815)) +- [ ] Double-check whether the slow-motion video tells us anything + quantitatively useful, so it can be characterized correctly on stage + ([31:14](https://youtu.be/-0qPEmmgSBA?t=1874)) +- [ ] Define attenuation and transmissibility precisely, and double-check + that the peak-ratio description matches what our pipeline computes + ([31:41](https://youtu.be/-0qPEmmgSBA?t=1901)) +- [ ] Understand the SAE J211 filter well enough to decide whether and how + to mention it ([32:21](https://youtu.be/-0qPEmmgSBA?t=1941), + [34:24](https://youtu.be/-0qPEmmgSBA?t=2064)) +- [ ] Review the definition of traditional design of experiments + ([38:39](https://youtu.be/-0qPEmmgSBA?t=2319)) +- [ ] Re-review Sterling's notes on f(x) and x for the surrogate-model + slide ([39:45](https://youtu.be/-0qPEmmgSBA?t=2385), + [42:08](https://youtu.be/-0qPEmmgSBA?t=2528)) +- [ ] Check the pronunciation of "Bayesian" + ([38:18](https://youtu.be/-0qPEmmgSBA?t=2298)) + +### Delivery, practice, and props + +- [ ] Decide whether to bring the physical TensoLogic ball and do the + compact-and-release live: 3D print a clasp to hold it compacted, remove + the clasp while talking, brief toss and catch (or let it bounce). + Weighed against the risk of fumbling it when nervous + ([9:35](https://youtu.be/-0qPEmmgSBA?t=575)) +- [ ] Decide on the jokes: the "give it to a child" durability joke (leaning + toward excluding it), the "until funding runs out" campaign joke, and + the "my hand under the table, we have a much safer way to do that now" + aside (leaning toward keeping that one) + ([5:18](https://youtu.be/-0qPEmmgSBA?t=318), + [22:46](https://youtu.be/-0qPEmmgSBA?t=1366), + [29:43](https://youtu.be/-0qPEmmgSBA?t=1783)) +- [ ] Poll idea for audience engagement: "who here has heard of + tensegrity?" and later "who here has heard of Bayesian optimization?" + ([7:10](https://youtu.be/-0qPEmmgSBA?t=430), + [38:14](https://youtu.be/-0qPEmmgSBA?t=2294)) +- [ ] Watch arm movements while presenting + ([11:08](https://youtu.be/-0qPEmmgSBA?t=668)) +- [ ] Practice the Titan-clip timing against the hook narration + ([4:52](https://youtu.be/-0qPEmmgSBA?t=292)) +- [ ] Decide on the closing line ("the faster we are able to iterate and + advance ourselves, the better life we can enjoy living now, because our + time is limited"), or find one that feels less cheesy + ([44:00](https://youtu.be/-0qPEmmgSBA?t=2640)) + +### To discuss with the team + +- [ ] Slide placement feedback: closed-loop slide position, possible extra + printing slide, search-space slide position + ([28:49](https://youtu.be/-0qPEmmgSBA?t=1729)) +- [ ] Crutch and walking-stick tips: "we actually need to talk about + partnering with them on that" + ([43:48](https://youtu.be/-0qPEmmgSBA?t=2628)) +- [ ] Sterling may have notes on how the Bayesian optimization block is + introduced ([39:31](https://youtu.be/-0qPEmmgSBA?t=2371)) + +--- + +## Terms to clarify, and where to look + +The terms Marcus flagged during the walkthrough, with places to read. Repo +files are linked by name; external references are standard, findable copies. + +**Transmissibility and attenuation.** The description given in the video is +the right shape: take the peak acceleration the top of the structure sees, +divide by the peak acceleration the bottom (input) sees. A ratio above 1 +means the structure amplified the shock; below 1 means it attenuated it, +and closer to 0 is better. Notes: (1) vibration textbooks define +transmissibility for steady sinusoidal input as the ratio of response +amplitude to input amplitude, and the peak-ratio version used for a single +shock is often called shock transmissibility, so it is worth saying +"peak-to-peak-ratio" explicitly on stage; (2) verified against the +campaign analysis code (`drop_test_60in_5felts_analysis.py` on the PR #86 +branch): the pipeline computes exactly this peak ratio on SAE J211 +CFC-180-filtered signals, T = peak top / peak CH5, where the top value is +the magnitude of the top sensor's three axes combined (CH2/CH3/CH4), not +the vertical channel alone, and each peak is the largest excursion within +1.5 ms of impact, so the two peaks need not be simultaneous. See also +[`regen_data_slide_figures.py`](regen_data_slide_figures.py) and +[`issue-94-analysis-slides.md`](issue-94-analysis-slides.md). For a +textbook treatment: S. S. Rao, *Mechanical Vibrations* (transmissibility +chapter) and *Harris' Shock and Vibration Handbook* (shock isolation and +shock data analysis chapters). + +**Classical definition of tensegrity (and "tensegrity-inspired").** The +conditions usually cited: compression members (struts) that do not touch +one another, a continuous network of tension members (cables/tendons), +self-equilibrated pre-stress (the structure is stressed with no external +load), and stability that comes from that pre-stress; cables are idealized +as inextensible. Anthony Pugh's phrasing, "a set of discontinuous +compressive components interacting with a set of continuous tensile +components," is close to the "islands of compression in a sea of tension" +line already paraphrased on the anatomy slide. Where to look: R. Buckminster +Fuller's patent US 3,063,521 (1962) for the coinage; Skelton & de Oliveira, +*Tensegrity Systems* (Springer, 2009), whose class-1/class-k definition is +the cleanest for Q&A (class 1: no two struts touch); Motro, *Tensegrity: +Structural Systems for the Future* (2003). "Tensegrity-inspired" as used in +our work and in the Pajunen paper: keeps the topology or geometry but drops +one or more classical conditions, in our case no as-built pre-stress and +extensible TPU members. The wording already on the hidden caveat slide and +in [`doumont-presentation-template.md`](doumont-presentation-template.md) +matches this. + +**TPU.** Thermoplastic polyurethane: an elastomer that prints, with +viscoelastic (damping) behavior, graded by Shore hardness (ours is a 95A +class filament). It is hygroscopic, so it needs drying before printing, +and its softness is why it jams in extruders more easily. The manufacturer +technical data sheet for the specific filament used in the campaign is the +citable source for hardness and damping claims; print-side history is in +PR #35's print lineage. + +**Pajunen et al. (mono-filament structure).** The on-slide credit is +Pajunen et al., *Extreme Mechanics Letters* (2021). Read the paper for the +exact polyamide grade before naming it on stage (a copy is archived on the +PR #58 branch; see [`data-catalogue.md`](data-catalogue.md)). The earlier +companion, Pajunen et al. 2019 in *Materials & Design* ("Design and impact +response of 3D-printable tensegrity-inspired structures"), is also worth a +skim since it coined much of the tensegrity-inspired framing. + +**Metamaterial.** Working definition: a material whose effective properties +come from its engineered architecture (geometry of a repeating unit cell) +rather than from its chemical composition. Good entry points: Bertoldi, +Vitelli, Christensen & van Hecke, "Flexible mechanical metamaterials," +*Nature Reviews Materials* (2017), and Kadic, Milton, van Hecke & Wegener, +"3D metamaterials," *Nature Reviews Physics* (2019). + +**Traditional design of experiments.** The NIST/SEMATECH e-Handbook of +Statistical Methods (free online, section 5) is the fastest practical +overview of factorial and response-surface designs; Montgomery's *Design +and Analysis of Experiments* is the standard textbook. The one-sentence +contrast for the talk: classical DoE fixes the whole test plan up front, +while Bayesian optimization re-plans after every batch using what it has +learned. + +**f(x) and x on the surrogate slide.** x is the design (for us, the vector +of five geometry parameters); f(x) is the measured objective at that design +(for us, e.g., transmissibility from a drop). The surrogate is a Gaussian +process fit to the (x, f(x)) pairs, giving both a prediction and an +uncertainty everywhere. Sterling's speaker notes on those slides are the +first thing to re-read; beyond that, Frazier's "A Tutorial on Bayesian +Optimization" (arXiv:1807.02811) sections 1 to 3 cover exactly the +surrogate + acquisition story the slides tell, and the Honegumi +documentation shows how our kind of campaign is set up in practice. +Pronunciation: "BAY-zhun," after Thomas Bayes. + +**Acrylic plate and shock transmission.** The physics concept to search is +mechanical impedance and stress-wave speed (sound speed in a bar is +sqrt(E/rho)). Stiff, light plates (magnesium and aluminum are the classic +shock-fixture choices, matching the recollection in the video) transmit a +sharp pulse with little distortion; acrylic (PMMA) is roughly 20 to 30 +times less stiff than aluminum and lossier, so it rounds the transmitted +pulse. *Harris' Shock and Vibration Handbook* has a chapter on shock-test +fixtures. Two useful framings for the talk if asked: both sensors measure +on the same fixture, so comparisons between specimens remain fair even if +the plate is not an ideal transmitter; and the input sensor sits below the +specimen, so the ratio brackets the specimen, not the plate. + +**Titan.** Saturn's largest moon, the target of the NIAC Super Ball Bot +mission concept in the clip. Already in the slide 3 speaker notes; NASA's +Titan page is the reference if a number (surface gravity, atmosphere) is +wanted. + +**Tandem exosuit.** The hook slide's speaker notes carry the source link +for the "Tandem Second Spine" image; check the vendor's page for what load +path it actually provides before characterizing it beyond "helps take load +off the back." + +--- + +## Slide-by-slide: what you want to say, and your notes + +### 1. Title slide ([0:00](https://youtu.be/-0qPEmmgSBA)) + +![Title slide](slide-walkthrough-2026-08-20/01-0010-title-slide.jpg) + +**Plan:** no time spent reading this slide; start right in on the hook. + +**Spoken script:** + +> You have a package, and you need to get that package to the surface of +> another celestial body. Doing this, you may face incredibly adverse +> conditions. A very thin atmosphere can make parachutes very difficult or +> impossible to work with. And the landing terrain is very rugged: even if +> you get your package to the surface, it might be hard for it to land +> upright, or even intact, on a jagged surface. You could add complex +> systems, retro rockets, parachutes. But what if you could just drop your +> package onto the surface and have it survive? That would be great, +> right? + +**Your notes:** + +- Find a natural word for planet-or-moon, since this clip is Titan, a moon + of Saturn. "Another celestial body" works but felt stiff. +- Option: have the Titan clip playing behind this narration (see slide 3). + +*Suggestion:* "another world" reads smoother aloud than "another celestial +body" and stays accurate for moons. + +### 2. NASA toy-throw clip ([2:25](https://youtu.be/-0qPEmmgSBA?t=145)) + +![NASA 360 clip slide](slide-walkthrough-2026-08-20/02-0150-nasa-toy-throw-clip.jpg) + +**Spoken lead-in:** + +> Researchers at NASA found, through testing under some of the most rugged +> and harsh conditions on Earth, a technology with incredible energy +> absorption capabilities, and it inspired them to build this +> tensegrity-inspired robot. + +Then the clip plays (Adrian Agogino's toy-throw bit). + +**Your notes:** + +- Possible joke afterward: "if you want to test whether a device survives + harsh conditions, give it to a child." By [5:22] you were leaning toward + excluding it. + +### 3. Titan descent clip ([3:55](https://youtu.be/-0qPEmmgSBA?t=235)) + +![Titan clip slide](slide-walkthrough-2026-08-20/03-0240-titan-descent-clip.jpg) + +**Your notes (two options, you leaned toward the second):** + +- Could omit this slide altogether, though "it's kind of fun to watch it go + down" and you can talk over it. +- Preferred: move this clip so it runs *while* you deliver the "you have a + package" narration from slide 1, and time it so the fall (starting about + 8 s into the clip) coincides with "what if you could just drop it onto + the surface." Practice the timing. +- Then transition: "Researchers at NASA were inspired by a super rugged + technology they found... it was able to take blow after blow," and play + the toy-throw clip. + +### 4. Hook slide: table, exosuit, Super Ball Bot ([5:29](https://youtu.be/-0qPEmmgSBA?t=329)) + +![Hook slide](slide-walkthrough-2026-08-20/04-0380-hook-slide.jpg) + +**Spoken script:** + +> If the only use for this principle were a planetary lander, maybe it +> wouldn't matter much to most of us. But tensegrity actually has a lot +> more applications. Maybe you've seen something like this: this floating +> table. Tensegrity artwork is a lot more commonplace than tensegrity +> mechanical devices, and honestly, this table is what I show my parents +> when they ask what I research. It also has a variety of other uses, like +> this exosuit, designed to help construction workers and people who lift +> a lot take part of the load off their back. And because of its +> principles, tensegrity is very useful for energy-absorbing devices. In +> fact, tensegrity is fantastic because it can compact down to a very +> small shape, and then deploy to its full form with all of its +> energy-absorption capabilities. + +**Your notes:** + +- Possible poll here: "who here has heard of tensegrity?" (mirrors the + Bayesian optimization poll later). +- Check exactly what the Tandem exosuit does before characterizing it. +- You noticed the anatomy slide comes later in the deck, and decided that + is fine: this slide previews, the definition lands two slides later. + +### 5. TensoLogic ball, compact and release ([9:00](https://youtu.be/-0qPEmmgSBA?t=540)) + +![TensoLogic ball drop video](slide-walkthrough-2026-08-20/05-0565-tensologic-ball-drop-video.jpg) + +**Your notes:** + +- Thinking of bringing the physical TensoLogic ball to the presentation + instead of (or alongside) this video, partly because in the video "you + get to see me struggle... with that wonderful noise." +- Live-prop plan: 3D print a clasp that holds it compacted, remove the + clasp while talking, give it a brief toss and catch, or let it bounce. +- Risk acknowledged: fumbling it when nervous. But "doing that live can be + really impactful." + +*Suggestion:* the clasp idea de-risks itself nicely: if the live release +ever feels shaky in rehearsal, the same clasp removal works seated at the +podium table, and the video stays in the deck as backup either way. + +### 6. Anatomy slide ([10:03](https://youtu.be/-0qPEmmgSBA?t=603)) + +![Anatomy slide](slide-walkthrough-2026-08-20/06-0605-anatomy-slide.jpg) + +**Spoken script:** + +> Let's take a second and just define what tensegrity is. Tensegrity is +> defined by having rigid struts suspended in a sea of tension elements. +> These green parts are the rigid elements, meaning they are constantly in +> compression; you can see the green arrows for where the forces are. The +> red parts represent cables that are in tension. You'll notice that none +> of the struts touch each other. They are all suspended in a state of +> self-tension. It's a bit like taking a spring and placing an object on +> top of it, so the spring is in a stressed state. The difference is that +> tensegrity devices are self-stressed: the stress is internal to the +> system, as if an object were resting on them, even before anything is. +> They are in a state of pre-stress. + +**Your notes:** + +- Delivery: "I have to really watch my arms during the presentation." +- Add the inextensible-cables point to your written definition: ideally + the tendons behave as inextensible cables. Steel wire is the classical + choice, but it makes the structure very stiff; softer cables can give + better energy absorption, at least in low-impact cases. (This sets up + the tensegrity-inspired definition later.) + +### 7. Steve Mould 2D clip ([11:25](https://youtu.be/-0qPEmmgSBA?t=685)) + +![Steve Mould clip](slide-walkthrough-2026-08-20/07-0690-steve-mould-clip.jpg) + +**Spoken lead-in:** + +> Now, this doesn't seem like it would be a very stable structure, but the +> physics behind it actually make it very, very stable, and it means any +> impact it undergoes gets dispersed through the structure really well. +> Let's take a look at a 2D tensegrity object to illustrate how this +> works. + +The clip plays (elastic bands stretch and restore equilibrium). + +**Spoken wrap-up after the clip:** + +> So tensegrity devices are actually quite stable, and any time you put a +> force on one, it distributes that force really well throughout the +> entire structure. Again: very good for energy absorption. + +**Your notes:** + +- Change the video to play automatically when the slide opens. + +### 8. Assembly is slow: TensoLogic 12x video ([12:52](https://youtu.be/-0qPEmmgSBA?t=772)) + +![TensoLogic 12x assembly](slide-walkthrough-2026-08-20/08-0810-tensologic-12x-assembly.jpg) + +**Spoken script:** + +> But not everything about tensegrity is ideal. The other properties are +> great: minimal mechanical grinding, because none of the parts touch; +> energy absorption, because impact disperses through the system really +> well. The biggest issue is that assembling these structures can be quite +> tedious. When you design them, you need every tendon cut to the right +> length so it has the right tension, and getting tension to be equivalent +> across the system is really, really difficult. You can see here that +> even with a kit, a simple T3 prism takes quite a bit of time, and if you +> scale up and add just a few more struts, the time it takes increases +> dramatically. So any time you want to design, build, or optimize these +> structures for a purpose, it takes a lot of time. It's a puzzle of +> figuring out how you are going to assemble it. That makes iteration +> very slow and makes these structures really difficult to optimize. + +**Your notes:** + +- "I'm obviously going to smooth that out. I'll probably write it out." + (This section is that write-out; edit in place.) +- The hidden "Tensegrity's unique properties" slide is omitted for good; + its two bullets (minimal grinding, force distribution) are now spoken + here and at the Steve Mould wrap-up. You agreed with Sterling that the + slide itself was awkward. + +### 9. 3D-printed tensegrity, tensegrity-inspired ([14:27](https://youtu.be/-0qPEmmgSBA?t=867)) + +![Tunable properties slide](slide-walkthrough-2026-08-20/09-1080-tunable-properties-slide.jpg) + +**Spoken script:** + +> Perhaps it's this difficulty in assembling and creating these structures +> that has led to a wider sub-genre: 3D-printed tensegrity and +> tensegrity-inspired structures. I want to pause here and explain what +> "tensegrity-inspired" means. When the word tensegrity-inspired is used, +> it denotes a structure or concept that takes inspiration from +> tensegrity in one way or another, but omits one or more of the classical +> conditions. Perhaps it is not in a state of pre-stress but takes +> inspiration from the topology, the geometry, so it looks like a +> tensegrity structure without being pre-stressed. Or perhaps it does not +> have those ideal, inextensible cables. +> +> [Two-material structure, to be shown first:] This is a tensegrity device +> that's a lot easier to assemble, and you can actually tune the stress in +> it by how long these bands are printed before they're stretched, which +> is really helpful for quick iteration. +> +> [Mono-filament structure:] And this structure, though made entirely of +> one material and not pre-stressed, behaved similarly to a tensegrity +> structure in its stress-strain curve under quasi-static compression, +> which is nonlinear. The point is that even tensegrity-inspired devices +> still exhibit some of the same ideal mechanical properties that a true +> tensegrity device has. + +**Your notes:** + +- Swap the order: right image (two-material, Filipe) first, then the + mono-filament Pajunen structure. +- Check the Pajunen material (a polyamide with a grade number) and decide + whether to name it. + +### 10. Dual-nozzle printing ([18:17](https://youtu.be/-0qPEmmgSBA?t=1097)) + +![Dual-nozzle slide](slide-walkthrough-2026-08-20/10-1125-dual-nozzle-slide.jpg) + +**Spoken script:** + +> We wanted to take a step towards pre-assembled, 3D-printed tensegrity +> structures with dual-nozzle printing. What we've created here is a +> dual-filament, printed-as-is structure that is tensegrity-inspired. +> Tensegrity-inspired in this case because it is not pre-stressed; but +> instead of being all one filament, it has TPU, thermoplastic +> polyurethane, which has good damping properties, and these rigid PLA +> struts. So it takes heavy inspiration from the topology of a tensegrity +> structure, and it keeps part of the definition in that it has two +> materials: one that can act in tension when the structure is stressed, +> and the struts acting in compression. + +**Your notes:** + +- "This is the part I'm currently stumbling over most": why is this + important? The framing you worked out on camera: print-in-place + dual-filament structures with tensegrity topology are themselves + somewhat novel (some professors we talked to didn't think it would be + possible at all), but similar printed structures do exist, so the real + novelty is not these objects; it is the intentional closed-loop + optimization between them. "Something people have not done is an + intentional closed-loop optimization process on these + tensegrity-inspired structures, to get the geometry as ideal as + possible. That is our entire goal." +- Double-check the TPU expansion and its damping properties before + saying them. + +*Suggestion:* that novelty sentence is strong enough to be the slide's +transition: "...struts acting in compression. Others have printed +structures like these. What no one had built is a loop that optimizes +them, and that loop is the rest of this talk." + +### 11. Closed-loop workflow diagram ([20:56](https://youtu.be/-0qPEmmgSBA?t=1256)) + +![Closed-loop diagram](slide-walkthrough-2026-08-20/11-1260-closed-loop-diagram.jpg) + +**Spoken script:** + +> Our goal is to create these tensegrity-inspired structures, to be able +> to print them at all, and then to put them through a closed-loop +> optimization process to get the best energy and shock absorbers. We +> start with that goal, and then we design our experiments: the test we +> will measure them with. We make the materials, printing the structures. +> We test their performance. And then we loop: the results of each round +> determine where we test next, over and over, until we get a structure as +> optimized as possible, or we are satisfied, and we conclude the +> campaign. + +**Your notes:** + +- "Probably the most useful diagram I've ever seen AI come up with." The + bottom-right node looked briefly funky to you, then fine. +- Still needs a worked-out introduction sentence for the slide. +- The "until funding runs out" joke: undecided (it lands at Sterling's + expense; check with him). +- Later thought ([28:32]): maybe move this slide toward the BO side, walk + through each loop step as its own beat, and maybe add a printing slide + in the sequence. You wanted feedback on this before rearranging. + +### 12. Search space: five geometry parameters ([23:00](https://youtu.be/-0qPEmmgSBA?t=1380) and [36:05](https://youtu.be/-0qPEmmgSBA?t=2165)) + +![Search space slide](slide-walkthrough-2026-08-20/20-2220-search-space-parameters.jpg) + +**Spoken script (second, fuller pass):** + +> Because we're 3D printing them, we can control all the parameters of the +> structure. We can adjust the height of the model, the cable diameter, +> the triangle radius, the strut diameter, and the angle of twist between +> the triangles. And when we do this, we set specific geometric limits, +> for example the height can go from 60 to 110 millimeters, because some +> combinations of these are physically impossible. This is the search +> space in which we want to optimize our structure. Here are four of the +> nine starting designs, and these are the kind that we take and run +> through the drop tests. + +**Spoken script (first pass, framing):** + +> Let's look first at the parameters we're adjusting to optimize these +> structures. The T3 prism is relatively simple, and that's on purpose: we +> want to ensure the process works here so that we can apply it to other +> structures. + +**Your notes:** + +- You saw the note in the speaker notes ("Marcus: feel free to move, + retitle, or hide this slide") and decided the slide is helpful and + stays. +- The stepping-stone framing needs to land *earlier* in the talk: 3D + printing is not the end goal; it is a stepping stone to quick iteration + and testing, and the future targets are metamaterial-like lattices + (Dr. Hill's interest, e.g., energy absorbers for package delivery, + compared against foam) and crutch or walking-stick tips, so a real + device made from this could be optimized. "It is a stepping stone in + optimization; that is something to establish earlier on." +- Look up a proper definition of metamaterial before using the word. + +### 13. Drop tower and instrumentation ([29:10](https://youtu.be/-0qPEmmgSBA?t=1750)) + +![Drop tower slide](slide-walkthrough-2026-08-20/15-1805-drop-tower-slide.jpg) + +**Spoken script:** + +> We want to optimize for energy absorption and shock response, so we set +> up a drop tower, where we can consistently drop from exactly the same +> height and deliver a nearly identical shock every time. In this video +> you can actually see my hand under the table taking the slow-motion +> video; we have a much safer way to do that now, a little more remotely. +> For our experiment, we take our specimens, strap them to this acrylic +> plate, and place two accelerometers: one at the top of the specimen and +> one at the bottom. That gives us the difference in acceleration between +> them when we drop. So from each drop we have a record of the +> acceleration that is input at the bottom of the specimen and the +> acceleration experienced at the top. We also capture slow-motion video. + +**Your notes:** + +- The hand-under-the-table aside is a keeper ("I think that's a good + job/joke to put in there"); the "partially for fun" line about slow-mo, + maybe not. +- Look into the acrylic plate: not fully confident on the material + science of how well acrylic transmits shock; recollection is that + magnesium is most ideal, aluminum second. +- Double-check what the slow-motion video actually tells us, so it can be + described accurately. + +### 14. Data slides: jolt, ringing, attenuation ([31:37](https://youtu.be/-0qPEmmgSBA?t=1897)) + +![Jolt and ringing slide](slide-walkthrough-2026-08-20/17-2010-jolt-ringing-slide.jpg) + +**What you want these slides to say:** + +> We take the highest acceleration that the top of the structure +> undergoes, and compare it to the highest acceleration that the bottom of +> the structure undergoes, as a ratio between the two. If that number is +> greater than one, the shock is being amplified through the structure; it +> is worse at absorbing it. If it is less than one, and the closer it gets +> to zero (it will never reach zero), the better the structure is +> absorbing the shock. That is attenuation. We also look at how the shock +> reverberates through the structure in the milliseconds following: the +> quicker the oscillation dies back toward zero, the better the structure +> is absorbing that shock. And that is ultimately what we are looking to +> optimize: find the structure that best does that. + +**Your notes:** + +- "These slides definitely need some work" / "a little messy." Wanted: + marker lines coming from the top of each peak (top sensor and bottom + sensor) with the ratio shown, possibly written as math. +- Define attenuation and transmissibility properly (see + [terms](#terms-to-clarify-and-where-to-look)); double-check the exact + metric and the filtering. +- "I don't know why there's two of these": the stored deck has the jolt + slide twice (slides 20 and 21) plus an untitled leftover (slide 19) + whose figure still has the old "thousandths of a second (ms)" axis. + Delete the duplicates. +- On the SAE J211 filter slide ([34:04]): "Is this AI's notes on this? + Thank you, Claude." Note kept: understand the filter, decide whether to + mention it aloud. + +![Filter slide](slide-walkthrough-2026-08-20/18-2070-j211-filter-slide.jpg) + +### 15. Make materials: printing details ([34:37](https://youtu.be/-0qPEmmgSBA?t=2077)) + +**What you want to add (possibly a new slide between the test-setup and +data blocks):** + +> The next part is how we make our structures. We 3D print them as-is, in +> place. We've tested various methods for supports to get TPU to print +> well. TPU is hygroscopic, so it has to be kept dry, and because it's +> softer it jams in the printer more often. So we've built a relatively +> specialized setup out of on-the-market tools, and that's led to very +> consistent, relatively quick prints of these structures. + +**Your notes:** + +- Considered a mini navigation graphic (the loop diagram in a corner with + the current stage highlighted) and decided it is probably unnecessary + noise; say it vocally instead. + +### 16. Bayesian optimization block ([37:44](https://youtu.be/-0qPEmmgSBA?t=2264)) + +![BO intro slide](slide-walkthrough-2026-08-20/21-2310-bo-intro-slide.jpg) + +**Spoken script (intro slide):** + +> We run these through our drop tests, but now we need a way to determine +> which structure is best and where we should test next. We could just do +> blind testing, thousands of structures across these parameters; we could +> go on for a very long time, and it would be very expensive in time and +> resources. We don't have infinite funding and we don't have infinite +> time. We need an optimization method that works well in the limit of low +> data and many variables. Who here has heard of Bayesian optimization? +> Traditional design of experiments, on this graph, only starts to +> optimize out here, and you can see it would take a long time to reach a +> true optimum of this property. Bayesian optimization, by around twelve +> to fifteen experiments, has already started this very steep improvement. +> And that is incredibly ideal for us. + +**Spoken script (surrogate slide):** + +> How does it work? You have a surrogate model, which is basically our +> best guess at reality. We are building a model to fit reality, and each +> test that we run and gather data on helps us pinch the model down toward +> what reality is. The black dots here are where we've made observations, +> and the bars extending from them are the standard deviation of our +> experiments, so there is some noise. As each point is added, the +> surrogate follows the true model really well. + +**Spoken script (acquisition and loop):** + +> The global optimum in the true model is what we want to discover, and we +> don't actually know the true model. The acquisition function helps us +> determine where we should test next. There are multiple ways of reading +> it, but all we need to know is that interpreting it tells us where to +> test next, we add data there, refit the surrogate to all of the data, +> and with just a few points we understand reality really well. + +**Your notes:** + +- Poll: "who here has heard of Bayesian optimization?" (hoping lab partner + Chenquan raises his hand). Check pronunciation. +- Review the definition of traditional design of experiments. +- Re-review Sterling's speaker notes, especially what f(x) and x mean; + "much better ways of wording" the loop close wanted. +- Sterling may have opinions on how this block is introduced. + +### 17. "By connecting these ideas" ([42:04](https://youtu.be/-0qPEmmgSBA?t=2524)) + +![Connect ideas slide](slide-walkthrough-2026-08-20/23-2540-connect-ideas-workflow.jpg) + +**Spoken script:** + +> With this, Bayesian optimization allows us to complete our loop fully. +> We can very quickly print the materials we've designed, run them through +> our designed experiment, and run the results through the Bayesian +> optimization process. By connecting all these ideas, we're able to +> iterate really quickly in this closed-loop optimization process. + +**Your notes:** + +- Unsure the slide reads well: the drop-tower photo is "a little janky" + and the right-hand icon is "just a random symbol for a neural network." + The note already typed in the slide proposes: printed-tensegrity image + for the print step, drop-tower image for testing, black-box image for + the BO step. + +### 18. Future applications ([43:15](https://youtu.be/-0qPEmmgSBA?t=2595)) + +![Future applications slide](slide-walkthrough-2026-08-20/24-2610-future-applications.jpg) + +**Spoken plan:** reiterate how the workflow expands: lattice structures, +then the crutch and walking-stick tips ("this I'm a little more familiar +with"). + +**Your notes:** + +- Look up the lattice paper cited on the slide (Al Sabouni-Zawadzka et + al. 2025) or find a better example image of a lattice structure; right + now it is only serving as "what a lattice looks like." +- Crutch tips: talk about partnering with them (team discussion item). + +### 19. Closing and acknowledgments ([43:55](https://youtu.be/-0qPEmmgSBA?t=2635)) + +![Closing logo slide](slide-walkthrough-2026-08-20/25-2695-logo-slide-pixelation-note.jpg) + +**Candidate closing line:** + +> The faster we are able to iterate and advance ourselves, the better life +> we can enjoy living now, because our time is limited. + +**Your notes:** + +- Not sure if that is a good close or cheesy; decide. +- Wants some way to credit the team, Chenquan, Audrey, Sterling, and + Dr. Hill, "because obviously I am not the only one doing this work," + whether or not it is spoken aloud. Aware Sterling may consider a name + list noise; flagged for discussion. +- Keep a slide up during questions. +- The VCL + BYU logo image is too pixelated; replace with higher quality + (note already typed on the slide). + +*Suggestion:* the Doumont-style alternative to a general aphorism is to +bookend: put the Titan lander image back up and close with the concrete +version, e.g., "the next time someone needs to drop a package on another +world, a loop like this can hand them a tested design in weeks." Keeps +your sentiment, loses the cheese risk. + +--- + +## Appendix: voice-to-text corrections + +Recurring mishaps in the auto-transcript, corrected throughout this +document: + +| Transcript said | Intended | +|---|---| +| "10-sacrity", "10 segredease", "tense equity", "10 Saturday" | tensegrity | +| "planetary ladder" | planetary lander | +| "regatrain" | rugged terrain | +| "two laws of printing" | dual-nozzle printing | +| "closed the federation process" | closed-loop optimization process | +| "thermo-polyurethane" | thermoplastic polyurethane (TPU) | +| "polymide with a specific like steel number" | a polyamide with a grade number (check the Pajunen paper) | +| "in the way of low low data" | in the limit of low data | +| "text and test next" | test next | +| "5th 1215 experiments" | about 12 to 15 experiments | +| "Chinquan" / "Chenquan" | lab partner's name, spelling to confirm | +| "Sterling Berg" | Sterling Baird | + +Transcription was done locally with faster-whisper (small model) on the Box +download; the YouTube auto-captions were not used. 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b/presentation/slide-walkthrough-2026-08-20/24-2610-future-applications.jpg differ diff --git a/presentation/slide-walkthrough-2026-08-20/25-2695-logo-slide-pixelation-note.jpg b/presentation/slide-walkthrough-2026-08-20/25-2695-logo-slide-pixelation-note.jpg new file mode 100644 index 00000000..bef2efe5 Binary files /dev/null and b/presentation/slide-walkthrough-2026-08-20/25-2695-logo-slide-pixelation-note.jpg differ diff --git a/presentation/supplement-slide-map.md b/presentation/supplement-slide-map.md new file mode 100644 index 00000000..1603da26 --- /dev/null +++ b/presentation/supplement-slide-map.md @@ -0,0 +1,93 @@ +# Supplement deck → Draft 1 map + +The supplement deck **adds to** `IDETC Tensegrity Slides Draft 1.pptx`. Nothing in +Draft 1 is replaced or deleted by it. Where an earlier review recommended hiding +one of @me-madsen's slides, that recommendation is withdrawn — the content is +restored below, with the media it was waiting for. + +| Supplement slide | Draft 1 slide it serves | Status | +|---|---|---| +| 2 — tensegrity lander hook (Titan descent clip) | 2 (hook) | fills the "just the fall" video that the presenter note asked for | +| **3 — what tensegrity is (Steve Mould 2D teaching clip, 35 s, with sound)** | **3 (was hidden)** | **restored, and now the background block.** The full teaching excerpt, not the 18 s silent crop that cut off the push-and-recovery | +| **4 — anatomy: struts in compression, cables in tension** | 3 | new: the mechanism visual the mock audience said the grad student needed | +| 5 — baby toy → landing robot (NASA 360 clip, **with sound**) | 2 / 3 | new: the origin story in the researcher's own words — he throws a tensegrity baby toy at the floor and calls it a landing robot | +| 6 — reusability (SUPERball 3.4 m drop clip) | 2 / 4 | new: the "survives repeated drops" argument, which nothing in Draft 1 carried | +| 7 — prior work | — | new: the missing prior-work beat | +| 8 — the gap | 4 (need) | new: the motivation → gap → solution transition | +| 9–19 — EMC Bayesian-optimization block | **5 (was hidden)**, 7 | **restored.** Sterling's originals, animations intact | +| 20 — tensegrity-inspired caveat | 8 (multi-material printing) | plays immediately after slide 8, in the same breath | +| 21 — print timelapse (H2D, two materials, one build) | 8 | new: our own 16 s timelapse, pulled through the lab Pi | +| 22 — supports still come off by hand | 8 | new: challenge/mitigation beat for the AM section | +| 23 — the whole experiment in real time (**with sound**) | 9 | new: the from-afar drop @sgbaird supplied | +| 24 — phone slow motion (**with sound**) | 9 | new: the cheap instrument, and what it cannot do | +| 25 — what the 960 fps camera is actually for | 9 | fills the "slo motion of drop test" placeholder | +| 26 — elastic snap-back at 0.7× impact speed | 9 → 12 | new: reusability argued with **our** data, not NASA's | +| 27 — impact sequence montage | 9 | fills the "looped gif of drop test at 60 in" placeholder | +| 28 — camera vs. DAQ: which instrument owns the pulse | 9 → 11 | new: measured evidence, sets up the results block | +| 29 — drop tower and payload instrumentation | 9 | fills the setup half of the slide | +| 30 — SAE J211 filtering → three objectives | 7 / 9 | connects the measurement to the objective function | +| **31–36 — the issue #94 block** | 9 / Q&A | new: opens the drop-tower analysis to spot-checking — see [`issue-94-analysis-slides.md`](issue-94-analysis-slides.md) | +| 37–38 | — | working slides; delete before the talk | + +Draft 1 slide 6 (specimen information value / experiment-first) stays a slide of +its own. It is the pre-emptive answer to the sharpest predicted Q&A challenge, +and merging it into slide 4 would bury it. + +## Media + +Everything on these slides is real and embedded — no dashed placeholders, no +internet needed at the podium. Sources and crops are in +[`build_supplement_deck.py`](build_supplement_deck.py); the clips themselves are +in [`media/`](media/). + +| Clip | Source | Length | Audio | +|---|---|---|---| +| `clip-titan-descent.mp4` | NASA Super Ball Bot / NIAC Titan concept | 20 s | — | +| `clip-tensegrity-2d-teaching.mp4` | Steve Mould, "Tensegrity Explained" | 35 s | **yes** | +| `clip-nasa-toy-lander.mp4` | Adrian Agogino, "NASA 360 Talks: Super Ball Bot" | 16 s | **yes** | +| `clip-superball-3m-drop.mp4` | SUPERball v2, IEEE Spectrum / NASA Ames | 10 s | — | +| `clip-print-timelapse.mp4` | our own print timelapse, TT3_01 (`nQNmi-NiL5I`) | 16 s | — | +| `clip-support-removal.mp4` | our own footage, manual support removal (PR #35, 2026-06-09) | 12 s | — | +| `clip-drop-afar.mp4` | our own footage, whole-room drop (@sgbaird attachment `b16e3d32`) | 7 s | **yes** | +| `clip-drop-phone-audio.mp4` | our own phone slow-mo, specimen `n0jdwk`, 13 in (PR #67) | 3.3 s | **yes** | +| `clip-our-slomo-drop.mp4` | our own 959 fps footage, prc1kn 60 in / 5 felt | 9 s | — | +| `clip-drop-highspeed.mp4` | our own 960 fps footage, specimen `7xadt6`, 60 in / 5 felt (PR #86 branch) | 6 s | — | + +The four external clips carry an on-slide credit line. The NASA footage needs +only "Credit: NASA"; the Steve Mould and IEEE Spectrum clips are copyrighted and +keep channel + title + URL on screen while they play. The six clips of our own +work carry a provenance line pointing at the campaign they came from. + +**Four clips are played with the sound on** — the 2D teaching model +(slide 3), the NASA 360 origin story (slide 5), the whole-room drop (slide 23) +and the phone slow-mo (slide 24). On the teaching clip the narration *is* the +explanation, so say nothing over it. On the two drops the bang is doing real +work: it conveys how violent a millisecond-scale event this is before any plot +appears. Everything else plays silently. + +One figure is generated rather than photographed: +`media/fig-tensegrity-anatomy.png`, the labelled anatomy on slide 4, is drawn by +`build_anatomy_figure()` in `build_supplement_deck.py` from a still of the same +teaching model (`media/photo-tensegrity-2d-model.jpg`). + +## Still needed + +Drop-tower photo, and the campaign ledger / Pareto-front +figures once the campaign closes, and the Colab notebook owed to issue #94. +Slide 38 tracks these. + +## Title slide + +The presenter is **Marcus Madsen, Research Assistant**. That block sits on Draft 1 +slide 1, but the EMC template also keeps a presenter block on the *Title Slide +layout* and the talk title on the *slide master* — which is why Draft 1 slide 14 +used to show Sterling's EMC title and affiliation. `set_presenter.py` stamps the +right identity onto every deck and onto both build sources (`emc-bo-block.pptx`, +`emc2026-bare-template.pptx`), so a rebuild cannot bring the EMC text back. + +Rebuild with: + +```bash +python presentation/build_supplement_deck.py +python presentation/set_presenter.py # run last; it stamps the built decks +``` diff --git a/presentation/tensologic-slide-insert-screenshot.png b/presentation/tensologic-slide-insert-screenshot.png new file mode 100644 index 00000000..bff48c60 Binary files /dev/null and b/presentation/tensologic-slide-insert-screenshot.png differ diff --git a/presentation/video-snips.md b/presentation/video-snips.md new file mode 100644 index 00000000..66a722fa --- /dev/null +++ b/presentation/video-snips.md @@ -0,0 +1,84 @@ +# Video snips for the IDETC presentation + +Source clips live in the shared Box folder (`tensegrity`): + + +Each file is named `youtube-.mp4` after the YouTube video it was snipped +from. The snips are not committed to the repo (size); this file records what each +snip is, how it relates to the full source video, and the attribution needed if +shown in the talk. + +## Correlation: snip ↔ source video + +| Snip file | Snip length | Source video | Channel | Full length | Published | Coverage | +|---|---|---|---|---|---|---| +| `youtube-0eC4A2PXM-U.mp4` (87.7 MB) | 2:02 | [NASA 360 Talks – Super Ball Bot](https://www.youtube.com/watch?v=0eC4A2PXM-U) | NASA eClips – ARCHIVE | 2:21 | 2015-02-19 | ~87% of the video | +| `youtube-0onncd0_0-o.mp4` (25.3 MB) | 0:35 | [Tensegrity Explained](https://www.youtube.com/watch?v=0onncd0_0-o) | Steve Mould | 11:06 | 2021-01-14 | ~5% excerpt | +| `youtube-L2cJej3EmcA.mp4` (10.2 MB) | 0:35 | [NASA Demonstrates Super Ball Bot Prototype](https://www.youtube.com/watch?v=L2cJej3EmcA) | Wall Street Journal | 0:45 | 2015-11-19 | ~78% of the video | +| `youtube-hkzeE6BVNIk.mp4` (59.4 MB) | 1:26 | [Design of SUPERball v2, a Compliant Tensegrity Robot](https://www.youtube.com/watch?v=hkzeE6BVNIk) | IEEE Spectrum | 1:31 | 2019-05-31 | ~95% (nearly full) | + +All four snips: H.264 video + AAC audio, 30 fps, ~1586×892 (screen-capture +resolution, not the native player resolution). + +## Cuts embedded in the supplement deck + +| Deck clip | Cut from | Window | Why | +|---|---|---|---| +| `media/clip-titan-descent.mp4` | NIAC mission-concept animation | 13–33 s | hook | +| `media/clip-tensegrity-2d-teaching.mp4` | Steve Mould snip | 0–35 s (whole snip) | background: what a tensegrity is | +| `media/clip-superball-3m-drop.mp4` | IEEE Spectrum snip | 45–55 s | reusability | +| `media/clip-nasa-toy-lander.mp4` | `youtube-0eC4A2PXM-U.mp4` | 0–16.4 s | baby toy → landing robot | + +The Steve Mould and NASA 360 cuts are played **with sound**. The Mould snip is +used whole rather than trimmed: an earlier 8–26 s crop cut off the push and the +spring-back at the end, which is the part that teaches. Transcript of the cut +(Adrian Agogino, NASA Ames): + +> "When you took one of these toys and we said, what can we use this for? Well, +> they make them as baby toys because they're really safe, it's hard to break +> them, hard to hurt yourself with it … and you can throw it on the ground really +> hard and you're not going to break it, you're like, hey, that's a landing +> robot." + +He reaches the phrase "planetary landers" verbatim at 24.5–31 s of the same snip +("we're using those to build robots or planetary landers that can land on another +planet"), which is the longer cut to use if the tighter one feels too abrupt. + +## Clips embedded in idetc-2026.pptx (the OneDrive deck) + +| Deck clip | Source | Length | Where | +|---|---|---|---| +| `media/clip-tensologic-fold-release.mp4` (4.2 MB) | [Super Ball Bot-style Tensegrity structure (tensologic), folding together and releasing](https://www.youtube.com/watch?v=y8KjBRH4Wis), BYU Vertical Cloud Lab, uploaded 2026-08-19 | 0:11, 1080p, 30 fps | Slide 5 (its own slide, right after the hook), inserted 2026-08-19 via the web editor | + +The clip is our own footage (Marcus in the lab folding a TensoLogic Super Ball +Bot-style structure flat and letting it spring back), so no external +attribution is needed; the source link lives in the slide's speaker notes. The +video is used whole. It was downloaded through the Pi because YouTube blocks +the Actions runner. + +## Source video descriptions (for slide captions / attribution) + +- **NASA 360 Talks – Super Ball Bot** — "Could the next planetary rover be a + Super Ball Bot? There are several advantages to a ball-like design and that's + why NASA's Innovative Advanced Concepts program is studying its feasibility." + ~99k views. +- **Tensegrity Explained** (Steve Mould) — "Tensegrity (or tensional integrity, + or floating compression) is really counterintuitive. These bizarre structures + can be explained quite nicely with a 2D version." ~11M views. Good candidate + for the audience-facing "what is tensegrity" moment (mechanism intuition). +- **NASA Demonstrates Super Ball Bot Prototype** (WSJ) — "NASA released footage + of a prototype Super Ball bot, a robot in development for future space + exploration missions. Photo: NASA." Real prototype drop footage. +- **Design of SUPERball v2** (IEEE Spectrum) — video accompanying "Design of + SUPERball v2, a Compliant Tensegrity Robot for Absorbing Large Impacts" by + Vespignani, Friesen, SunSpiral, and Bruce (IROS 2018). Engineering detail on + the v2 robot. + +## Attribution notes + +- The two NASA-produced clips (NASA 360, and the NASA footage inside the WSJ + clip) are NASA media — generally usable with credit ("Credit: NASA"). +- The Steve Mould and IEEE Spectrum clips are copyrighted; short excerpts in a + conference talk with on-slide credit (channel + title + URL) is the norm. +- Put the credit line on the slide where the clip plays, per Doumont: small, + unobtrusive, but present.