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REVIEW 4 major objections 5 minor 55 references

The paper claims a single pipeline turns one paper PDF into an editable poster, narrated video, and bilingual blog in one navigable viewer, with posters scoring above the authors' own on aesthetics under two automated judges on 100 papers.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-08-02 08:38 UTC pith:BJLZ3FOH

load-bearing objection A carefully engineered paper-to-poster/video/blog pipeline whose headline superiority over human posters is real only under two VLM judges from the same model families; valuable as an engineering contribution, not as evidence of human aesthetic preference. the 4 major comments →

arxiv 2607.04438 v2 pith:BJLZ3FOH submitted 2026-07-05 cs.CV cs.AIcs.HCcs.MAcs.MM

ResearchStudio-Reel: Automate the Last Mile of Research from Paper to Poster, Video, and Blog

Lingao Xiao , Yalun Dai , Yangyu Huang , Qihao Zhao , Wenshan Wu , Hugo He , Ruishuo Chen , Jin Jiang
show 12 more authors
Qianli Ma Jiahuan Zhang Xin Zhang Ying Xin Yang Ou Yan Xia Scarlett Li Longbo Huang Zhipeng Zhang Yang He Yap Kim Hui Yan Lu
This is my paper
classification cs.CV cs.AIcs.HCcs.MAcs.MM
keywords paper-to-posterpaper-to-videopaper-to-blogdissemination automationmeasured-fill loopsection-level alignmentnative-editable deliverablesvision-language model evaluation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tries to establish that the last mile of research dissemination — turning an accepted paper into a conference poster, a talk video, and a blog post — can be automated as a single editable, navigable workspace, not as three disconnected one-off renders. Its system reads the paper PDF once into a shared asset bundle, then produces an editable PowerPoint poster, a narrated video with an editable slide deck, and a bilingual Word blog, all cross-linked in an interactive viewer that maps poster regions to video segments and blog passages. The quantitative claim is that on a 100-paper benchmark judged by two vision-language models, the automated posters score above the authors' own posters on average aesthetics (3.56 vs 3.03) and win on overall quality on 74 and 95 of the 100 papers under the two judges. A reader should care because the artifacts are native-editable source files: an author can fix a typo, swap a figure, or re-cut a slide without regenerating the whole pipeline.

Core claim

The paper claims that a five-skill architecture can automate the last mile of dissemination while keeping outputs editable: one shared extractor parses the paper PDF once into a bundle of cleaned figures, captions, metadata, a nine-section summary, and narration clips with stable IDs; three generators consume that bundle verbatim to emit an editable PowerPoint poster, a narrated video with an editable deck and a timeline sidecar, and a bilingual Word blog; and a convergence layer reads the alignment record to bind them into one poster-first viewer. The poster generator's distinctive mechanism is a measured-fill loop that measures each section's fill ratio, maps it to one of five categorical

What carries the argument

Key machinery: the shared asset bundle and the section-level alignment record. The bundle (the paper's text, cleaned figure crops, captions, metadata, a nine-section summary, and narration clips, all with stable section IDs and figure handles) is extracted once and consumed verbatim by all three generators, so the figure in the poster is the figure in the video and blog. The alignment record is a sidecar that maps each canonical section ID to its poster block, slide targets, video timestamps, subtitle tracks, and blog passages; the convergence layer reads this sidecar instead of inferring boundaries from pixels. Supporting these is the poster's measured-fill loop, a categorical controller th

Load-bearing premise

The headline comparison assumes the two automated judges' aesthetic ratings are fair and unbiased even though the same model family both generates a configuration and scores the output; the paper itself says these aesthetic numbers are a proxy signal, not proof.

What would settle it

Have an independent panel of human raters, blind to provenance, score the same 100 poster pairs with the same six-criterion rubric; if the authors' own posters win on aesthetics or overall quality, the central claim collapses. A cheaper mechanistic check: cross-score outputs, letting the judge used in the system's own configuration score the other family's outputs and vice versa, and test whether same-family scoring systematically inflates scores.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • A single extraction pass means the figure in the poster is the same figure in the video and blog; no downstream generator re-crops or re-parses the paper.
  • The native-editable source files are first-class deliverables, so authors can revise a poster, deck, or blog in PowerPoint or Word without regenerating the other artifacts or losing alignment.
  • Poster convergence is auditable: the fill loop terminates only when every section sits in the 90–98% band and every figure meets its size floor, and a circuit breaker ships the best-measured state instead of grinding forever.
  • The video's timeline sidecar keeps sections addressable after export, letting the interactive viewer seek by section rather than infer boundaries from the MP4.
  • The poster-quality advantage transfers across two different model/harness settings, indicating the workflow rather than a single model carries the result.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The shared-bundle-plus-alignment pattern is a transferable delivery contract: the same architecture could produce other native-format outcomes (teaching slides, lay summaries, briefing packs) without redesigning the pipeline.
  • The measured-fill loop generalizes beyond posters: any fixed-canvas layout task (slides, infographics, one-page reports) can be driven by the same five-state categorical controller with one deterministic move per state.
  • A human reading-and-recall study, which the paper leaves for future work, would settle whether the PaperQuiz-versus-aesthetics inversion reflects a genuine tradeoff or an artifact of exact-match grading; if human recall tracks the aesthetic scores, the fill loop's density target may be miscalibrated.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper describes ResearchStudio-Reel, a skill-based pipeline that takes a paper PDF through a shared extraction stage (Paper2Assets) and produces a PowerPoint poster, a narrated video with editable deck, a bilingual Word blog, and a Paper2Reel interactive viewer that binds poster, video, and blog at the section level. The main empirical claim is on the Paper2Poster benchmark: under two VLM judges, the Claude Code configuration achieves 3.56 average aesthetics versus 3.03 for author posters, wins overall on 74/100 and 95/100 papers, and is best among automated systems on the aesthetic criteria. The paper also contributes deterministic release gates, a measured-fill poster loop, operational profiling, and capability audits for video and blog. Video and blog quality are not quantitatively evaluated, and the paper explicitly frames the aesthetic numbers as a VLM proxy signal.

Significance. If the poster-quality result were independently confirmed, the system would be a substantial engineering advance: one shared asset bundle feeds native-editable PowerPoint and Word artifacts, aligned through an interactive convergence layer, with deterministic package gates that make the delivery contract checkable. Strengths of the evaluation include the external Paper2Poster benchmark, true-size rendering before scoring, two judges from different model families, per-paper win-rate computation, a clear discussion of the PaperQuiz/aesthetics tension, and unusually candid limitations. The central weakness is that the headline comparison is in-family: the primary configuration is generated by claude-opus-4.8 and scored by claude-opus-4.8, the Codex configuration by gpt-5.5 and scored by gpt-5.5, and the pipeline is explicitly optimized to the rubric used by these judges. The paper's own caveats are appropriate but do not overcome the evidentiary gap.

major comments (4)
  1. [§3, Table 1; §2.2] The headline claim that ResearchStudio-Reel (Claude Code) 'exceeds the authors' posters in average aesthetics (3.56 vs. 3.03)' and wins 74/100 and 95/100 is evaluated exclusively by two VLM judges, claude-opus-4.8 and gpt-5.5. The primary configuration is generated by claude-opus-4.8 and scored by claude-opus-4.8; the Codex configuration is generated by gpt-5.5 and scored by gpt-5.5. Moreover, the measured-fill loop and the composition axes in §2.2.2 (A2–A5) are explicitly engineered to satisfy the same six-criterion rubric used by these judges. The paper's disclaimer in §3 Analysis that the aesthetic numbers are 'a proxy signal rather than proof' is appropriate, but the abstract and conclusion state the result without that nuance. This is load-bearing: the claim is currently an in-family, metric-optimized result. Please add a third-party human-rater study, or at minimum a third-family j
  2. [§3, Table 1 note †] The comparison against prior automated systems and the author ground-truth depends on baselines that are 'reproduced with best efforts and scored by us under Claude Code with claude-opus-4.8.' Because the prior systems are stochastic agentic pipelines, 'best efforts' is not a controlled condition; as reported, the 'best among automated systems' and the win-rate claims cannot be independently checked. Please release per-paper scores and generated poster files for all systems, or use the original benchmark's reported scores where available, and specify the exact version and configuration used for each reproduced baseline.
  3. [§3, 'Capability coverage'; Appendix A] The paper's title and contribution list promise poster, video, and blog, but quantitative evaluation is limited to posters. Tables 2 and 3 are feature checklists, and Appendix A explicitly states that video and blog are not evaluated and that no human editing-effort or navigation-understanding study exists. The 'last mile automation' claim for the full workspace is therefore supported only by existence checks. Please add targeted quantitative evaluations for video (duration accuracy, caption/timeline alignment, visual-cue correctness) and blog (bilingual fact consistency, figure placement, layout defects), or substantially soften the abstract and conclusion claims to match the evidence.
  4. [Appendix C] All reported runs went through a private Copilot API proxy, and the model identifiers 'name the underlying models as served by that proxy.' This makes exact reproduction of the benchmark numbers impossible and leaves open the possibility that proxy-specific behavior contributed to the scores. Please provide the exact proxy configuration/version and, for the headline numbers, run the same pipeline on first-party endpoints or release the full run logs.
minor comments (5)
  1. [Table 4 caption] The caption says 'claude-opus-4-8' while the rest of the paper uses 'claude-opus-4.8'; please harmonize.
  2. [§5.2] The system's skill is named Paper2Video, which is also the name of the external benchmark/system in reference [4]. This naming collision is confusing in the related-work discussion; please disambiguate, e.g., by referring to the external work as 'Paper2Video [4] (benchmark)'.
  3. [Figure 10] The figure includes 'Claude Code (claude-opus-4.7)' and 'Claude Code (claude-opus-4.6)' panels, but these model versions are not listed in Table 1 and are not described in the text. Please state what these panels illustrate and why they are not part of the quantitative comparison.
  4. [§3, per-paper win rate] The win rates are reported as 74/100 and 95/100 with percentages of non-tied papers. Please also report the number of ties and losses so the head-to-head distribution is fully specified.
  5. [§2.2.4, RRP gate] The Reader-Reconstruction Preference gate is described as an optional in-loop signal but no result using it is reported. Please state whether it was enabled for the Table 1 poster runs, and if so, what effect it had.

Circularity Check

0 steps flagged

No significant circularity: the headline poster result is evaluated against an external benchmark and rubric, not derived from the system's own inputs or self-citations.

full rationale

ResearchStudio-Reel is a systems paper; its only load-bearing quantitative claim is the Paper2Poster benchmark comparison. That benchmark, the six-criterion aesthetic/information rubric, and the PaperQuiz probe are external (Pang et al., [1]) and are reused verbatim, with per-judge breakdowns disclosed in Appendix D and a transparent definition of the overall score as the mean of the six rubric sub-criteria. No parameter is fitted to the benchmark and then reported as a prediction: the measured-fill loop optimizes a geometric fullRatio (h_content/h_card), not the VLM aesthetic score, and the optional RRP reader-reconstruction check is an in-loop signal, not a headline metric. The same-family generator/judge overlap (claude-opus-4.8 generates the Claude Code configuration and is also one of the two judges) is a genuine validity limitation, and the paper itself flags it ('aesthetic appeal is inherently subjective... the aesthetic numbers should be read as a proxy signal rather than proof'; 'a third-party human-rater study across all three artifacts is left for future work'). But this is evaluator-bias risk, not circularity: the result is not equal by construction to the generator's self-preference, since the cross-family GPT judge also rates the Claude Code poster above the author ground truth (3.51 vs. 2.93 in Table 5), and the Codex configuration is rated above the author reference by both judges as well. Self-citations to OmniPresent [12], ppt-master [22], and ResearchStudio-Idea [55] appear only in related work or as a public code dependency; ppt-master is an externally inspectable code artifact, and video/blog quality is not quantitatively claimed. Thus there is no reduction of a derived claim to its own inputs or to an unverified self-citation chain.

Axiom & Free-Parameter Ledger

5 free parameters · 4 axioms · 3 invented entities

The system is grounded in an external benchmark (Paper2Poster [1]) and its rubric, which keeps the contribution honest. The debts the reader pays are the hand-calibrated fill thresholds, the VLM-judge evaluation premise (same model families as the generators), author-reproduced baselines, and the private-proxy model serving described in Appendix C.

free parameters (5)
  • FULL fill band [0.90, 0.98] = 0.90–0.98
    Measured-fill loop target band for section fullness (fullRatio = content height / card height); the gate is deliberately loose at 0.90 to avoid oscillation. Hand-chosen; drives loop termination and hence poster density (§2.2.2).
  • EMPTY/SPARSE thresholds = 0.70 / 0.90
    Categorical verdict boundaries in the staged-fill loop that select which remediation move fires (EMPTY < 0.70, SPARSE 0.70–0.90). Hand-chosen engineering calibration (§2.2.2).
  • figure-fill gate = ≥ 0.90 on one axis
    Termination condition requiring each figure, in particular the Method figure, to paint at least 90% of its card on one axis (§2.2.2, §2.2.4).
  • circuit breaker round bound = ~12 rounds
    Bounds the fill-loop iteration count; on non-convergence the best-measured state ships, marked degraded (§2.2, Figure 3).
  • render-time whitespace expand target = toward 98%
    Post-loop expansion of under-filled cards toward 98% fullness by growing inter-row whitespace; the loop itself converges against the cheaper 90% gate (§2.2.2).
axioms (4)
  • domain assumption VLM judges (claude-opus-4.8, gpt-5.5) provide a valid proxy for poster quality
    The entire quantitative evaluation is scored by these judges; the paper itself says aesthetic appeal is subjective and judge scores are 'a proxy signal rather than proof' (§3 Analysis, Appendix A).
  • domain assumption The Paper2Poster benchmark, its six-criterion rubric, and PaperQuiz are reused faithfully and are fair for all systems
    Reused verbatim (§3, Appendix C). The benchmark is external [1], giving independent grounding, but the baseline systems were reproduced by the present authors, which is a fairness premise.
  • domain assumption The model identifiers name models as served by a private Copilot API proxy reliably
    Appendix C: 'the model identifiers used throughout the paper name the underlying models as served by that proxy'; assumes proxy serving behavior matches first-party endpoints.
  • domain assumption Agent skill runtimes (Claude Code / Codex) execute the multi-step skills reliably and idempotently
    The five-skill workflow relies on deterministic idempotent scripts (§2.1) plus LLM-driven steps; the release gates are designed to catch failures, but faithful execution is an implicit background premise.
invented entities (3)
  • Paper2Assets shared asset bundle independent evidence
    purpose: Single extraction of text, figures, captions, metadata, logos, QR codes, and narration clips consumed verbatim by all generators (§2.1).
    A shipped software artifact with a documented schema; its contents and checksum manifest are checkable from the released repo (claimed), so there is a falsifiable handle outside the paper.
  • alignment sidecar / timeline.json independent evidence
    purpose: Maps section ids to poster blocks, video time windows, captions, slides, and blog blocks for Paper2Reel (§2.3.2, §2.5.2).
    The load-bearing convergence object; the package gate and browser-level viewer tests provide an operational check, verifiable from the repo.
  • Reader-Reconstruction Preference (RRP) gate no independent evidence
    purpose: Optional edit-acceptance gate measuring whether a held-out reader model can still answer questions about the paper from the poster alone (§2.2.4).
    Described as 'currently an in-loop signal rather than a headline metric' (Appendix A); no numbers reported, so no independent falsifiable handle yet.

pith-pipeline@v1.3.0-alltime-deepseek · 25635 in / 19105 out tokens · 190723 ms · 2026-08-02T08:38:09.686297+00:00 · methodology

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read the original abstract

Despite growing automation, turning a paper into a coherent poster, talk video, and blog piece often remains a labor-intensive last mile. Recent systems increasingly generate multiple dissemination formats, but a practical workflow must also keep the outputs editable in native tools and bound into one navigable deliverable for revision and reuse. We present ResearchStudio-Reel, a native-editable dissemination workspace that binds its three artifacts into one interactive deliverable at the experience level, implemented as five skills executable in Claude Code and Codex: one shared extractor, three editable artifact generators, and one interactive convergence layer. A shared asset bundle feeds a PowerPoint poster and video deck, plus a bilingual Word blog; rather than re-rendering the paper into a fourth format, Paper2Reel converges these already-produced artifacts at the experience level, binding poster regions, video segments, and blog passages into one interactive viewer. Artifact-specific release checks make this delivery contract testable, and Paper2Poster additionally uses a measured-fill loop. On the Paper2Poster benchmark, our Claude Code configuration achieves the best scores among automated systems on all three aesthetic sub-criteria and the best or tied-best scores on two of three information sub-criteria. Under two VLMjudges, it exceeds the authors' posters in average aesthetics (3.56 vs. 3.03) and wins on overall quality on 74 and 95 of the 100 papers under the two judges. The full pipeline additionally packages the native-editable source artifacts and their aligned viewer. Project is available at https://aka.ms/ResearchStudio

Figures

Figures reproduced from arXiv: 2607.04438 by Hugo He, Jiahuan Zhang, Jin Jiang, Lingao Xiao, Longbo Huang, Qianli Ma, Qihao Zhao, Ruishuo Chen, Scarlett Li, Wenshan Wu, Xin Zhang, Yalun Dai, Yang He, Yang Ou, Yangyu Huang, Yan Lu, Yan Xia, Yap Kim Hui, Ying Xin, Zhipeng Zhang.

Figure 1
Figure 1. Figure 1: Three editor-ready artifacts from one paper. From a single accepted PDF, ResearchStudio-Reel produces a print-ready conference poster, a narration-aligned talk video, and a bilingual blog spread, shown here across example papers. Every deliverable stays editable in its native tool (PowerPoint for the poster and the video deck, Word for the blog), and the three are cross-linked into one navigable surface. A… view at source ↗
Figure 2
Figure 2. Figure 2: The ResearchStudio-Reel pipeline. One PDF in, three editor-ready artifacts out, with one shared extraction stage in the middle. A single Paper2Assets pass produces the bundle that Paper2Poster, Paper2Video, and Paper2Blog each consume verbatim, and Paper2Reel binds the three into one navigable surface. Sharing the same section identifiers, figure handles, and claim anchors keeps the artifacts mutually cros… view at source ↗
Figure 2
Figure 2. Figure 2: The ResearchStudio-Reel pipeline. One PDF in, three editor-ready artifacts out, with one shared extraction stage in the middle. A single Paper2Assets pass produces the bundle that Paper2Poster, Paper2Video, and Paper2Blog each consume verbatim, and Paper2Reel binds the three into one navigable surface. Sharing the same section identifiers, figure handles, and claim anchors keeps the artifacts mutually cros… view at source ↗
Figure 3
Figure 3. Figure 3: The Paper2Poster pipeline. A Paper2Assets bundle (paper spec, cleaned figures, logos, QR) drives an agent that picks the Method plus secondary figures and composes a self-contained poster.html along four axes: column layout, visual style, title-band header, and the Scan-to-Read block. A staged-fill loop then measures each section (slack + polish) and edits one section per round until every panel reads FULL… view at source ↗
Figure 3
Figure 3. Figure 3: The Paper2Poster pipeline. A Paper2Assets bundle (paper spec, cleaned figures, logos, QR) drives an agent that picks the Method plus secondary figures and composes a self-contained poster.html along four axes: column layout, visual style, title-band header, and the Scan-to-Read block. A staged-fill loop then measures each section (slack + polish) and edits one section per round until every panel reads FULL… view at source ↗
Figure 4
Figure 4. Figure 4: The staged-fill loop, visualized on the Latent Diffusion Models poster. A debug overlay boxes every section and colors it by fill verdict (red / amber for EMPTY / SPARSE, green for FULL, orange / magenta for SPILLAGE / OVERFLOW), annotated with its fill percentage. (a) Initial explorations: the freshly composed draft is uneven, with several underfilled sections and small figures. (b) Fill-loop in progress:… view at source ↗
Figure 4
Figure 4. Figure 4: The staged-fill loop, visualized on the Latent Diffusion Models poster. A debug overlay boxes every section and colors it by fill verdict (red / amber for EMPTY / SPARSE, green for FULL, orange / magenta for SPILLAGE / OVERFLOW), annotated with its fill percentage. (a) Initial explorations: the freshly composed draft is uneven, with several underfilled sections and small figures. (b) Fill-loop in progress:… view at source ↗
Figure 5
Figure 5. Figure 5: Paper2Video overview. The skill reuses the Paper2Assets bundle, plans narration and duration, delegates deck authoring to the full ppt-master workflow, synthesizes aligned audio and captions, renders visual attention cues, and packages the editable deck with captioned and no-subtitle videos. The root deliverables are video.pptx, video.mp4, and video_no_subtitles.mp4, while timelines, captions, audio clips,… view at source ↗
Figure 5
Figure 5. Figure 5: Paper2Video overview. The skill reuses the Paper2Assets bundle, plans narration and duration, delegates deck authoring to the full ppt-master workflow, synthesizes aligned audio and captions, renders visual attention cues, and packages the editable deck with captioned and no-subtitle videos. The root deliverables are video.pptx, video.mp4, and video_no_subtitles.mp4, while timelines, captions, audio clips,… view at source ↗
Figure 6
Figure 6. Figure 6: Paper2Video deliverable showcase. The figure pairs the editable video.pptx with the captioned video.mp4 and summarizes the user-facing controls checked by the package gate: target duration, highlight style, caption mode, and video specification. 9 [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 6
Figure 6. Figure 6: Paper2Video deliverable showcase. The figure pairs the editable video.pptx with the captioned video.mp4 and summarizes the user-facing controls checked by the package gate: target duration, highlight style, caption mode, and video specification. 8 [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Paper2Blog pipeline. The skill reuses the Paper2Assets bundle, builds one shared evidence map, selects a shared article figure set, writes two language-specific outlines and DOCX files, and runs a strict package gate. The root deliverables are the Chinese article and the English article. Outlines, previews, and QA reports stay in the skill’s auditable intermediates directory. 10 [PITH_FULL_IMAGE:figures/f… view at source ↗
Figure 7
Figure 7. Figure 7: Paper2Blog pipeline. The skill reuses the Paper2Assets bundle, builds one shared evidence map, selects a shared article figure set, writes two language-specific outlines and DOCX files, and runs a strict package gate. The root deliverables are the Chinese article and the English article. Outlines, previews, and QA reports stay in the skill’s auditable intermediates directory. 9 [PITH_FULL_IMAGE:figures/fu… view at source ↗
Figure 8
Figure 8. Figure 8: Paper2Blog DOCX showcase. The figure shows the two required Word deliverables, the English article and the Chinese article, together with the layout checks discussed in the text: typography balance, figure fit, caption placement, and pagination risk. 11 [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
Figure 8
Figure 8. Figure 8: Paper2Blog DOCX showcase. The figure shows the two required Word deliverables, the English article and the Chinese article, together with the layout checks discussed in the text: typography balance, figure fit, caption placement, and pagination risk. 10 [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Paper2Reel interaction showcase. (1) Hovering a poster section highlights the active block while neighboring sections fade. (2) Double-clicking the section opens the synchronized modal, with video playback, caption control, and slide thumbnail on the left, a language switch in the header, and the aligned blog passage on the right. 12 [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
Figure 9
Figure 9. Figure 9: Paper2Reel interaction showcase. (1) Hovering a poster section highlights the active block while neighboring sections fade. (2) Double-clicking the section opens the synchronized modal, with video playback, caption control, and slide thumbnail on the left, a language switch in the header, and the aligned blog passage on the right. 11 [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Qualitative ablation study. The author ground-truth (top left) is the human reference; the other five are all our method, holding the ResearchStudio-Reel skill, prompt, and pipeline fixed while varying only the harness and base model. Every variant converges to a full, legible single page; they differ in figure choice, phrasing density, and accent, not in structural soundness. All settings use high reason… view at source ↗
Figure 10
Figure 10. Figure 10: Qualitative comparison across configurations. The author ground-truth (top left) is the human reference; [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Poster comparison on one benchmark paper (“A Context-Integrated Transformer-Based Neural Network for Auction Design”). Every system renders from the same source PDF: our poster and the human-made (authors’) poster (left, largest), the Paper2Poster Tool [1] and PosterGen [4] baselines (center), the portrait P2P [3] baseline (right), and three single-shot LLM baselines (Claude-4.8 Opus, GPT-5.5, Gemini-3.1 … view at source ↗
Figure 11
Figure 11. Figure 11: Poster comparison on one benchmark paper (“A Context-Integrated Transformer-Based Neural Network for Auction Design”). Every system renders from the same source PDF: our poster and the human-made (authors’) poster (left, largest), the Paper2Poster Tool [1] and PosterGen [2] baselines (center), the portrait P2P [3] baseline (right), and three single-shot LLM baselines (Claude-4.8 Opus, GPT-5.5, Gemini-3.1 … view at source ↗

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