AutoFigure: Generating and Refining Publication-Ready Scientific Illustrations
Authors: Minjun Zhu
Zhen Lin
Yixuan Weng
Panzhong Lu
Qiujie Xie
Yifan Wei
Sifan Liu
QiYao Sun
Yue Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Automated Scientific Illustration
Agentic AI
Text-to-Image
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: High-quality scientific illustrations are crucial for effectively communicating complex scientific and technical concepts, yet their manual creation remains a well-recognized bottleneck in both academia and industry. We present FigureBench, the first large-scale benchmark for generating scientific illustrations from long-form scientific texts. It contains 3,300 high-quality scientific text–figure pairs, covering diverse text-to-illustration tasks from scientific papers, surveys, blogs, and textbooks. Moreover, we propose AutoFigure, an agentic framework that automatically generates high-quality scientific illustrations based on long-form scientific text. Specifically, before rendering the final result, AutoFigure engages in extensive thinking, recombination, and validation to produce a layout that is both structurally sound and aesthetically refined, outputting a scientific illustration that achieves both structural completeness and aesthetic appeal. Leveraging the high-quality data from FigureBench, we conduct extensive experiments to test the performance of AutoFigure against various baseline methods. The results demonstrate that Autofigure consistently surpasses all baseline methods, producing publication-ready scientific illustrations.

Published as a conference paper at ICLR 2026
AUTOFIGURE: GENERATING AND REFINING PUBLICATION-READD SCIENTIFIC ILLUSTRATIONS
Minjun Zhu-24 Zhen Lin 2 Yixuan Weng"*
Panzhong Lu", 2 Qiujie Xie", Yifan Wei", Sifan Liu", Qiyao Sun', Yue Zhang
"Zhejiang University School of Engineering, Westlake University
wengsyx&gmai 1 com, rzhuminjun, zhangyue] @west ake.edu.cm
Projecttittss/deesccctttsc.. Q AutoFigure
n AutoFigure-Edii FigureBench
ABSTRACT
High-qualiyy scientific illustrations are crucial for effectively communicating complex scientific and technical concepts, yet their manual creation remains a well-recognized bottleneck in both academia and industry. We present Fig- ureBench, the first large-scale benchmark for generating scientific illustra- tions from long-form scientific texts. It contains 3,300 high-quality scientific text-figure pairs, covering diverse text-to-illustration tasks from scientific pa- pers, surveys, blogs, and textbooks. Moreover, we propose AUTOFIGUM FIGURE, the first agentic framework that automatically generates high-quality scientific illus- trations based long-form scientific text. Specifically, before rendering the fi- nal result, AUTOFIGURE engages in extensive thinking, recombination, and val- idation produce a layout that is both structurally sound and aesthetically re- fined, outputting a scientific illustration that achieves both structural complete- ness and aesthetic appeal. Leveraging the high-quality data from FigureBench, we conduct extensive experiments to test the performance of AUTOFIGURE against various baseline methods. The results demonstrate that AUTOFIGURE consis- tently surpasses all baseline methods, producing publication-ready scientific il- lustrations. The code, dataset and huggingface space are released in https: //github. con WeweeeerrrAuueeeeeeeee Fuure..
INTRODUCTION
Scientific illustration is crucial medium for science communication, serving complement scientific texts (Fytas et al., 2021; Kim et al., 2022). It allows readers to quickly grasp the main ideas within minutes and helps prevent misinterpretation (Chang al., 2025). However, creating effective scientific illustrations is challenging. It requires a deep logical understanding of long- form scientific texts, along with the distillation of critical information. Additionally, the visual presentation must balance structural fidelity and image quality, ultimately transforming the text into clear, accurate, and aesthetically pleasing illustrations. As a result, producing a high-qualit, illustration usually takes human researchers several days, requiring creators to possess both domain knowledge and professional design skills.
Research into the automatic generation of scientific illustrations from long-form scientific texts could greatly enhance the efficiency and accessibility of science communication. However, this area remains largely unexplored. While previous datasets like Paper2Fig 100k (Rodriguez et al., 2023b), ACL-Fig (Karishma et al., 2023), and SciCap+ (Yang et al., 2024a) have advanced the field, they primarily focus reconstructing figures from captions, short snippets, or existing metadata. In con- trast, our work targets Long-context Scientifie Illustration Design, a task that requires distilling an entire methodology from long document (avg. >10k tokens) and autonomously planning the visual
1 The contributions are equal.
Corresponding Author.
AutoFigure is the complete implementation presented in this paper. AutoFigure-Fade Edit is new version of AutoFigure that supports fully editable icons and

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Published as a conference paper at ICLR 2026
structure, rather than simply translating explicit drawing instructions. Parallel to these limitations in benchmarks, existing automated systems also face challenges in generative capability. Although progress has been made in the field of automated generation of visual scientific communication (e.g., PosterAgent (Pang et al., 2025) and PPTAgent (Zheng et al., 2025)), these methods primarily focus on understanding, extracting, and combining existing multimodal content from papers, rather than understanding the original text and generating corresponding visual content. Another line of work employs executable code as an intermediate state between scientific text and illustration. (Belouadi et al., 2023; 2024; 2025; Ellis et al., 2018). These approaches primarily optimize for structural and geometric correctness. However, as demonstrated by our quantitative evaluations in they often face challenges in balancing these rigid constraints with the aesthetic fluency and readability required for publication standards, resulting lower scores compared to AUTOFIGURE visual design metrics. Meanwhile, mainstream end-to-end text-to-image (T2I) models often fail to effectively visualize long scientific texts. Although they generate aesthetically pleasing images, they struggle to pre- serve structural fidelity (Liu et al., 2025). In Figure 6, we compare the generation results of the aforementioned methods when faced with long-form scientific texts. Taken together, these limita- tions underscore the challenges of directly transforming long scientific texts into illustrations that are both accurate and visually appealing.
To address these challenges, we introduce AUTOFIGURE, agentic framework based the Rea- soned Rendering paradigm. This paradigm breaks down the scientific illustration generation pro- cess into two distinct stages: (I) Semantic Parsing and Layout Planning, converting the unstruc- tured long-form scientific text into a structured, machine-readable conditioning image with an as- sociated style description. (2) Aesthetic Rendering and Text Refinement, which transforms the structurally optimized symbolic blueprint into a high-fidelit illustration, while addressing the com- mon problem of blurry text rendering through an "erase-and-cod -nd-ccreeet" strategy. We further propose a large-scale benchmark named FigureBench (Figure 1) to comprehensively evaluate the quality of the Al Al-generated scientific illustrations. It consists of 3,300 high-quality long-form scientist vext-figure pairs, with 300 reserved as the test set and the remaining as the development set. For the critical test set, we randomly sample 400 papers from Research- 14K (Weng et al., 2025) and extract the most relevant conceptual illustrations using GPT-5 After sixteen days of human annotation, 200 qugh-qualtty pairs are retained with a high Inter-Rater Reliability (IRR, Cohen's 's K = 0.91)' To further enhance the diversity, an additional 100 samples are curated from scientific surveys, blogs, and textbooks, yielding 300 test instances in total. Leveraging these expert-labeled data, we further finetune an automated filter to construct large-scale development set comprising 3,000 illustration pairs.
Finally, based on FigureBench, we design evaluation protocol grounded in the VLM-as-a-judge paradigm. combines referenced scoring and blind pairwise comparison to assess Al Al-generated scientific illustrations across multiple dimensions (e.g., aesthetic quality, accuracy). Through exten- sive quantitative and qualitative evaluations, including automated evaluations ($5.1), human evalu- ation ($5.2), and controlled ablation studies ($5.3), we demonstrate that AUTOFIGURE effectively resolves the trade-off between aesthetic fluency and structural fidelity. The generated scientific illus- trations not only maintain high accuracy in structure and text but also achieve publication quality in layout and visual appeal. Qualitative examples are presented in Figures 6, 3 and Appendix Section E, showcasing the versatility of AUTOFIGURE in generating complex scientific illustrations (e.g.. procedural flows, algorithmic pipelines) from diverse range of academic texts.
In this paper, we construct FigureBench, the first large-scale benchmark specifically targeting Long- context Scientific Illustration Design, and propose a novel framework named AUTOFIGURE. With this framework, we achieve the fully automated generation of high-quality scientific illustra- tions. The effectiveness of AUTOFIGURE is also strongly validated by human expert evaluations, with up to 66.7% % of generated results judged to meet publication standards (Figure 4). We hope this work can provide researchers with powerful automation tool, lay solid foundation for the devel- opment of automatic scientific illustration models, and endow future "AI scientists" with excellent visual expression capabilities.
'Cohen's K assesses annotator agreement heyond random chance on laheling tasks.
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Table 1: Comprehensive Analysis of the FigureBench.
Category
Number (Total)
Text Tokens
Text Density (%, Avg.)
Components (Avg.)
Colors (Avg.)
Shapes (Avg.)
(Avg.)
Paper
3200
12732
42.1
5.4
6,4
6.7
Blog
20
4047
46.0
4.2
5.5
5.3
Survey
40
2179
43.8
6.7
Textbook
40
352
25.0
4.5
4.2
3.4
Total/Average
3300
10300
41.2
5.3
2 RELATED WORK
Automated Scientific Visuals Generation. Existing work on automated scientific visuals primarily explores the generation of artifacts like posters and slides. Modern agentic systems such as PosterA- gent (Pang et al., 2025) and PPTAgent (Zheng et al., 2025) have advanced significantly beyond early summarization techniques (Qiang et al., 2019; Xu & Wan, 2022: Hu & Wan, 2014; Sravanthi al., 2009). However, these systems are fundamentally designed to rearrange and summarize existing fig- ures and textual content from source document. Moreover, existing Achematic-focussee works such as SridBench (Chang et al., 2025) and FigGen (Rodriguez et al., 2023a) are often limited by their reliance on sparse inputs, such as captions, which lack sufficient structural information. Our work, instead, addresses the task of generating scientific illustrations from scratch based on a long-form scientific context, critical step towards producing a complete and original scientific artifact.
Text-to- Im-Ima Generation. Recent progress in diffusion models (Song et al., 2021) have greatly improved the performance T2I generation (Saharia et al., 2022; Ramesh et al., 2022). While text- based conditioning provides flexibility and fereffriendlieess, current models face particular chal- lenges when dealing with scientific long-form texts, which often contain specialized terminology, complex structures, and intricate relationships between concepts. These texts not only span multiple sentences and hundreds of tokens but also require a deep understanding of domain-specific knowl- edge (Zheng al., 2024). Effectively encoding such lengthy and detailed conditions, while ensuring precise alignment between the scientific text and generated images, remains a critical gap for gener- ative models (Liu et al., 2025; Chen al., 2024). To address this gap, propose FigureBench for systematic evaluation and AUTOFIGURE for advancing automated scientific illustration.
Automated Scientific Discovery. The rise of AI Scientists (Lu et al., 2024; Yamada et al., 2025; Intology, 2025; Zhu et al., 2025b; Xie et al., 2025c), powered by Large Language Models (LLMs), revolutionizing scientific discovery by autonomously managing the entire research workflow (Xie et al., 2025b; Starace et al.; Weng et al., 2026; Chan et al.; Wang et al., 2024a). This shift is sub- stantiated by the growing acceptance manuscripts generated by the Al Scientist-v2 (Yamada et al., 2025) exceeded human acceptance thresholds at ICLR 2025 workshops, and Zochi (Intology, 2025) successfully authored papers ac- cepted into the main proceedings of ACL 2025. This is further complemented by significant progress in producing textual artifacts such as reviews and surveys (Zhu et al., 2025a; Wang et al., 2024b). These developments signal that human-level Al capable of uncovering novel phenomena may be imminent. However, this progress has exposed critical bottleneck, as the inability to generate illus- trations prevents AI Scientists from visually articulating their own findings. Automating this capa- bility is the essential next step, empowering these systems to translate complex, machine-generate discoveries into an intuitive visual language that is fully comprehensible human researchers.
Al-generated papers at prestigious venues. For instance,
FIGUREBENCH: A BENCHMARK FOR AUTOMATED SCIENTIFIC ILLUSTRATION GENERATION
Automatic scientific illustration aims to constructs a mapping function that takes long-form sci- entific text T input and generates publication-qullity illustration Iyimal- In this section, introduce FigureBench, the first large-scale benchmark for generating scientific illustrations from formformmo scientific texts. As depicted in Figure FigureBench is curated encompass wide ray of document types, including research papers, surveys, technical blogs, and textbooks, establish- ing challenging and diverse testbed to spur research in automatic scientific illustration generation.
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Paper
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The composition of the FigureBench dataset. features a rich collection of text-figure pairs from four distinct sources (Paper, Survey, Blog, and TextBook), demonstrating the benchmark's 's capability to evaluate automatic illustration generation across various domains and complexities.
Figure 1:
Data Curation. To curate a high-quality test set for this task, we began by randomly sampling 400 scientific papers from the Research- 14K dataset (Weng al., 2025). For each paper, we used GPT- 5 to select the single illustration that best represented its core methodology. This resulted in 400 initial paper-figure pairs. We then filtered these pairs, retaining only conceptual illustrations (i.e., excluding data-driven charts) where each key visual element was explicitly described in the source text. To ensure high quality and consistency, each remaining pair was evaluated by two independent annotators. Only pairs that were approved by both annotators were included in the final dataset. This rigorous annotation process yielded a high Inter-Rater Reliability (IRR) of 0.91, resulting in a final test set of 200 high-quality samples.
To further enhance the diversity of our test data, we manually curated an additional 100 samples from three distinct sources: surveys, technical blogs, and texthooks. For the survey category, we collected structural diagrams (e.g., roadmaps and taxonomies) from recent Al surveys published on arXiv. Textbook samples were sourced from open-licensed educational platforms like OpenStax for their pedagogical clarity, while blog samples were hand-collected from technical outlets such as the ICLR Blog Track to capture modern and accessible visual styles. The entire curation pro- cess strictly adhered to open-source licenses, with a detailed breakdown provided in Appendix A. Finally, we leveraged our high-qualit, curated set of 300 samples (200 from papers and 100 from diverse sources) to fine-tune a vision-language model. This model then served as an automated filter, which we applied the larger Research-145 corpus (Weng et al., 2025), resulting in a large-scale development set containing 3,000 scientific illustration samples.
We explicitly distinguish the roles of these datasets: the Test Set is strictly reserved for evaluation, whereas the Development Set is designed for training, development and experimental purposes. Although AUTOFIGURE operates as an inference-only pipeline and does not utilize the Development Set for training, we provide this resource to facilitate future exploration of end-to-end or trainable methods by the community.
Dataset Analysis. To quantify the characteristics of FigureBench, we conduct detailed statistical analysis, presented in Table 1. The analysis confirms the task's significant challenges. For instance, the average Text Tokens metric varies by over an order of magnitude between Textbooks (352) and Papers (12,732), highlighting the need for robust long-context reasoning. Additionally, the high average Text Density (41.2%), which indicates the proportion of the image area occupied by text, and the varied number of Colors (averaging 6.2), both statistically analyzed using the InternVL. 3.5 model (Wang al., 2025), underscore the challenge of balancing informational richness with visual clarity. Moreover, the mean number of Components (5.3) and Shapes (6.4) demonstrates the structural complexity. The collected paper data is also temporally recent from 2025.
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AutoFigure Architecture
Stage III: Rendering Strategy
Decoupled Generative Paradigh: Reasoning, Refinement, and Layered Rendering
Stage I: Concept Extraction
Stage II: ritique-and-Refinn
Synthesis
Critique: Aligement, Overiap, Balance
Ertttins
Feedback
Raw Image
Petationn
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Text Blurl
Input Next
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Score Comparison Qutendd qtheaat
Initial Blueprint (50, AO)
ABC
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"Map unstructured test to symbolic graph"
Update: Re interpret method
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Aslansns, Ant -ecleoo uscrnlle
Mothodology:
Concept
Bendering
Figure 2: An Overview of the AUTOFIGURE, which decouples structural layout generation from aesthetic rendering. Stage ensures structural fidelity by having a multi-agent system generate and iteratively self-correct a symbolic layout (SVG), Stage 2 renders the validated layout and employs an crase-and-corres module- using OCR and cross-verificas verisiveriiinttion to guarantee perfect textual accuracy with high-fidelity vector overlays. This figure is also produced by AUTOFIGURE and serves qualitative showcase of its generation quality.
Evaluation Metries. The evaluation of scientific illustrations is non-trivial, as traditional T2I met- rics (e.g.. FID (Jayasumana et al., 2024)) usually misaligned with the requirements for logi- cal and topological correctness. Therefore, our evaluation protocol leverages the VL-Maas-a-jude paradigm for structural reasoning and long-context comprehension, consisting of two complemen- tary methods: (1) Referenced scoring, where VLM is provided with the full text, the ground-trute figure, and the generated image. It assesses the generated image across three dimensions with eight sub-metrics: Visual Design (aesthetic quality, visual expressiveness, professional polish), Com- munication Effectiveness (clarity, logical flow), and Content Fidelity (accuracy, completeness, appropriateness). The VLM outputs score and textual reasoning for each sub-dimension, with the Overall score calculated as their average. (2) blind pairwise comparison. ting, the VLM receives the full text and two images (ground-truth and generated) in randomized order, without knowledge which is the original. on seven criteria including aesthetic quality, clarity. information sophistication, accuracy, complete- ness, appropriateness, and provide a final choice for the better figure. We note that VL.M-as-a-jude paradigm not fully replace human expertise (Lee et al., 2024; Xie al., 2025a). To this end, we further conduct an human evaluation, for which we recruit ten first-authors to assess generated figures for their own work ($5.2), providing gold-standard measure of real- world utility.
this evaluation set-
asked to select a winner (A, B, or Tie) based
4 AUTOFIGURE
We introduce AUTOFIGURE, a decoupled generative paradigm for high-fidelity scientific illus- tration generation. Our approach tackles the challenge of producing semantically accurate and visually coherent figures by separating the reasoning and rendering processes. Our core innova- tion lies in a three-stage pipeline. First, we employ a large language model (LLM) for conceptual grounding, distilling unstructured text into structured, symbolic blueprint. Second, a novel self- refinement loop simulating a dialogue between an Al designer and critic- iitiiiiviyyiielly optimizes this blueprint for structural coherence and logical consistency. Finally, a dedicated rendering stage, featuring a unique erase-and-cod -nd-ccreet strategy to ensure textual legibility. The following sections detail each phase of this pipeline.
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4.1 STAGE I: CONCEPTUAL GROUNDING AND LAYOUT GENERATION
Given a long-form scientific document T, Stage I produces (i) machine-readable symbolic layout So (e.g., SVG/HTML) that specifies the 2D geometry and topology of the schematic, and (ii) style descriptor Ag- We additionally rasterize So into a layout reference image to that will be used to condition the renderer in Stage II.
Concept Extraction and Symbolic Construction. Given the input text T, the agent outputs (a) distilled methodology summary meethod and (b) set of entities and relations that will be visualized as nodes and directed edges. We serialize this structure into markup- based symbolic layout S (SVG/HTML) and a category-conditioned style description Ag. where C' E (PAPER, SURVEY, BLOG, TEXTBOOK) and So encodes a directed graph Go (Vo, Eo). All stage prompts and the exact imput-outpn schema are provided in Appendix M for reproducibility.
Extraction
Commiled: la Neard Radlanca Flidd
Figure 3: Examples showcasing the versatility of AUTOFIGURE in generating complex scientific illustrations from diverse range of academic texts. Note that we employ unified default style (Delicate and cute cartoon comic style, using Morandi color palette) solely to ensure visual consistency and readability for comparative analysis. This is a choice of presentation rather than a limitation of the method; users can freely specifyy or mix arbitrary styles as needed (see in Appendix L). We present a diverse range of results in Appendix Q to further illustrate our approach.
This step is the core of our "thinking" process, implementing a self- refinement loop that simulates dialogue between an AI "designer" and an AI "critic", aiming to find the globally optimal layout through iterative search. First, the initial layout (Sp, Ag) is eval- uated get an initial score qn. which is set as the current best version: (Shests Abest) (Sp. Ao)
Critique-and-
and Qbest - go- Subsequently, in each iteration i, the system attempts to generate superior solution:
Peat Feedback E crétiic (Srests Abest))
(s(t) cands (() + gegen(( (T (Tmethods (1) Frest)
(2)
where the critic & Peritic evaluates the best-perforning layout (Sbests Abest) for alignment, balance, and overlap avoidance, producing textual feedback (i(1) The generator T gen then then uses this (4)
feedback to reinterprets Tmethod and produce a candidate layout (s(t) A (1) ( with score Toand- (4) If Toand Qbeats it replaces the current best. The loop continues until a preset limit of N itera- tions or until the score converges, yielding the final layout (S (Srinats44 Afintt.. a logically consistent, structurally coherent, and aesthetically balanced conditioning layout with style description.
cands

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Published as a conference paper at ICLR 2026
Table 2: A comprehensive user evaluation across four generation tasks, with updated methods and scoring. Win-Rate calculated through blind pairwise comparisons against the reference, indicating the percentage of times a method is selected as producing more suitable illustrations for the descriptive text.
Visual Design
Communication
Content Fidelity
Method
Aesibetic
Express.
Polish
Clarity
Flow
Accuracy
Complete.
Appropriate.
Overall
Win-Rate
HTMI-Code
5.61
4.50
5.79
7.42
7.53
7.26
6.34
6.76
6.40
30.0%
SVG-Code
4.39
3.61
4.09
5.68
5.71
5.98
5.05
5.17
4.96
45.0%
GPT-Image
3.60
5.83
3.92
10.0%
Diagram Agent
1.95
1.47
1.61
2.16
2.05
2.34
1.76
2.00
1.92
0.0%
7.25
7.44
8.04
7.32
6.65
75.0%
SURVEY
Gemini-HTML.
4.77
3.59
4.88
6.99
6.52
8.04
7.04
5.55
5.92
37.5%
Gemini-SVG
4.28
3.16
4.25
6.51
6.06
7.25
6.16
5.04
5.34
44.1%
GPT-Image
2.85
6.28
4.59
Diagram Agent
2.11
1.55
1.77
2.69
2.67
2.86
2,06
2.07
2.22
0.0%
AUTOFIGURE
6.75
TEXTBOOK
72.5%
Gemini-SVG
4.90
3.99
4.81
6.91
6.74
8.03
7.33
6.28
6.12
76.9%
GPT-Inage
Diagram Agent
2.03
1.51
1.63
2.51
2.20
3.24
2.73
2.17
2.25
0.0%
AUTOFIGURE
8.13
PAPER
HTML
6.99
6.37
11.0%
SVG-Code
5.00
4.19
4.89
6.34
6.48
6.15
5.53
5.37
5.49
31.0%
GPT Image
Diagram Agent
2.25
1.73
2.04
2.67
2.49
2.11
1.72
1.94
2.12
0.0%
AUTOFIGURE
6.92
7.34
6.66
6.51
7.03
53.0%
4.2 STAGE II: AESTHETIC SYNTHESIS AND TEXT POST-PROCESSING
The final stage translates the structurally optimized symbolic blueprint (Stinats A finoa) into a high- fidelity illustration I final.
Style-Guided Aesthetic Rendering. We use a transformation function & prompt (LLM-based) to
convert the (Syiaat: A final) into an exhaustive text-to-image prompt, paired with structural graph derived from S final (which precisely dictates the position and interconnection of all elements). These inputs are fed into multimodal generative model to render an image patished that is faithful the layout structure and perfectly embodies the optimized aesthetic style.
Ensuring Textual Accuracy. We improve text legibility via an erase-and and-correct process. First, non-LLM eraser O Perase removes all text pixels from [polished to produce a clean background Lernedd Peess (poliishaa)- Second, a OCR engine &oer extracts preliminary strings and bounding boxes (Toers Cost) Pocc(Hpotssee). Third, a multimodal verifier & Verliy aligns each OCR string with the ground -ruth labels T. parsed from Simal and outputs corrected text map Teom Dverity Tg) Finally, render Tear vector-text overlays at Corr top of Lerased to obtain Iinal-
5 EXPERIMENTS
To comprehensively evaluate AUTOFIGURE, we conduct (i) automated evaluations on Fig- ureBench ($ 5.1), (ii) domain-expert study with paper-authors ($ 5.2), and (iii) controlled abla- tions isolating key modules ($ 5.3). Figure provides representative qualitative results; beyond these in-text examples, the appendix contains substantially extended evidence, including: detailed qualitative case studies across diverse papers (Appendix $ E); additional evaluations under open- source model deployments (Appendix G); an extended blind pairwise comparison with absolute quality judgments (Appendix H): module-level analyses of the eextrrefinement/pos wowttppooo----pinnggggg com- ponent (Appendix I); efficiency and cost analysis under different deployment settings (Appendix J); a human- audited sanity check of automated dataset statistics (Appendix K); further results on style controllability and diversity (Appendix L); and further results extended Aaselines(Appendie N).

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Overall Analysis
Publication Distribution
Refuraace
100
Figure
100
63.39
Selection Rate (96)
Auto Pigurr
Winn Rate (%)
66.7%
80
80
0%
4,7%
4.8%
4.8%
(c) Accuracy Performance Score (d) Clarity Performance Score
(e) Aesthetics Performance Score
Auto
Auto Figure
Auto
4:24
Soore (2-0)
4,99
Score (1-5)
Soor
2:45
2:92
2.52
2477
2.40
243
1.11
1,0
3,29
AutoFigure GPT-Image
SVG Code
PPTX Code
Reference
HTML Code
Diagram Agent
None
Figure 4: Human evaluation results from 10 first-author experts assessing AL Al-generated figures for 21 of their own publications. The comprehensive study required experts to perform three tasks: (a) forced-choice holistic ranking of models against the original reference determine a rate, (b) publication intent selection, and (c-c) multi-dimensional scoring on a Likert scale for accuracy, clarity, and aesthetics.
5.1 AUTOMATED EVALUATIONS
Experimental Setup. We assess AUTOFIGURE against three distinct types of baseline methods: (1)
End-to-end text-to-image methods (Sun et al., 2024), where we used the GPT-Image model (Hurst al., 2024) directly generate a scientific schematic from the paper's text based on instructions; (2) Text-to-code methods, where we use a LLM to generate corresponding HTML Code and SVG Code (Rodriguez et al., 2025; Malashenko al., 2025; Yang al., 2024b), which are then automatically rendered into images; and (3) Multi-agent frameworks, represented by Diagram Agent (Wei et al., 2025), which automates workflow design. For AUTOFIGURE and other decoupled baselines, we use Gemini-2.5-Pro as the sketch model and GPT-Image as the rendering model.
detailed Table 2, AUTOFIGURE achieves the highest Overall score across all four document categories: Blog (7.60), Survey (6.99), Textbook (8.00), and Paper (7.03). Notably, AUTOFIGURE also dominates in Win-Rate evaluations through blind pairwise comparisons, achieving 75.0% for Blog, 78.1% for Survey, exceptional 97.5% for Textbook, and 53.0% for Paper. The results demonstrate that AUTOFIGURE consistently surpasses all baseline methods in both automated evaluations and human preferences. showcasing superior balance of visual quality, commu- nicative effectiveness, and content fidelity, Moreover, AUTOFIGURE achieves the best performance in most sub-metrics under Visual Design Excellence and Communication Effectiveness, indicating its ability to produce schematics that are both attractive and easy to understand. The Win-Rate results particularly highlight the inherent limitations of existing paradigms: code-generation meth- ods (Gemini- (Gemini-HTML/SVV HTML/SVG) achieve moderate Win-Rates (30-77%) but sacrifice visual aesthetics for structural control, while the end-to-end model GPT-Image shows consistently low Win-Rates (7- 55%) due to poor content accuracy. For instance, in the Paper category, the Aesthetic scores of text-to-code methods (5.90 and 5.00) are significantly lower than AUTOFIGURE'S (7.28), limiting their Win-Rates to 11.0% and 31.0% respectively. Conversely, GPT-Image exhibits critical weak- ness in content accuracy, scoring the lowest among generative models in this metric for the Paper category (4.77). resulting in only 7.0% Win-Rate. The multi-agent framework, Diagram Agent, consistently achieves 0% Rate across all categories while performing poorly in all dimensions, underscoring the profound difficulty of this task without specialized, structured approach.
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(a) Model Performance w/o Rendering
(b) Model Performance with Rendering
Perform Score
104
204
2.72
2.20
GAT
29
C.M
Camm.
Content Fidelity
Qludde4411000 1-Opus
Gemin--..ae Safro
Grob-4
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(c) Performance with Test-Time Scaling
(d) Format Performance Comparison
Perfomance Score
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6.22
0.37
Visual Design
Domm. Effect.
Conkent
Overall
Test-Time Scaling Iterations
Rddlity
Visua Des on
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Figure 5: Ablation studies of the AUTOFIGURE framework. Subplots compare different backbone models on (a) pre-rendering symbolic layouts versus (b) final rendered outputs. Also shown are (c) performance scaling with increased test-time refinement iterations (d) the impact different intermediate sketch formats.
5.2 HUMAN EVALUATION WITH DOMAIN EXPERTS
Experimental Setup. To evaluate whether the figures generated by AUTOFIGURE meet the publication-ready standards of the relevant domain experts, we recruited 10 human experts assess Al-generated figures based on their own first-author publications. The evaluation involved three tasks across 21 high-qualie papers: (1) Multi-dimeensioal scoring: Each figure rated 1-5 (2) Forced-choice ranking: Experts ranked all AI- generated figures against the original human-authored references. (3) Publication intent selection:
Likert scale for Accuracy, Clarity, and Aesthetics.
Experts indicated which figures they would choose to include in camera-ready paper.
The results are shown Figure 4, showing that AUTOFIGURE's quality is judged far superior to other Al systems and closely approaches the human-created originals by resolving the key trade-off between accuracy and aesthetics. The win-rate analysis in Figure 4(a) shows AUTOFIG- URE achieves an 83. 83.3% win rate against oth er models. second only to the original buman-authore reference (96.8%) Notably, as shown in Figure 4(b), 66.7% of experts are willing to adopt figures generated by AUTOFIGURT for camera-ready version of their own papers, indicating that it can produce figures that meet the standards of real-world academic publishing. achieves better aesthetics at the cost of low accuracy, while SVG Code has slightly better accuracy but poor aesthetics.
contrast, the perfor-
mance of baseline methods is highly polarized.
5.3 ABLATION STUDIES
Analysis on pre-rendering symbolic layouts. By comparing the pre-rendering scores in Figure 5(a) with the post-rendering scores in Figure 5(b), we observe a consistent and significant improvement in Visual Design and Overall scores for all backbone models, For instance, with GPT-5 as the reasoning core, the Overall score jumps from 6.38 to 7.480 after rendering. This proves the effectiveness of final drawing phase. The decoupled rendering stage effectively enhances visual appeal without compromising the schematic's structural integrity and content fidelity.
Analysis on refinement loop. To investigate the impact of the critique-and-refine loop, we conduct test-time scaling experiment that fixes the backbone models and varies the number of "thinking" iterations from to 5. As shown in Figure 5(c), the overall performance score steadily rises from an initial 6.28 (zero iterations) to 7.14 after five iterations. This improvement demonstrates that the refinement loop is an effective optimization process.
Analysis reasoning models and intermediate formats. The figure quality is also heavily in- fluenced by the choices of the reasoning model and the intermediate data format. Figures 5(a) and 5(b) show that stronger reasoning models like Claude-4.1 1-Opus produce superior layouts compared

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Collect Stept Trals SFT Policy
Step3 Optintia Fallyy
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(b). Diagram Agent
Fine-tune
Final Aligned Policy (InstructGPT)
Policy
Reward Model (RM)
(c). GPT-Image
(d). SVG
(e). HTML
(f). AutoFigure
Figure 6: Qualitative comparison for generating scientific illustration of the InstructGPT framework (Ouyang et al., 2022). We compare the original human design (a) with illustrations generated by various baseline meth- ods (b-e) and our AUTOFIGURE (f). Notably, AUTOFIGURE achieves both high scientific fidelity and aesthctic appeal, yielding publication-ready result.
to others. Furthermore, Figure 5(d) highlights the critical role of the intermediate representation, as expressive and structured formats like SVG (8.98) and HTML (8.85) can generate the entire fig- ure in one coherent file, while PPT's (6.12) requirement for multiple incremental code insertions introduces inconsistencies that cause the final output to diverge from the original paper's content.
Case study. This case highlights AUTOFIGURE'S advantage in jointly preserving semantic struc- ture and visual readability for multi-stage procedural diagrams. Compared with the original figure 6 (a), DIAGRAM AGENT in Figure 6 (b) collapses the process into an overly thin, low-information chain, losing the essential stage separation and the data artifacts (demonstrations, comparisons, re- ward scores) that define the RLHF workflow. The end-to-end generator GPT-IMAGE in Figure 6 (c) captures only a coarse flow and exhibits inconsistent typography and crowded labeling, which undermines legibility and instructional value. Code-only baselines Figure 6 (d-e) better maintain box-and-arrow skeleton, but the outputs remain visually sterile and fragmented (e.g., weak hierarchy, poor spacing, and limited affordances to emphasize key roles such as labelers and reward model- ing). In contrast, AUTOFIGURE in Figure 6 (f) explicitly decomposes the content into three aligned stages (SFT, RM, PPO), renders consistent typographic hierarchy and spacing, and uses semanti- cally grounded icons and callouts to make roles and data transformations immediately interpretable, yielding polished infographic without sacrificing the core scientific fidelity.
6 CONCLUSION
Generating high-quality scientific illustrations from long-form scientific texts poses new challenges for existing automated scientific visuals generation technologies. To advance this field, this paper introduced FigureBench, a comprehensive benchmark consisting of 3,300 high-quality long-form scientist fixt-fiuure pairs, covering diverse types of scientific texts. Building upon FigureBench, we further proposed AUTOFIGURE, an agentic framework based on the Reasoned Rendering paradigm, which generates accurate and visually appealing illustrations in an iterative process. Through au- tomatic evaluations grounded in the VLM-as-a-judde paradigm and human expert assessments, we demonstrated that AUTOFIGURE's 's ability to generate scientifically rigorous and aesthetically ap- pealing illustrations that meet the standards of academic publishing. By automating critical bot- tleneck in scientific communication, our work lays the groundwork for Al-driven scientific visual expression, enabling more efficient and accessible creation of publication-ready illustrations.
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ACKNOWLEDGEMENT
This publication has been supported by the National Natural Science Foundation of China (NSFC) Key Project under Grant Number 62336006 and 625B2152, and the 2025 Dean's Special "PhD Student Project" of the School of Engineering, Westlake University.
We sincerely thank Yiran Ding, Zhiyuan Ning, Fang Guo, Guangsheng Bao, Hongbo Zhang, and Shulin Huang for their contributions to this paper.
ETHICS STATEMENT
We acknowledge the significant ethical considerations associated with powerful generative tech- nologies like AUTOFIGURE. The primary risk involves the potential for misuse, where the system could be used to generate scientifically plausible but factually incorrect or misleading schematics to support false claims. To mitigate this risk, sible deployment. Our mitigation strategy is twofold. First, we explicitly declare that AUTOFIGURE an assistive tool with limitations. This disclaimer, stating that the system is not substitute for expert verification and may not produce perfectly reliable outputs, will be placed prominently within this paper and in the README file of the public code repository. Second, the open-source license governing AUTOFIGURE will include a mandatory attribution clause. This clause requires any aca- demic publication using figure generated by our tool to (a) include a specific section that discusses the role AI played in the work, and (b) explicitly caption the figure as having been generated by AUTOFIGURE. These requirements are designed to ensure transparency and accountability in the downstream use of our technology, fostering research environment where Al tools are used to augment, not compromise, scientific integrity.
committed to policy of transparency and respon-
REFERENCES
Jonas Belouadi, Anne Lauscher, and Steffen Eger. Automatikz: Text-guided synthesis of scientific vector graphics with tikz. arXiv preprint arXiv:2310.00367, 2023.
Jonas Belouadi, Simone Ponzetto, and Steffen Eger. Detikzify: Synthesizing graphics programs for scientific figures and sketches with tikz. Advances in Neural Information Processing Systems, 37: 85074-85108, 2024.
Jonas Belouadi, Eddy 1lg, Margret Keuper, Hideki Tanaka, Masao Utiyama, Raj Dabre, Steffen Eger, and Simone Ponzetto. Tikzero: Zero-shot text-guided graphics program synthesis. Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 17793-17806, 2025.
Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, James Aung, Dane Sherbur,, Evan Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan, et al. Mle-bench: Evaluating machine learning agents on machine learning engineering. In The Thirteenth International Conference on Learning Representations.
Yifan Chang, Yukang Feng, Jianwen Sun, Jiaxin Ai, Chuanhao Li, S. Kevin Zhou, and Kaipeng Zhang. Sridbench: Benchmark of scientific research illustration drawing image generation model, 2025, URL https:://// /ttp///aaiiivv//aa//aa//eeee..aa org/ abs/2505. 22126.
Junsong Chen, YU Jincheng, GE Chongjian, Lewei Yao, Enze Xie, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li. Pixart-alpha: Fast training of diffusion transformer for photorealistic text-to-image synthesis. In The Twelfth International Conference on Learning Representations, 2024.
Kevin Ellis, Daniel Ritchie, Armando ics programs from hand-drawn images. Advances neural information processing systems, 31, 2018.
and Josh Tenenbaum. Learning to infer graph-
Panagiotis Fytas, Georgios Rizos, and Lucia Specia. What makes a scientific paper be accepted for publication?, 2021. URL https://arsee wwtp://wwwwww.////////a//////// 24 07112.
11

---

Published as a conference paper at ICLR 2026
Jiaxin Ge, Zora Zhiruo Wang, Xuhui Zhou, Yi-Hao Peng. Sanjay Subramanian, Qinyue Tan, Maarten Sap, Alane Suhr, Daniel Fried, Graham Neubig, and Trevor Darrell. Autopresent: Designing structured visuals from scratch. arXiv preprint arXiv:2501.00912, 2025. URL
https: fittps://arxee //axxivv...// wwwriii.orrr.oo org/ alos 2501 00912.
Yue Hu and Xiaojun Wan. Ppsgen: Learning-basel presentation slides generation for academic papers. IEEE transactions on knowledge and data engineering. 27(4):1085-1097, 2014.
Aaron Hurst, Adam Lerer, Adam Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Os- trow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276. 2024.
Intology. Zochi technical report. arXiv, 2025.
Sadeep Jayasumana, Srikumar Ramalingam, Andreas Veit, Daniel Glasner, Ayan Chakrabarti, and Sanjiv Kumar. Rethinking fid: Towards a better evaluation metric for image generation. In Pro- ceedings of the IEEF/CVE Conference on Computer Vision and Pattern Recognition, pp. 9307- 2024.
Zeba Karishma, Shaurya Rohatgi, Kavya Shrinivas Puranik, Jian Wu, and C Lee Giles. Acl-fig: A dataset for scientific figure classification. arXiv preprint arXiv:2301 122993 2023.
Yohan Kim, Jieun Lee, Jeong-Ju Yoo, Eun-Ae Jung, Sang Gyune Kim, and Young Seok Kim. Seeing is believing: the effect of graphical abstracts citations and social media exposure in gastroen- terology & hepatology journals. Journal of Korean Medical Science, 37(45), 2022.
Seongyun Lee, Seungone Kim, Sue Park, Geewook Kim, and Minjoon Seo. Prometheus-vision: Vision-language model as a judge for fine-grained evaluation. In Findings of the association for computational linguistics ACL 2024, pp. 11286-11315, 2024.
Luping Liu, Chao Du, Tianyu Pang, Zehan Wang, Chongxuan Li, and Dong Xu. Improving long-text alignment for text-to-image diffusion models. In The Thirteenth International Confer- ence on Learning Representations, 2025. URL https: 22K8zyIt70.
oppnrevveew https://
net forum? dd=
Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, and David Ha. The ai scientist: Towards fully automated open-ended scientific discovery. arXiv preprint 3XXi:22408.0622210 2024. URL https: www.//wwww.oo/..ooo arxiv. org/ abs/ / 2 2408. 06222v3.
Boris Malashenko, Ivan Jarsky, and Valeria Efimova. Leveraging large language models for scalable vector graphics processing: A review. arXiv preprint arXiv:2503.cm 04983, 2025.
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to fol- low instructions with human feedback. Advances in neural information processing systems, 35: 27730-27744, 2022.
Wei Pang, Kevin Qinghong Lin, Xiangru Jian, Xi He, and Philip Torr. Paper2poster: Towards multimodal poster automation from scientific papers. arXiv preprint wrXiv:2505.2149) 2025.
Yu-Ting Qiang, Yan-Wei Fu, Xiao Yu, Yan-Wen Guo, Zhi-Hua Zhou, and Leonid Sigal. Learning to generate posters of scientific papers by probabilistic graphical models. Journal of Computer Science and Technology, 34(1):155-1m) 169, 2019.
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text- conditional image generation with clip latents. arXiv preprint arXiv:2204.06125. 2022.
Juan Rodriguez, David Vazquez, Issam H. Laradji, Marco Pedersoli, and Pau Rodriguez. Figgen: Text to scientific figure generation, 2023a. URL https: / http:///pppeeeeee.eeeee net wtw.s/wwwnt//oouummmm formm? iid Hx T
/
Juan A Rodriguez, David Vazquez, Issam Laradji, Marco Pedersoli, and Pau Rodriguez. Ocr-vqgan: Taming eext-within-image generation. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp. 3689-3698, 2023b.
12

---

Published as a conference paper at ICLR 2026
Juan A Rodriguez, Abhay Puri, Shubham Agarwal, Issam H Laradji. Pau Rodriguez, Sai Rajeswar, David Vazquez, Christopher Pal, and Marco Pedersoli. Starvector: Generating scalable vector graphies code from images and text. In Proceedings of the Computer Viston and Pattern Recog- nition Conference, pp. 16175-16186, 2025.
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Bureu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural informa- tion processing systems, 35:36479-36494, 2022.
Yang Song, Jascha Sohl-Dickstein, Diederik Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. In Interna- tional Conference on Learning Representations, 2021.
M Sravanthi, C Ravindranath Chowdary, and Sreenivasa Kumar. Slidesgen: Automatic generation of presentation slides for a technical paper using summarization. In FLAIRS, 2009.
Giulio Starace, Oliver Jaffe, Dane Sherburn, James Aung. Jun Shern Chan, Leon Maksin, Rachel Dias, Evan Mays, Benjamin Kinsella, Wyatt Thompson, et al. Paperbench: Evaluating ai's ability to replicate ai research. In Forty-second International Conference on Machine Learning.
Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan. Autoregressive model beats diffusion: Llama for scalable image generation. arXiv preprint arXiv:2406.06552. 2024.
Qingyun Wang, Doug Downey, Heng Ji, and Tom Hope. SciMON: Scientific inspiration machines optimized for novelty. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume I: Long Papers), pp. 279-299, Bangkok, Thailand, August 2024a. Association for Computational Lin- guistics. doi: 10.18653/v 1/228 aclooo.. URL https: /t///aaaaat///// woolllnnhoooggo org/2024. acl-iong.15 18/.
Weiyun Wang, Zhangwei Gao, Lixin Gu, Hengjun Pu, Long Cui, Xingguang Wei, Zhaoyang Liu, Linglin Jing, Shenglong Ye, Jie Shao, et al. Internv13.5: Advancing open-source multimodal models in versatility, reasoning, and efficiency, arXiv preprint arXiv:2508. 18265, 2025.
Yidong Wang, Qi Guo, Wenjin Yao, Hongbo Zhang, Xin Zhang, Zhen Wu, Meishan Zhang, Xinyu Dai, Qingsong Wen, Wei Ye, et al. Autosurvey: Large language models can automatically write surveys. Advances in Neural Information Processing Systems, 37:1 151 195114, 2024b.
Jingxuan Wei, Cheng Tan, Qi Chen, Gaowei Wu, Siyuan Li, Zhangyang Gao, Linzhuang Sun, Bihui Yu, and Ruifeng Guo. From words to structured visuals: A benchmark and framework for text-to- diagram generation and editing. In Proceedings of the Computer Vision and Pattern Recognition 2025.
Conference, pp.
Yixuan Weng, Minjun Zhu, Guangsheng Bao, Hongbo Zhang, Jindong Wang, Yue Zhang, and Linyi Yang. Cycleresearcher: Improving automated research via automated review. In The Thirteenth International Conference Learning Representations, 2025. URL https::///////c.. //tp/r/vveee..eeeee/e net/forund d=bjesViooHYs
Yixuan Weng, Minjun Zhu, Qiujie Xie, Qiyao Sun, Zhen Lin, Sifan Liu, and Yue Zhang. Deepscien- tist: Advancing frontier-pushing scientific findings progressively. In The Fourteenth International Conference on Learning Representations, 2026. URL https:: //pppreeeeeeeee net forum?
id=c27gsiqqGs.
Alva West, Yixuan Weng, Minjun Zhu, Zhen Lin, Zhiyuan Ning, and Yue Zhang. Abduct, act, Scaffolding causal inference for automated failure attribution in multi-agent systems. arXiv preprint ar Xxxv:25009.14000 2025.
predict:
Qiujie Xie, Qingqiu Li, Zhuohao Yu, Yuejie Zhang, Yue Zhang, and Linyi Yang. An empirical analysis of uncertainty in large language model evaluations. In The Thirteenth International Con- ference on Learning Representations, 2025a. URL https: / openzeview net forum?
Id=J4xtuct22g.
13

---

Published as a conference paper at ICLR 2026
Qiujie Xie, Yixuan Weng. Minjun Zhu, Fuchen Shen, Shulin Huang, Zhen Lin, Jiahui Zhou, Zilan Mao, Zijie Yang, Linyi Yang, Jian Wu, and Yue Zhang. How far are ai scientists from changing the world?, 2025b. URL https: : http://awwvv..../....../ wttp://gg/.///.../a///a/// 232766
Qiujie Xie, Yixuan Weng, Minjun Zhu, Fuchen Shen, Shulin Huang, Zhen Lin, Jiahui Zhou, Zilan Mao, Zijie Yang, Linyi Yang, et al. How far are ai scientists from changing the world? arXiv preprint arXiv:2507. 23276, 2025c.
Sheng Xu and Xiaojun Wan. Posterbot: system for generating posters of scientific papers with neural models. In Proceedings of the AAAI Conference on Artificial Intelligence, 2022.
Yutaro Yamada, Robert Tjarko Lange, Cong Lu, Shengran Hu, Chris Lu, Jakob Foerster, Jeff Clune, and David Ha. The ai scientist-v2: Workshop-level automated scientific discovery via agentic tree search. arXiv preprint arXiv:2504.088 080666 2025.
Zhishen Yang, Raj Dabre, Hideki Tanaka, and Naoaki Okazaki. Scicapt: A knowledge augmented dataset to study the challenges of scientific figure captioning. Journal of Natural Language Pro- cessing, (3): 140-1165, 2024a.
Zhiyu Yang. Zihan Zhou, Shuo Wang, Xin Cong, Xu Han, Yukun Yan, Zhenghao Liu, Zhixing Tan, Pengyuan Liu, Dong Yu, et al. Matplotagent: Method and evaluation for llm-based agentic scientific data visualization. In Findings of the Association for Computational Linguistics ACL 2024, pp. 11789-11804, 2024b.
Hao Zheng, Xinyan Guan, Hao Kong, Jia Zheng, Weixiang Zhou, Hongyu Lin, Yaojie Lu, Ben He, Xianpei Han, and Le Sun. Pptagent: Generating and evaluating presentations beyond text-to- slides. arXiv preprint SSGv:25001.39336 2025.
Wendi Zheng, Jiayan Teng, Zhuoyi Yang, Weihan Wang. Jidong Chen, Xiaotao Gu, Yuxiao Dong, Ming Ding, and Jie Tang. Cogview3: Finer and faster text-to-image generation via relay diffusion. In European Conference on Computer Vision, pp. 1-22, 2024.
Minjun Zhu, Yixuan Weng, Linyi Yang, and Yue Zhang. Deepreview: Improving 11m- base paper review with buman-like deep thinking process. arXiv preprint arXiv:2503. 08569, 2025a.
Minjun Zhu, Qiujie Xie, Yixuan Weng, Jian Wu, Zhen Lin, Linyi Yang, and Yue Zhang. Ai scientists fail without strong implementation capability. arXiv preprint arXiv:2506.01372,, 2025b.
A DATA CURATION DETAILS FOR FIGUREBENCH
The curation of the FigureBench dataset was guided by rigorous, multi-stage pipeline designed to ensure high quality, relevance, and full adherence to open-source principles. Our process began with the Research- 14K dataset, from which we exclusively selected papers governed by permissive licenses (e.g., Research-Daataet-LLin Dataset-LLeenss and CC BY 4.0, as detailed in Table 3). This step ensured that all subsequent annotation and redistribution activities complied with the original authors' terms. From this licensed subset, an initial filtering pass using GPT-5 identified approximately 400 candi- date text-figure pairs likely to contain high-quality schematic diagrams. To validate these candidates for our gold-standard set, we utilized an online annotation platform and two expert annotators with backgrounds in machine learning. Each annotator independently judged whether figure accurately and effecttively represented the core methodology of its paper. A stringent unanimous approval pol- icy was enforced: figure was accepted as a positive sample only if both annotators gave their approval. This process yielded 200 high-quality positive samples, which form the core of our test set, and 200 verified negative samples (those not unanimously approved) suitable for training dis- criminator models.
B HUMAN EVALUATION PROTOCOL
To rigorously assess the practical utility of AUTOFIGURE and other baseline models. we designed a comprehensive human evaluation study hosted on a custom annotation website. We recruited 10 annotators who have all previously published as first authors academic papers in the computer
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Table 3: Data source details for FigureBench
Data Type
Source
License
Number
paper
Research-
Research- Dataset- Lacasset-Lcense&CCC BY 4.0
3200
survey
Arxiv
CC BY
40
CMU ML Blog
blog
The BAIR Blog +
CC
ICLR Blogposts 2025
OpenStax: Foundations
CC BY
textbook
of Computer Science
40
OpenStax: Information
CC BY-NC-SA 4.0
Systems
science field. To ground their evaluation in familiar context, each expert was presented with figures generated for their own past publications. The evaluation was structured into three distinct tasks, compelling the experts assess the quality of outputs from multiple perspectives: multi- dimensional scoring, holistic ranking, and a publication-readines selection.
B. TASK I: MULTI-DIMENSIONAL QUALITY SCORING
The initial task required experts to perform detailed, multi-dimensionll quality assessment of figures generated by six different AI models: AUTOFIGURE, Diagram Agent, GPT-Image.HTMI. Code, PPTX Code, and SVG Code. For each of the six generated figures, evaluators were asked to provide rating on five-star scale across three key dimensions: Accuracy, Clarity, and Aesthetics. The Accuracy dimension measured how faithfully the figure represented the core concepts, relation- ships, and technical details described in the original paper. one-star rating indicated a complete deviation from the paper's content, while five-star rating signified perfect representation.
The Clarity dimension assessed the figure's ' legibility and effectiveness in communicating informa- tion, considering factors such as text readability, the completeness of legends, and the logic of the layout. A one-star rating was for figures that were confusing and difficult to interpret, whereas a five-star rating for those that were immediately understandable. Finally, the Aesthetics dimen- sion focused on the visual design quality, including the harmony of the color scheme, the refinement of graphical elements, and its overall professional appearance suitable for academic publication. The user interface facilitated this process with interactive star-rating components for each of the 18 required judgments models dimensions), ensuring that evaluators provided a complete set of scores before proceeding.
B.2 TASK 2: HOLISTIC COMPARATIVE RANKING
Following the detailed scoring, the second task asked evaluators to perform a holistic ranking of all figures. This task was designed move beyond dimensional analysis and capture an overall judgment of quality. Crucially, the set of items to be ranked included not only the six Al-generated figures but also the original, human-created figure from the publication, serving reference. Evaluators were instructed to consider all aspects of quality, including the previously scored dimensions as well as less tangible factors like innovativeness and overall fitness for the paper's context.
ground-truth
The ranking was implemented through a drag- and-drop interface where each figure was displayed on movable card. These cards showed the model's name (or "Reference" for the original), preview of the figure, and its current rank from to Experts could adjust the order by dragging the cards or using "move up" and "move down" buttons, with the interface providing smooth animations to reflect the real-time changes. The system enforced a strict ranking, with no ties allowed, compelling the evaluators to make definitive comparative judgments. This design allowed for direct comparison of AL- AL- generated outputs against the human- authored baseline, providing a clear measure of their competitive quality.
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B.3 TASK 3: PUBLICATION INTENT SELECTION
The final task framed the evaluation in the most practical terms by simulating the author's decision- making process for publication. Evaluators were asked: "If you were the author of this paper, which of these figures, if any, would you be willing to use in a camera-ready version of your publication?" This task required them synthesize their quality assessments with practical considerations, such as stylistic fit with their paper, alignment with conventional academic standards, and potential impact on peer reviewers.
The interface presented each of the six Al-generated figures on selectable card. Experts could choose one or more figures they deemed publication-ready An explicit option, "I would not select any of the generated figures," was also provided to capture instances where none of the Al outputs met the required standard for publication. The interface provided clear visual feedback for selected highlighted border, and displayed summary of the current selections at the bottom of the page. This binary-style decision provided a direct measure of cach model's 's real-world applicability and acceptance rate from the perspective of the original author.
items, such green checkmark
C DISCUSSION AND FUTURE OUTLOOK
Our work establishes strong and generalizable foundation for Automated Scientific Schematic Generation (ASSG). By focusing on the Computer Science domain field with diverse and rapidly evolving visual language- we have demonstrated that the "Reasoned Rendering" paradigm can successfully produce high-quality, publication-ready figures. This achievement serves as a ro- bust proof-of-concept and a blueprint for future advancements in AI-driven scientific communica- tion.
Building on this foundation, an exciting future direction is the extension of our framework to other scientific disciplines. The methodology established in FigureBench and AUTOFIGURE can be adapted to create specialized tools that understand the unique visual conventions of fields like biology, chemistry, and economics. For instance, future work could incorporate domain- specific knowledge to accurately generate intricate biological signaling pathway diagrams or standardized molecular structures. This path forward leads from powerful generalist tool suite of expert AT illustrators, each tailored to empower researchers in their respective communities.
Furthermore, framework masters the creation of high-fidelity static diagrams, which remain the cornerstone of formal scientific publishing. Having established this essential capability, the logi- cal next frontier is to bring these static representations URB decoupiin structural planning from aesthetic rendering are perfectly suited for the genera- tion of dynamic and interactive schematics. We envision future systems that can produce animated figures to illustrate processes over time or interactive diagrams that allow for user-driven exploration of complex models. This represents transformative opportunity, moving beyond simply document- ing scientific findings to creating rich, immersive experiences that can accelerate understanding and discovery.
life. The core principles of AUTOFIG-
D USE OF LARGE LANGUAGE MODELS
LLMs were utilized as assistive tools at various stages of this research and manuscript preparation to enhance productivity and quality. Our use of these tools was supervised, with the final responsibility for all content resting with the human authors. In the initial phase of our work, we employed LLM- based tools to assist with literature discovery. These tools helped in identifying and summarizing a broad range of relevant prior work, which facilitated a comprehensive understanding of the existing research landscape. During the implementation of the AUTOFIGURE framework, we utilized Claude to assist in writing and refining code segments. This process accelerated development and helped in debugging and optimizing our software components. For the preparation of the manuscript, LLMS (e.g. Gemini-2.5-PP were used for text polishing, including improving grammatical correctness, clarity, and readability. The core scientific ideas, methodologies, and arguments presented this paper were conceived and articulated by the authors. Finally, upon completion of the draft, we sub- jected the manuscript to a secondary review process using the DeepReviewer (Zhu et al., 2025a) model. This tool helped to proactively identify potential weaknesses in our argumentation, exper-
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imental setup, and presentation. We carefully considered the feedback provided by DeepReviewer and made targeted revisions to address the concerns we deemed valid, thereby strengthening the final version of this paper.
Ooppone
Humnan Studyy
LLM
Apprendes Peodideecinee Approsaatinn
Peosene
(a). Original Figure
Brioa Saleriy
Diagram Agent
Rssul Art Hightii Invviial Fatictss
Nurtark
(c). GPT- Gmoge
(d). SVG
HTML
(f). AutoFigure
Figure 7: Qualitative comparison, contrasting baseline failures with AUTOFIGURE'S superior output.
LLM Artadis
LLM Defenses
Conversational
Ballyy Angrene
(a). Original Figure
(6). Diagram Agent
Evalustion
(c). GPT-Image
(d). SVG
(e). HTML
(f). AutoFigure
Figure 8: Analysis for the Survey category, showing AUTOFIGURE'S ability render complex relationships clearly and accurately.
E QUALITATIVE CASE STUDIES
To provide granular, qualitative understanding of AUTOFIGURE's capabilities, this section presents a targeted analysis of its performance against baselines across three distinct document types: technical blog, academic survey, and textbook. These examples, shown in Figures 7, and
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Aegineen
Chapter 12: The Naterfall Model
repurcaal ravioraaion softew's covtlgree yrices socal
Ieep
Sequential Process
Key Characteristies
Argolneets
priie auttaces mesee sertann to sndo chappe.
Sulgeet
Historical
(a).
Original Figure
Whileiieee Sede Analysis
Key Takeswit
an for vthecc
Diagram Agent
ANTERFALL MODE
Oetiowet cour
(c). GPT-Image (d). SVG
(e). HTML
(f). AutoFigure
Figure Case study for the Textbook category, demonstrating AUTOFIGURE's effectiveness in creating clear pedagogical illustrations.
9, visually substantiate the quantitative findings by highlighting the specific failure modes of base- line methods and demonstrating how AUTOFIGURE's "Reasoned Rendering" paradigm successfully overcomes them.
The blog case in Figure 7 illustrates the generation of complex process diagram. The end-to-end model, GPT-lmage, not only fails to render legible text but also hallucinates entirely incorrect topic, producing a diagram for "Conditioned Language/Word Detection" instead of the intended "Virtual Persona Opinions." 'The code-based methods (SVG, HTML.) manage represent the basic flow but result visually simplistic and unprofessional layouts that fail to capture nuances the original figure. In stark contrast, AUTOFIGURE correctly abstracts the source text into a coherent, multi-step narrative ("Prior Approaches," "Our Approach," "Result") and renders using a clean, aesthetically pleasing design with thematic icons, demonstrating sophisticated understanding of both content and presentation.
complex challenge: visualizing taxonomy of LLM safety concepts. Here, the baselines fail catastrophically on structural fidelity. Diagram Agent produces gross oversim- plification, while GPT-Image and the code-based methods fail capture the critical categorical relationships between Attacks, Defenses, and Evaluation metrics. Their outputs are either logically flat, confusing flowchart. AUTOFIGURE, however, excels by reimagining the content as clear infographic. It correctly organizes concepts into distinct, labeled groups (LLM Attacks, LLM Defenses, Evaluation) around central "Conversational LLM" motif, using thematic icons (a bomb for attacks, scales for defenses) to enhance comprehension. This shows an ability not just to reproduce, but logically restructure information for clarity.
Figure presents
incorrect or presented
Finally, the textbook example in Figure 9 highlights the importance of pedagogical clarity. The original figure a simple, canonical diagram the Waterfall Model. GPT-Image captures the basic downward flow but suffers from severe text-rendering artifacts, making the labels illegible and thus educationally useless. The SVG and HTML methods produce a structurally correct but visually sterile diagram. AUTOFIGURE's output is transformative; it not only generates clear and accurate waterfall process with engaging icons but also enriches it by synthesizing key information from the source text into supplementary panels like "Key Characteristies: " "Historical Context," and "Key Takeaway. This moves beyond mere illustration to create a comprehensive, high-quality educational artifact, demonstrating deep contextual understanding of the task's purpose.
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F LIMITATIONS AND FAILURE ANALYSIS
Despite the strong performance of AUTOFIGURE, we identify several persistent limitations. Most notably, we explicitly list fine-grained text- sett-rendeing accuracy as a primary bottleneck of the current system and, more broadly, of existing generrttnn pipelines on FigureBench. Even with our erase-and-correct posteprocsssing.. the system can still exhibit rare but consequential character-level emrors under small font sizes, dense layouts, or visually complex backgrounds. A representative example is the spelling mistake "ravity" (missing "g" in "gravity") observed in one generated fig- ure, which illustrates the gap between long-contex semantic reasoning (where the pipeline largely succeeds) and pixel-/glyph-leve fidelity (where even minor artifacts occur). Importantly, argue that the existence of such subtle errors inversely highlights the discriminative power and re- search value of FigureBench: it exposes hard, unresolved frontier for the community, rather than indicating a fundamental flaw in our approach. Beyond text accuracy, a second limitation is the tension between aesthetic presentation and scientific rigor: our "concretization" behavior can oc- casionally drift beyond the strictly literal content if the source text is underspecified. and theoreti- cal or vaguely phrased passages may lead the model to produce visually plausible but imperfect conceptual structure (e.g., compressing nuanced distinctions or inadvertently imposing hierarchical relations on parallel concepts).
To make these limitations transparent and actionable, the revised manuscript adds a dedicated Limi- tations and Failure Analysis section with concrete failure cases and causal analysis, complementing our qualitative case studies (Appendix $ E; Figures 7-9). While those case studies demonstrate that AUTOFIGURE substantially mitigates common baseline failure modes (c.g., illegible text, topic hallucination, and structural collapse), they also help delineate boundary conditions for our system: (i) when accurate rendering hinges on domain-specifie conventions or external facts not explicitly stated in the input, the produced structure or labeling may be incomplete; (ii) when the input de- mands fine grained ontological distinctions (e.g., multi-branch taxonomies), the model may favor cleaner visual organization the expense of faithfully preserving subtle relations; and (iii) when layouts are dense, small typographic errors can survive post-processing and harm educational util- ity. Looking forward, we note several promising directions to address these deeper reasoning and verification gaps, including incorporating external knowledge bases eetrieval-augmente ground- ing) and introducing domain verifiers (Domain Verifiers) that can enforee constraint checks over entities, relations, and terminology before final rendering. We expect such verification-oriae -rriented components-togeeee together with more robust constrained text rendering (e.g.. vector-text overlays or tighter OCR-to-layout alignment)-to be key future steps toward closing the "reasoning render- ing" gap on FigureBench.
G PERFORMANCE EVALUATION ON OPEN-SOURCE MODELS
To ensure the reproducibility of our research and facilitate broader adoption with minimal deploy- ment costs, we extended our evaluation to include state-of-the-art (SOTA) open-source and open- weight models. Specifically, we evaluated AUTOFIGURE using vT-23--1L-235--222B.. GLM-4.55V and ERNIE-4.5-VL as the reasoning backbones. This analysis aims to verify whether AUTOFIGURE can maintain high-quality generation without relying on proprictary commercial APIs.
Comparative Analysis with Commercial Models. As presented in Table 4, the experimental re- sults demonstrate that top-tier open-source models are highly capable of driving the AUTOFIGURE framework. Notably. V achieved an Overall Score of 7.08, which not only signif- icantly outperforms other open-source baselines like GLM-4.5V (5.99) but also surpasses several leading commercial models, including Gemini-2.55Pro (6.99), Claude-4.1 1-Opus (6.80), and Grok-4 (6.76). It ranks second only to GPT-5 (7.48) among all tested models. This finding strongly sug- gests that the open- community has reached a maturity level sufficient for complex scientific illustration tasks.
Detailed Capabilities and Win-Rates. We further provide a granular breakdown of open-source model performance across specific sub-dimensions and their Overall Wan-Rates in blind pairwise comparisons (Table 5). The performance gap between models highlights a strong correlation be- tween the system's output quality and the backbone model's capabilities in visual reasoning and
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Table 4: Performance comparison between Commercial and Open-Source models
Visual Design
Comm. Effect.
Content Fidelity
Model Type
Model
Overall
GPT-5
7.17
7.62
7.70
7.48
Gemini-2.5-Pro
6.59
7.59
6.54
6.99
Commercial
Claude-4.1-Opus
6.75
7.17
6.61
6.80
Grok-4
6.46
7.60
6.49
6.76
Owen3-V1-235B
7.57
7.01
7.18
7.08
Open-Source
GIM--.5VV
6.09
6.53
6.13
5.99
5RNIF-4..-VI.
3.04
2.89
2.68
2.64
instruction following. The success Qwen3-V VL confirms that by selecting a capable open-source backbone, AUTOFIGURE can be deployed as cost-effective solution.
Table S: Detailed dimensional breakdown and Overall Win-Rates for Open-Source models.
Visual Design
Comm.
Content
Win-Rate (Overall)
Model
Aesth.
Expr.
Polish
Clarity
Flow
Soph.
Fidel.
Qwen3-VL-235B
0.90
0.90
0.90
0.25
0.25
0.25
0.15
40.0%
GLM-4.5V
0.70
0.70
0.70
0.45
0.65
0.25
0.20
55.0%
ERNIB-4.5-VL
0.20
0.15
0.20
0.00
0.10
0.05
0.05
10.0%
H EXTENDED BLIND PAIRWISE COMPARISON WITH ABSOLUTE QUALITY OPTIONS
To address the limitations of simple relative ranking (which restricts choices "A", "B", or "Tie") and to investigate the absolute quality of the generated figures, we conducted an extended blind pairwise comparison. In this experiment, we updated the evaluation prompt to include "Both Good" and "Both Bad" options. This allows us to identify potential "race-to-the-be to-theebottomm scenarios (where a model wins only because the competitor is worse) or cases where models are indistinguishable in quality.
We performed this evaluation on the Paper subset, challenging category requiring high structural fidelity. The results are summarized in Table 6.
Table 6: Extended pairwise comparison results on the Paper subset with "Both Good" and "Both Bad" options. AutoFigure demonstrates a high win rate while the low "Both Bad" count confirms a high quality baseline across most methods.
Visual Design
Communication
Content
Pairwise Decisions
Method
Overall
Aesth.
Expr.
Polish
Clarity
Flow
Sophist.
Fidelity
Win
Lose
Good
Bad
AutoFigure
0.88
0.93
0.88
0.38
0.45
0.35
0.28
29
11
0
0
0.73
Gemini-HTML
0.63
0.53
0.63
0.58
0.53
0.30
0.25
21
18
1
0
0.53
Gemini-SVG
0.48
0.40
0.48
0.48
0.45
0.33
18
20
0
0.45
GPT-Image
0.25
0.25
0.25
0.08
0.08
0.00
0.00
4
36
0
0
0.10
Diagram Agent
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0
39
1
0.00
The extended evaluation yields three critical insights: the negligible occurrence of "Both Bad" (1 instance) and "Both Good" (3 instances) selections confirms that the models meet high baseline of readability while maintaining clear discriminability in quality, rather than suffering from "race to the bottom." Within this validated framework, AutoFigure maintains dominance with a 72.5% win rate; its overwhelming superiority in visual design metrics (e.g.. 0.93 vs. 0.53 in Expressiveness) significantly outweighs the structural clarity of code-based baselines like Demini-HTMB securing the highest overall score (0.73) and demonstrating true publication readiness.
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1 ABLATION STUDY: CONTRIBUTION OF THE TEXT REFINEMENT MODULE
To systematically evaluate the contribution of individual components within our multi-stage pipeline, we conducted a focused ablation study on the Stage 2 Text Refinement (Erase-and- Correct) module. This complements the ablations on the Reasoning Backbone and Intermediate Format presented in the main text. We compared the full AutoFigure pipeline against a variant where the text erasure and correction step was removed (w/o Text Refinement) quantify its impact on the final output quality.
Table Ablation study results on the Text Refinement module. While the Overall score shows a moderate increase, the module significantly enhances visual design metrics, confirming its role achieving publication- ready quality.
Visual Design
Communication
Content Quality
Model
Overall
Aesth.
Expr.
Polish
Clarity
Flow
Acc.
Comp.
Appr.
AutoFigure (Full)
7.49
7.20
6.80
7.53
7.73
7.45
6,83
6.42
7.18
w/o Text Refinement
7.39
7.12
6.70
7.50
7.70
7.53
6.74
6.47
7.14
The comparative evaluation yields clear conclusion regarding the necessity of the refinement stage. Although the Overall score improvement appears moderate (7.18 vs. 7.14), the granular breakdown reveals that the Text Refinement module drives significant gains in dimensions most critical to visual presentation: Aesthetic Quality (+0.10), Professional Polish (+0. 10), and Visual Expressiveness (+0.08). These improvements confirm that the "Erase-and- -aranet strategy is pivotal for elimi- nating generative artifacts (such as blurred text) and elevating the figure from a "usable" draft to professional, publication-ready illustration.
J EFFICIENCY AND COST ANALYSIS
To assess the practical viability and scalability of AutoFigure real-world applications, we con- ducted comprehensive analysis of inference latency and economic costs. We utilized typical long- form academic papers (average length > 10kk tokens) as input and compared two distinct deployment settings: (1) A commercial closed-souree solution using the Gemini-2.5-PH API, and (2) A lo- cal deployment using the open-source Qwen-3-VL model on a high-performance computing node equipped with NVIDIA H100 GPUs.
As detailed in Table 8, the choice of deployment strategy significantly impacts both generation speed and cost. When relying the commercial API, generating single publication-ready il- lustration takes approximately 17.5 minutes with an average cost of $0.20. In contrast, deploying the system locally H100 GPUs reduces the total generation time ~9.3 minutes-2 nearly 2x apeedup-primarily due to the elimination of network latency and higher inference throughput during the intensive iterative reasoning phase (Stage 2). Furthermore, the local deployment model reduces the marginal cost per figure to effectively zero (excluding hardware amortization and elec- tricity).
Our experiments indicate that deploying AutoFigure does not require prohibitively expensive su- percomputing clusters. The performance metrics reported for the local setup (Qwen-3-VL. can be achieved using a computing node equipped with 2x NVIDIA H100 GPUs or two standard NVIDIA DGX Spark servers (approximate value $3,000 each). This accessibility ensures that research labs can deploy AutoFigure locally to ensure data privacy and high throughput without recurring API costs.
K HUMAN SANITY CHECK ON AUTOMATED DATASET STATISTICS
We utilized VL-335 to automatically compute statistical metrics (Text Density, Components, Colors, and Shapes) for the FigureBench dataset. To address potential concerns regarding the reli-
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Table 8: Breakdown of efficiency and cost for generating single scientific illustration. Comparing Commer- API (Gemini-2.5) vs. Local Deployment (Qwen-3-VL H100).
Gemini-2.5 (API)
Qwen-3-VL (Local)
Stage
Core Task
Remarks
Time Cost
Time Cost
Stage 1
Concept Extraction
-22s / $0.01
-2ss $0.00
Local inference climinates
Method Distillation
work latency, doubling speed.
Stage 2
Layout Planning (Avg. 5
-660s $0.14
-390s/ S0.00
throughpul significantly accelerates itcrative critique- and-refine loop.
H100
iterations)
Stage 3
Aesthetic Rendering Post-processing
~ 3770 $0.05
-250s / 50.00
Code generation and local ren- dcring/OCR are faster than API calls.
Total
End-to-End Generation
17.5 min/ $0.20
-9.3 min $0.00"
Local deployment achieves speedup with negligible marginal cost.
Marginal cost excludes hardware amortization and electricity.
ability of these automated measurements and to quantify potential errors, we conducted a human- audited sanity check.
Methodology. We randomly sampled subset of 21 text-figure pairs from the FigureBench test set. Expert annotators were tasked with manually verifying the four key metrics for each sample. The detailed breakdown of this human audit is presented in Table 9.
Table Human audited statistics on random subset samples. The results serve a sanity check the automated statistics provided by Intern VL-3.5.
Text Density (%)
Connected Components
Paper ID
Color
Shape
2212.0950
75
5
2304.01665
30
4
5
2304.0355
40
5
5
6
2305504505
65
8
4
2305.15072
4
2510.0513
65
6
3
2310.05157
90
3
2
2402.13753
3
2402.16045
65
$
3
2402.1818
45
4
5
2404. 1196
90
8
6
2405.00 1405.06312
55
8
7
2408. 1179
30
7
8
2409.07429
70
4
10
7
2411.00816
2412.11506
45
4
5
2502. 10709
50
6
5
2502.13723
2503.06635
20
9
10
7
2503.08569
75
6
10
5
2504.20972
45
7
5
Average (Human)
54.29
5.62
7.29
5.29
Comparison and Discussion. Comparing the human-verified averages this subset with the full-
dataset automated statistics (Text Density 41.2%, Components 5.3, Colors 6.2, Shapes 6.4), we observe that the values are in the same order of magnitude and the relative deviations within
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reasonable range. Specifically, both human and automated statistics indicate high level of visual complexity (Components: 5.62 vs. 5.3; Colors: 7.29 vs. 6.2), confirming that the dataset presents non-trivial challenge for generation models. Regarding text density, the human estimate (54.29%) slightly higher than the automated measurement (41.,%) likely because human annotators tend to perceive the bounding box area of text blocks whereas the model calculates pixel-level density; nevertheless, both metrics consistently categorize the samples as "text-heavy" compared to standard image datasets. Overall, these results verify that the automated statistics generated by Intern VL-3.5 are reliable at macro level, effectively characterizing the difficulty distribution of FigureBench and supporting the validity of our dataset analysis.
L STYLE CONTROLLABILITY AND DIVERSITY
To address concerns regarding the apparent style uniformity in the main paper and demonstrate the versatility of our framework, we conducted a controlled experiment on style controllability. We emphasize that the consistent visual style (Q-version avatars with Morandi color palette) used throughout the main text was a deliberate choice to ensure visual consistency and readability for comparative analysis, rather than limitation of the model.
Experimental Setup. We kept the structural layout and textual content (Stage output) fixed and only varied the style description prompt Stage 2. We tested three distinct style prompts: 1) Prompt I (Default): "Delicate and cute cartoon comic style (using Morandi color palette)": 2) Prompt (Creative):: "comic style"; and 3) Prompt 3 (Minimalist): "modern minimalist design".
Quantitative Analysis. Table 10 presents the automated multi multi-dimenssonal evaluation results. The Overall scores across the three styles are highly consistent (ranging from 7.18 to 7.27), indicating that altering the style descriptor does not negatively impact the structural integrity logical flow of The
the illustration. Table 11 shows the results of the blind pairwise comparison Win-Rates are similarly stable, confirming that AutoFigure can adapt to different aesthetic require- ments without compromising content quality.
Table 10: Automated multi-dimensional scores under diffcrent style prompts. The consistent Overall scores demonstrate that AutoFigure maintains high quality across diverse aesthetic styles.
Aesthetic & Design
Visual Express.
Prof. Polish
Logical Flow
Complete- ness
Approp- riateness
Style Prompt
Clarity
Accuracy
Overall
Prompt 1 (Default)
7.49
7.20
6.80
7.53
7.73
7.45
6,83
6.42
7.18
Prompt 2 (Comic)
7.32
7.24
6.78
7.58
7.78
7.63
7.02
6.82
7.27
Prompt 3 (Minimalist)
7.14
6.27
7.09
7.72
7.75
7.54
6.75
7.28
7.19
Table 11: Blind pairwise comparison results for different style prompts. Comparison metrics show robust performance across styles.
Visual Design
Comm.
Content
Decision Counts
Style Prompt
Overall
Aesth.
Expr.
Polish
Clarity Flow Sophist. Fidel.
Win Lose
Good Bad
Prompt 1 (Default)
0.85
0.85
0.85
0.40
0.50
0.35
0.35
7
0
0.65
Prompt 2 (Comic)
0.90
1,00
0.90
0.35
0.40
0.35
0.40
12
7
0
0.60
Prompt 3 (Minimalist)
0.65
0.65
0.65
0.65
0.65
0.45
0.35
13
5
1
0.65
M MINIMAL WORKING EXAMPLE: WORKFLOW FOR THE "A2P" PAPER
To illustrate the practical operation of AutoFigure, we detail the step-by-ste generation process for the main figure of A2P (West et al., 2025) and corresponding artifacts in the supplementary material. The end-to-en end workflow proceeds follows:
Input Analysis and Method Extraction: AutoFigure first ingests the source document (supporting formats such pdf, txt, md, and texx. In this instance, the tex source file is processed
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by Gemini-2.5-Pro, which analyzes the full text to extract and distill the core methodological con- tributions.
Initial Layout Generation: The extracted methodology is passed to the Initial Design Agent and Initial Critic Agens. These agents collaborate to generate a preliminary vector layout file (iteration. svg) along with its corresponding -png version (iteration__..pp and pro- vide an initial quality assessment score.
The initial layout and score fed into the "Critique-and-Reeiie Refinn loop. The critique agent orchestrated a comprehensive optimization on the initial draft interatiin_a 1) It corrected the erroneous arrow connections ensure logical data flow towards the output; 2) It engineered the spatial arrangement by moving the "Output" module to the right, establishing an in- tuitive left-to-right visual flow; 3) It expanded the 'Crucial Pre-processing' section refine method- ological details; 4) It consolidated the disorganized layout into three distinct, aligned columns; and 5) It resolved aesthetic artifacts, specifically fixing the text overflow in the conversation log steps. In this specific case, the design achieved the required quality threshold (score of 8.5) after the first iteration (iteration_]l triggering an early exit from the loop without further modification with its artifacts layout. png and 1 layout... svg.
Iterative Refinement:
The Rendering Module employs Gemini-2.5-Pro to translate the finalized SVG code into descriptive text-to-image prompt. This prompt, along with the rasterized layout reference (layout.. ppng) is sent to the image generation model (Nano-Banana) to synthesize the aesthetically polished illustration (pol (polished, ww..she.ppggg
Aesthetic Rendering:
To address potential text rendering artifacts, EasyOCR is used to detect text content and bounding box coordinates from polished png, storing the data in mapping file (library. json).
OCR Extraction:
Text Verification: 1ibrary. json and the ground-truth structure layout. svg submit- ted to Gemini-2.5-Pro for verification. The model corrects any OCR errors or hallucinations in the extracted text using the SVG the ground truth, producing validated text mapping perrected_libracy.
Background Erasure:
The ClipDrop API is applied to pol i shed. png remove the original (potentially blurred) text, resulting in a clean background image (erased. png).
Final Composition: Finally, Gemini-2.5-Pro utilizes the coordinates and content from corrected_ Serrectee__ibbaryr libraryy..... json to programmatically overlay precise, vector-quality text onto the erased. png background (generating final presentation slide), thereby producing the final, publication-ready scientific illustration (figure (etgure.ppx)..............)))).)
N EXTENDED BASELINE EXPERIMENTAL RESULTS
To comprehensively evaluate the performance positioning of AutoFigure, expanded com- parative experiments on the Paper category by incorporating two additional classes of baselines: TiKZzbbsed code generation methods and Agentic presentation systems. Specifically, duced TikZero and TikZero+ (Belouadi et al., 2025) as representatives of the Automatikz, paradigm, which attempts to directly generate compilable LaTeX TiKZ code from scientific text, and AutoP- resent (Ge et al., 2025) as a representative of presentation generation agents that focus on arranging content into slide layouts. Note that we excluded systems like Paper2Poster (Pang al., 2025) and PPTAgent (Zheng et al., 2025) from this specific benchmark, as they strictly require original source images for layout arrangement rather than generating conceptual illustrations from pure text. The quantitative results of this expanded comparison are presented in Table 12, which clearly contrasts the capabilities of these different paradigms.
intro-
The results reveal a dominant performance by AutoFigure (Overall: 7.03, Win-Rate: 53.0%), while the new baselines struggle significantly. TikZ-based methods (TikZero/TikZeroo/ scored extremely low (Overall < 1.5), failure that extends beyond syntax errors fundamental limitation of the end-to-end code generation paradigm; forcing an LLM to linearly serialize dighediimnsional sci- entific structures (dozens of entities and complex topological flows) into LaTeX code imposes an excessive cognitive load, causing the model to deplete its reasoning capacity on low-level coordi- nate calculations rather than macro-level logical construction. In contrast, AutoFigure's decoupled
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Table 12: Extended baseline comparison results under the Paper category.
Visual Design
Communication
Content Fidelity
Method
Overall Win-Rate
Aesthetic Express.
Polish Clarity
Flow
Accuracy
Complete.
Approp.
AutuFigure
7.28
6,99
6.92
7.34
7.87
6.96
6,51
6.40
7.03
53.0%
HTML-Code
5.90
5.04
5.84
7.17
7.38
6.99
6.37
6.15
6.35
11.0%
SVG-Code
5.00
4.19
4.89
6.34
6.48
6.15
5.53
5.37
5.49
31.0%
GPT-Image
4.24
3.47
4.00
5.63
5.63
4.77
4.08
4.25
4.51
7.0%
AutoPresent
2.74
1.79
2.00
2.87
2,91
3.15
2.60
2.35
2.55
10.0%
Diagram Agent
2.25
1.73
2.67
2.49
1.72
1.94
2.12
0.0%
TikZero+
1.52
1.25
1.38
1.90
1.93
1.20
1.10
1.35
1.45
0.0%
TikZero
2.00
1.50
1.00
1.00
1.50
1.00
1.00
1.00
1,25
0.0%
Ceeasoonnngttee--RReeeeee strategy effectively bypasses this bottleneck. Similarly, while Auto- Present (Overall 2.55) outperformed TikZ. methods, it still lagged significantly behind AutoFigure because such agents are primarily designed for arranging existing textual and visual assets into slides rather than designing explanatory schematics from scratch, lacking the specialized reasoning modules required to translate abstract scientific text into visual logic.
O QUALITATIVE ANALYSIS ON CHALLENGES IN THE "PAPER" CATEGORY
Our quantitative evaluation revealed that the "Paper" category exhibits lower win rates compared to "Survey" or "Textbook" categories. To investigate the underlying causes, conducted deep qualitative analysis using the InstructGPT paper as representative case study. We attribute this performance gap primarily the hierarchical complexity of the information and the necessity for novel, bespoke design patterns that characterize research papers.
primary challenge lies in the hierarchical density information. Unlike textbook diagrams that often explain single isolated concept, illustrations in research papers, such as the InstructGPT framework, frequently need visualize information across three distinet depth levels simultane- ously. This includes the macro-level workflow (e.g., the transition from SFT to RM and PPO), the micro-level procedural sub-steps within each phase (e.g.. constructing demonstration datasets, rank- ing candidate outputs), and the fine-grained entity details (e.g., specific roles like human labelers, pre-trained models, or loss terms like KL penalty). For AutoFigure, extracting this multi-layered structure from long-form text presents massive challenge during the semantic parsing stage. The model must perform complex reasoning to determine which information constitutes "critical node" for visualization versus what should be condensed into textual descriptions, and subsequently decide y how to spatially arrange these nested relationships in a 2D layout. This cognitive load is significantly higher than that required for standard pedagogical schematies.
Furthermore, unlike surveys or textbooks which often rely on established schemas (such as tax- onomy or canonical flowcharts), scientific paper illustrations typically bespoke designs intended to represent a unique, novel pipeline. For instance, the original InstructGPT diagram em- ploys specific color coding and positional grouping to uniquely delineate its three stages, a visual structure tailored specifically to its methodology. Consequently, AutoFigure cannot rely on learn- ing stable visual templates or "pattern matching" from the training data. Instead, it must engage in "design from scratch," conceptualizing a custom topology for pipeline that has no prior visual precedent. This high degree freedom leads to a prevalent trade-off in our generated results: the model may either merge sub-steps to maintain clean layout, resulting in penalties for incomplete information, or attempt to preserve every detected node, leading to a cluttered layout that reduces readability. This acute trade-off between structural completeness and aesthetic clarity explains the lower win rates observed the Paper category compared to domains with more standardized visual grammars.
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Published as a conference paper at ICLR 2026
P HUMAN-LLM CORRELATION STUDY
To ensure the methodological rigor of our evaluation and address potential concerns regarding bias in the VLM-as-a-judge paradigm, conducted dedicated Human-LLM correlation study. Specif- ically, we recruited human experts to rigorously score 95 generated samples covering five different generation methods across all evaluation dimensions, including Visual Design, Communication Ef- fectiveness, and Content Fidelity. We then calculated the statistical correlation between these human scores and the automated scores assigned by the VLM.
The experimental results demonstrate that the VLM evaluator is highly aligned with human scores, both numerically and in terms of ranking consistency. First, regarding the Overall Score Corre- lation, the Pearson Correlation coefficient reached r 0.659 (p < 0.001), indicating signifi- cant linear positive correlation between the VLM scoring trends and human experts, confirming the model's ability to accurately capture quality differences.
Second, regarding Ranking Consistency, the Spearman Rank Correlation was p 0.593, and Kendall's Tau was 0.497. These metrics indicate that the VLM maintains robust consistency with human judgment in determining the relative ranking of different models. Notably, the Mean Rank- ing Error (MRE) calculated to be only 0.98, implying that the ranking deviation between the VLM and humans is, on average, less than one position.
Third, we performed Sub-metric Validation. Statistically significant positive correlations were observed across the three core dimensions: Accuracy (r 0.646)) Aesthetics (r 0.587), and Clarity (r = 0.559). This demonstrates the robustness of the VLM across different evaluation perspectives. Collectively, this analysis confirms that the VLM-as-au a---as---judge method adopted this work is verified and reliable evaluation proxy that accurately reflects human preferences.
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Human LLM Evaluation Comparison
Ranking Position Compartson Spearman p 0.593 (p 0.001)
Average Score Compartson Pearson 0.659 0.001)
Magain
Code
Nefect Aguanwe
Unwerf
LLM Ranking Position
Averoge
2.0
1.5
2.0
2.5
5.0
3.5
4.0
45
5.0
Human Ranking Position
Human Average Score (1-5)
Accuracy Dimenston
Clarity Dimension
(p
LEM Accuracy Score (0-10)
Score
Accuracy
Clarity
Figure 10: Human-LLM correlation study validating the VLM-as-a-judge paradigm. Subplots illustrate the alignment between human expert ratings and VLM automated scores across (Top-Left) ranking positions, (Top-Right) overall average scores, (Bottom-Left) accuracy dimension, and (Bottom-Right) clarity dimen- sion.
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Published as a conference paper at ICLR 2026
Q
CASES
The Voting Paradox: How Majority Preferences Can Be Inconsistent
Individual Vater Preferences
Head-to-Head Votes
The Resulting Paradox
When sciedd on pains majerity winser emerges each time
The collection preferenca cyclicl
Voter A
1st:
2nd:
3rd:
SSs
Turkey
Turkey
Lasegna
Turkey
Beef
beets
beats
Voter B
Tot:
2nd:
3rd:
Losogna
Turkey
Beef
Wins
Lasegna
Voter C
2nd
3rd:
Lasagna
Beef
beats
Turkey > Boef Lasagna Turkey...
Lasegne
Turkey
Lasogna
Wins
Turkey
Conclusion:
The collective preference derived from individual rasional preferances through majonty rule can be cyel cal and non ton-transiivve meaning there ne stable consistent majortty winner despite each individual voter being rational and ordered.
Figure 11: This is Case from Textbook. The original text is located at [Link].
Baumrind's Framework of Parenting Styles
Permissive Parenting High Warmsh Structure
High Warmtt Respettionss))
Authoritative Parenting
Narmi
Core Appecoct: Child focused Iidu gent
Care Approach Relationship focused Democaatc Modapher: The Dreet
Parest-Oild Interaction
Metapher: The Gelain Dessert
Parent- Crld Interaction
Frar dlore
dusipl Plrrecterd pedi sudtede
agiarate rrsteree Paal teltlaa rusisiinnalll
featon the click
High wil meteen cepalit rellect
[Low Samandinggaas
Low
High plgh Semandingseu
Uninvolved Parenting
Authoritarion Parenting
Low &
Warrth
Approachi fecused Neglectfi
Charatteridte Poende tile cheii senelceddee ahiiidt
Patertial Outcorses ni segalo
Characteritic dewunk-ee dedeeiate prense
Outcorees
Ecargle (Pau deed sountt offeeing gullaas))
Exargle uidee
Warmat
Based wirh Diana Resembed (itas, 2070, with Vietesteed whale addertly Ebaoner Marcstay Martin (HEEO
Figure 12: This Case from Textbook. The original text is located [Link].
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Published as a conference paper at ICLR 2026
The Microenvironment:
Five Factors Influencing a Company
These factors directly impact marketing decisions but are not directly controlled by the morketer.
Suppliers
The Company Marketer
2. Market Intermediaries
DDO
Provide
Role: Third pary sollers who didtitute the product.
operations
Factor Bargaining power of ti pplerr can affect
Retailes::
Wholesslers Sell
Exumple:: plac piraa and contred panda farm ricrowone oven
to customera
other tusinazees. edp, (elodttical (appillos)
eg. Tarpet,
4. Competitors
Customers
General Public
Rode: community within Kaye Public percepiion and and rep Jarionn are essential long farm
Role: Other bxsiinesces operati perttiggg the same narlect Seratogy: reallyee
Rote: tanget aud tnce for The company's procucts Unddrstardinng efectie
gap propoudtio..
Figure 13: This Case from Textbook. The original text located [Link].
The Marketing Exchange Process
Buyer (or Customer)
The Seller Offers product/service to sotisfy the need.
Something Volue (e.p. Money)
Hos a want or need (e.g hunger).
Desired Object (Product Servica)
Desired Object
Key Distinction: Customer vs. Consumer
The Concept of Value Volue perceived benefit
Scenorio Differer ente Individuols
The Same Individual
An exchonge aocura ONLY
Benefits > Costa
purchoses
Benefity (eg, cover tmo)
teg, money, etfort, (mm)
The Complete Process Flow
Buyer Has a Need
2. Evaluates Value
3. Exchange Occurs
4. Need Satisfied
2 Costs 2 Benefits (No Volue) NO EXCHANGE
Figure 14: This is Case from Textbook. The original text is located [Link].
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Published as a conference paper at ICLR 2026
The Circular Flow Model of the Economy
Households
Spending (9)
GOOOS & SERVICES MARKET
Firms
(Firms Supply, Househokks Demondd) Ci Giomlles: Grocer computers hoirzuts. streaming services
Goods
F-Factors of Production
LABOR (FACTOR) MARKET
(fouseholds Supply. Firms Demond) retal work land copital
How Economists Work: Methodological Analogy
Economist
Toolkit
Theories
Economic Issue
INFLATION
Applies
Solve
Y=C+I+G
Figure 15: This Case from Textbook. The original text located [Link].
The Balance of Payments: Two-Way Street
Visucliziig the flow trade, Investment, payments
Home Country (eg. US Bconsmy)
Trade Goods Services
Rest of the World (e Goool Eccnemy)
NATIONAL ECONDHY
(+g, sutturre rold Fusza aysntsnd products ABS)
GLOBAL ECONOHY
Expers
(e.) pouns Cie euros ye Non foeegnoueett
U.S. Imports of Goods & Services (eg. elecroncs Sore))
Paymonts Imports
Part B: Investment Income
Di USS comaang bullingg laccary o1er2011)
(4.. protis and de car de ant bock to J5L)
gn
(KD. forigg twester baging U.S. podes bondu)
(hd. Potrec p2 on 3o hneig benes
Notel Un lowed tronefers (eg. foneign dld,, snother cor Doweet oh Gament are 121 thown in115
Figure 16: This is Case from Textbook. The original text is located [Link].
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Published as a conference paper at ICLR 2026
The Agricultural Revolution
Independent Origins of Agriculture Around the World
Fertile Crescent
Highlands of Guinea
Americas 5,030-3,000 years ago
10,00010,,000 years ugp
9,000-5,000 years ago
Key Developments barley Domestication gaata & sheap
Developments +Cultivation banonce Early droining of werlanda for farming
Key Gevelopments beons, Comesscaaioon lunzs lufbeys
12,000
11,00U
10,000
9,000
8,000
7,000
6,000
5,000
4,000
3,000
(Yeera Ago)
River Valleys of Asia
Sub Saharan Africa 5,000-1000 yeors ego
10,009-9,0000 years ago
Developmente millet Damestication of pips & poulity
Key Dowelomments: -Cutthetion of sorghum yeoe Domestication
Figure 17: Case from Textbook. The original text located [Link].
The MapReduce Framework
Step-by-Step Guide Distributed Pracessing
Phose
Shuffle Group
Phose
Reduce Phose Aggregation
Parthiiiing
Hoppor
Portition
Reduser Mode
Input
Proseosan E300 defon
Final Output
Fouu "top warf Outpet <lbe evenc
Sioca
for each key Ney The"
Lt Aqurepabe vallems
Rozulta
Processes
Oroaped Dets
Ourpet witee
Spuu Tuord har"
Pertition
Reduser Node
prreport
Bize
worl
Mapper Nade
shrref So polno
Procesces
ecch key Kay waod
Itaut chard,
Duppot- clved
Figure 18: This is Case from Textbook. The original text is located [Link].
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Published as a conference paper at ICLR 2026
How Study Macroeconomics: Three-Perspective Approach
1. Macroeconomic Goals
2. Analytical Frameworks
3. Government Policy Tools
Economic Growth
Core Model: Demand/Supply The primuay model asd fluclaaions.
Monetary Policy
Deternines the privilling slandad is cauntry
Who Radir's Certal Back jug.
Rosree) Action:
Prie
Metric change Redl SDM Torget 25 unnual goowth
mase Mirgusaatoos
Co mod money supply
Cxmpples:
Low Unemployment productier and
RRiagg note to light pledion Exsing
Uhenploynatt
Neoclassical
Keynesian
Fiscal Policy
Whr Nuttona Eovernment
Aclera:
Low Inflation
BAACE
Uhonges goverment
Protects the purchesing power peopla's sevings Vugas.
Changes thoee
Metric Coesa Prica hado XOP) Torget: 1-75 arccell
equilibruur. aae.aaaaaa
fectallers
Prioes "Sidk"
-bllurrshrre spanding
groneet udivalts checks
Figure 19: Case from Textbook. The original text located [Link].
Profeesional paper methodology
The Marketing Mix (The 4Ps)
The core tactics used promote products and influence consumer purchasing decisions.
Product
Price
Place
Promotion
The provides.
What the
Comm unication efforts attract the customer.
purchased and cistri bulled.
Exrmpee Tangile Goods Bvarpptine Car Suthvore tpbbcciption
Eumplex:
Eomplex
Excmppex Advertsing Commeniak, Wab
Freed Vokun Towerkn Teclioo Joesunt, Poyment Plaro
Seadl Gtrms, Supeer precerioee Digitll Online Stone, App Store
Saie Public Reistienn Secia/Med
Target Consumer
Influence ace Consumers to Buy
Figure 20: This is Case 10 from Textbook. The original text located [Link].
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Chapter 5: Virtual Machines
1. Virtual Machine (VM)?
Virtual Machine
enviromment reprodud arenitaduur. act as layer of abstraction between software and the Ehysicaliaardeea
provides
2. Main Types
System VM
Process VM (Application VM)
Function: Emulates al hardworr faclites. Allow programs
Function: Environmenc for digle precess. Lartwnrs receende
Langunge
Example Vhlwaans Virturier
Dcampte: Java Viruul Machine (MM)
High-Lack Langunge LLA
3. Case Study: The Java Runtime Process
Source Code Prog
Bytecode
(MMI
Target Machine
Idteracii tnoependien
Tabee Tytriado and porport th raroritte
Duecpna platem-ppacii (EPU CD spect()
Complation
Excction
Preduces
Interoroter
Compleer
Comsiian conccapt
Learning Checkpoint: Explain the primary design goal that led the creaticn of the JVM How docs the JIT compler enhance its perforance?
Figure This is Case Textbook. The original text located [Link].
Visualizing VQAScore: Superior Method Evaluating Text-to-Visual Models BBog-of-worde
Precise, Generctive Evoluction
Port 1: The Chollenge with Text-to-lmage Evaluation
Port Old New Method
Way: "Bog of-words" Problem
Way VOAScore's
Combine Promot
Shared Prompt
TTh brown dig chases the alork dayy
CLIP Hodal
Correct Image
Incerrect (Swopped) Image
Gonerated
Formu lete a Question
There this image show the brown dag cheses lock dog" Plecee crower yos
CONFUSEDI ScorE 55 Score 2.94
Precite grment
Buth dlenticl acones confuse
P(Year)) 0.98
VQAScore 0.93
Port 3 VQAScore's Impoct- - Advontages, Application & Benchmark
Key Advantages
Application: Improving Generation Cundidrter
New Benchmark: GenAi-Eench
Image
Chellenging
to?
ViASooe 331
oir scoiig lroge
Figure 22: This is Case from Blog. The original text is located at [Link].
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Published as a conference paper at ICLR 2026
RELond: A Visual Overview the Landmine Risk Estimation Pipeline
From Row Data to Actionable Demining Plans
LINPUT & DATASET ENHANCEMENT vanporig flems ouere sits liaie,, cervoul Strout
INTERPRE TABLE RISK MODELING
3. ACTIONABLE E CLUSTER IDENTIFICATION
Artuut deep ring mace the korn Invorta in Subaan..
Optiiingg soharies tu centriiatt dumblingg weiiinggonss
Diverse Duta Eourscyy
Goographic
Socia- Demographiss
Integratable IBM Hocal (RELand)
Gready Selection Okefloeed
CIR Clustering (liwer)
War Rarmantt
Historicl Events
SeghhreeHggh Rask Cella
Caleshe Choters
RiAk
Sequarttal Decason Boda
iniratin
Ju
FRAL DEMPING
Figure 23: This Case from Blog. original text is located at [Link].
Visualizing N.Positionalit:: A Framework for Uncovering Al Bias performance can vary dramatically cultural context.
Works Well
Performs Poorly
Scenario Western Context
NLP Toxiaity Model
Scenario B: Moon-Wetern Context
The Posiitionallit Framework: A Two-Ster StepStep Process Quantify Bias
Step Gather Globally Representative Data via Re-annotation
Diverse Annototors
Annotations
Collection
Step Compute Compare Alignment (Pecrson's
r
Colculate For Each Group
Original Model/ Dolonel Lobela
Correkation
Global Annotator Group Lobels
Positionalit Seares
The Result: A Quantified Bias Towards WEIRD Populations
High Alignment (r 0.6)
Low Alignment (r 03)
NLP System
WEIRD Populctions
Marginalized Populstions
Figure 24: This Case 3 from Blog. The original text is located at [Link].
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LLM Watermarking: Vulnerabilities & Defenses
Visuai Guida Common Attazk Vactons and Mitigat on Stratagics
PRobsstness enailees Piggybock Spoofing
Multiple Keys
VULNERABLITYY INTENDED FEATURE
MALEERALLLTH INTENDED FEATURE
VULNERABLITY INTENDED FEATURE
Rebuat
Multiste Secret Kays
Public Detection
Wotermark
THREAFITERATIV
Oulpon
Edit
Gulput
Quen
Dubputt
Outpur
I Atticcior & Variants
2 Find Outpat
MITISATED BY
BITIAAED
MITIGATED BY
GUIDELINE
GUESELINE
GUIDE
Comb ne with Frogil Wenermarks
Limit Rotes
Add Noise Detectior API
Proposed
Proposed Solutton
Clech
Solution
Uendigatuur bussl walooraa oris kllgg by?
Ieploment rote Sidling premaay detoelion,
Use Difforoiiil Privacy edd sebe spores
Figure 25: This Case from Blog. original text is located at [Link].
LLM Training Paradigms: Shifting From 'What' to 'How'
Paradigm Learning Answer
Paradigm 2: Learning Answer
Directly mopping inputs outputs.
Executing strategy using compute.
f
Input Problem
Input Problem (x)
Compune Budget (C)
Adoptive Pollcy Ae(x)
Standard LLM
Therotive Reasoning Process
Training Dota
Objective
1. Alterpt
Verily
Refine
Final Answer (y)
Findl Reasonec Output
Finel Answer (V)
3.
Limitot on Fails novel complex problems.
Advantage: Generofizes and harder problems.
Figure 26: This Case 5 from Blog. The original text is located at [Link].
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Koala: A Visual Guide to High-Quality Data Fine-Tuning
1. Foundation
2. Quahlityyytyy Dotoset Curotion
3. Fine-Tuning
4. Result
5. Evaluation
Cf d1PT
Koals Alpaca
LLaM4 Baso Model
Treining Process
Kaalo 138 Mode!
Dialogues
Hours
Open
Preferre 770%
Anthropic
Ties wind 54%
Key Insight: Quality Trumps Quantity
This high quality disilleed mocel alightly outperformed the model trained all data sources combined.
Figure This Case from Blog. original text is located at [Link].
Strategies for Constructing Generative Vision Agent (GVA)
A Texonomy of Agent Training and Interaction Paradigms
Adaptian Strotegy
2. Fine-tuning Strotegy (uuin and Sytettshort
(Mo Hondl Truiing Requrvel
GVA Core
Dart Concept. Adagtable Ageit ligg ledeodt Sociads gytee
Core Concrptt Speciabee Model an grocdde
near
I--uulllly Focus
Reinforcement Learning
Proinpping
Feeebeok
Marery
Phaano Bhinn Lsent
Hagn now nol sudte bonel
Cor Cancegé Triel and Broor Loanning
memelt cvellee Foutime sedtoling an rilire
Cooperation Competition
rrabllpom Intnectert
Babching debiiid
Bobiiing princiigg deticee
Denogtt Agent pepeeteeneeee receened Prudeo
Kay Chip rockeii
aodk.
woree fer
Prough wit- devell
wcarbrikor
O deploty vini prgvemetr wodth argiee
rielic
Characlrrsi cz
C lubii ddllees ticulie can'dl bealed perceett lering
( Hoddau Arg Ood uhee goll vialy audia
Nour Norreas dalicie Pank ad bkass delogh calactia Artt grean
Figure 28: This is Case I from Survey. The original text is located [Link].
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Collaboration Types in LLM-based Multi-Agent Systems
4.2.1 Cooperation
Agsits bepeher compan revuure pubilliee sco0s colletive
4,2.2 Competition
pais sasidll oreiher lem (ret pria compen
Agent
Agendt
2
Agunt
Advantages
Challanges
Advantages
Challenges
sanwiegg coloo padio's foden
www.iriinnoeee crnplenly
Powils sloi peltomccac koarrr arts
RESSMEER
Limited
Shared Gos! CAVEL
Meta
Eargles LLMAREMA LEG0 Expláior Cet Debato
Coopetition
Anel www.erdaaaaaaaaeoooooo cum
Se Agin compttoon
Hybrid Colloboration
Sa ua Jilund in insiing
Debater
Debeter
Advantages gas proud reditiealaa' dyann undcolleciv progres
Chullenges
Adventages
Challengss
yorppl vosile tage abucran
ted Dan ochecine cun rers
Unsdgnng lae eflu
Shared
Judge
Jdli's zea oar ingll per
Exueplos: Voture di Bxssts Neak Nogolct #1 Scanaiks
Examplos LEG0 (muul slagel Anieed Debale
Figure This Case 2 from Survey, original located at [Link].
A Conceptual Framework for Embodied AI
integrated view agents perceiving, acting, and adopting through physical intersction
Foundational Pillars
Core Dynamics
Tangible Outcomes
Ropted in physical
ogente perceive
and the world
Perception Accurres dito
Cognition
Obeere Physisa Forme
Reasone
2. Interactivity sca senergaa
Robotics
Autonomous Driving
elosed intaroztion.
Madii Sanves
Endi conmeeeet
Embodied Agent
Intelligence Improvement
growth cogrition betovior, soredign..
Action
Executes via
Co-Cirll adasery Furedige
Intelligent Mig.
Historical Foundations
Sedins
Dudilies
Endissmmntt
1850s Toring's Propoatiin
1980s Cognition
2020g= Geneentive
Figure 30: This is Case 3 from Survey. The original text is located at [Link].
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Roadmap to Unified Omnidirectional Model (PANORAMA System) Staged Approach to Advance Embodied Al with 360 Vision
Phase I: Data & Sensor Foundation
Phase Il: Model Development & Pretraining
Phase IlI Validation & Deployment
Stage Integration
Stage Reasoning
Stage Evaluation Benchmarking
fay Adiien: le uer Jisk win creetiet libes
Key Actiona
Key Antumm
Uhe LUe a seralrden sale
Venuo r1o prcacion cretrwar
bumplec:: Cakmmu
Mebror
Insaud hallwing Morgetton Gouping
arrant, Leiero
Stage Modal Exparsiio
Stage Unified Pretraining
Stage Deplayment Generalization
Kry Attiat Omik polks mall servo cappns
Actians
Bny Astlomo:
lic wilk olecctic booo
Taelei furee andoon iupvrtatiion cantaal
HS8 Luis Aatis,
Eralo our adaiiiiion
Figure This Case 4 from Survey, The original located at [Link].
38


Reviews:
[{"review_id": "kW2D1AxDD9", "reviewer": "Reviewer_Sgpo", "summary": "This paper studies the task of generating scientific diagrams directly from long scientific text. The idea is to transform complex scientific context (texts) into a figure that communicates the core concepts visually. \n\nThe authors introduce a new benchmark called FigureBench, with 3.3K high quality text to scientific figure pairs. They also propose AutoFigure, an agent style system that first plans a symbolic layout and then renders it. \n\nExperiments show that AutoFigure outperforms previous methods on their benchmark, using both VLM as judge metrics and human expert evaluation.", "questions": "1. (Comment for improvement) In Figure 1, the visual framing could be confusing. At first glance it gives the impression that the authors are training the model with the InstructGPT pipeline, rather than generating a figure of the InstructGPT pipeline. The caption could clarify this more explicitly so that the intent is obvious to the reader.\n\n\n2. The generated figures look significantly better than previous works, but they still do not look ready for professional use in a camera ready paper. What do the authors believe are the main remaining blockers to achieving near perfect results, and which components of the current system will need to be improved most to close this gap?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The paper introduces a modern benchmark for scientific illustration generation that includes diverse long text to figure pairs from papers, surveys, blogs and textbooks. The authors also provide dataset statistics and analysis showing the challenge of long context reasoning.\n\n2. The method uses an agent based pipeline that first grounds concepts with a VLM to produce a symbolic layout, then performs iterative refinement to improve structure, and finally renders the figure. This decoupled design is well motivated and appears to be effective.\n\n3. The paper clearly states why FID is not well aligned to this task and instead uses VLM as judge evaluation combined with human expert assessment. This evaluation choice makes sense for this application.\n\n4. The approach does not require training. It leverages frontier foundation models to achieve strong results.", "weaknesses": "1. The comparison to prior datasets is incomplete. Paper2Fig100k [1] dataset is not mentioned or cited, and Paper2Fig100k already contains more than 100k text to figure pairs. The claim that FigureBench is the first large scale benchmark is therefore not correct, and should be reframed more precisely.\n\n\n2. There is no reference to recent TiKZ based diagram generation approaches such as Automatikz [2], which are directly relevant to the diagram synthesis space.\n\n\n4. The design of the VLM as judge metric is not described in sufficient detail. Ideally the authors should provide a validation study that justifies the choice of prompts, models, and scoring dimensions, and should provide a correlation analysis between VLM scoring and human scoring.\n\n\n5. The system pipeline is quite complex. While the ablations help, the number of components shown in Figure 3 suggests that more systematic ablations would be useful to better understand which parts contribute most.\n\n\n6. The paper does not report efficiency metrics. It would be important to know typical generation time, how this scales with text length and concept complexity, and the approximate economic cost of running this agent per figure.\n\n\n7. The experiments rely mainly on GPT Image and Gemini 2.5 Pro as backbones. To strengthen the experiments section it would be helpful to evaluate with more alternative LLM/VLM and image generation models.\n\n8. The set of baselines is limited. There is no comparison to other agentic scientific content generation systems such as Paper2Poster or PPTAgent. A deeper analysis of which methods are closest in terms of workflow and whether they can be run on FigureBench would make the empirical comparison more convincing.\n\n[1] Rodriguez, Juan A., et al. \"Ocr-vqgan: Taming text-within-image generation.\" Proceedings of the IEEE/CVF winter conference on applications of computer vision. 2023.\n\n[2] Belouadi, Jonas, Anne Lauscher, and Steffen Eger. \"Automatikz: Text-guided synthesis of scientific vector graphics with tikz.\" arXiv preprint arXiv:2310.00367 (2023).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "XEFAbuCHD4", "reviewer": "Reviewer_xScc", "summary": "The authors propose AutoFigure, a method that takes text as input such as and outputs a figure that reflects the entities and the relationships between them, much like a pipeline or a control flow that appears in papers to explain the procedure. \n\nThe method first uses concept extraction to break down the textual input into a structured output which then gets refined using vlms via iterative feedback, which then passes through an aesthetic synthesis process to beautify it and render it.\n\nThe authors ran experiments that evaluate how good the generated figures are based on aesthetics, content, and fidelity. They used human evaluation to assess whether the synthetically generated figures are of high enough quality to be included in a camera-ready paper.\n\nThey also create a 3,300-sample FigureBench dataset from AutoFigure to evaluate thier system.", "questions": "Did you generate your figures in this paper using AutoFigure itself? If not, why not? This would be the most direct way to demonstrate the system’s real-life appliction.\n\nWhere are the quantitative and qualitative results that show the usefulness of the \"erase-and-correct\" strategy? Please include a clear comparison (with and without this step) so readers can see what it improves and by how much.\n\nWhere are the actual prompts used for each stage (concept extraction, critique and refine, rendering, validation)? Showing a few concrete examples would help readers understand how the LLMs were ran and what inputs they receive.\n\nWhere does this method fail? Please include examples or categories of failure cases (for instance, overly complex text, ambiguous relationships, or dense mathematical descriptions). .\n\nHow does this method perform when using open-source models like LLaMA or Mistral instead of commercial ones such as Gemini, Claude, or GPT? It would be important to know if AutoFigure can function well with open-source models.\n\nIs the LLM used to generate the first layout (So, A0) the same as the one used in the subsequent refinement iterations? If not, please clarify which models are used at each stage and why.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper addresses a very impactful task related to figure generation, which is important in many different industries and research domains.\n\nThe authors perform a good and comprehensive human and automatic evaluation with several strong baselines.\n\nThe paper is well-written and explains the problem in a clear and complete way.\n\nThe authors include a detailed human evaluation setup, which is a good way to test whether the AutoFigure generation is actually good. They recruited 10 human experts to evaluate AI-generated figures from their own first-author papers. The evaluation included three tasks:\n(1) Multi-dimensional scoring, Forced-choice ranking against human-created figures, and  (3) Publication intent selection, asking if the generated figure is good enough to be included in a camera-ready paper.", "weaknesses": "No open-source models were tested (it only mentions Gemini, Grok, Claude, and GPT). It is important to include methods that work well with open-source LLMs so the research can be reproduced with minimal cost and used more broadly.\n\nA lot of methodology details are missing. For example, when the paper says \"identifying key entities and their relationships, and distilling a core methodology summary,\" it is not explained how this is actually done as no system prompt nor user prompt are shown.\n\nUsing an LLM as a judge is risky because it is not clear how they ensure it does not produce biased or inconsistent evaluations. The LLM itself needs to be tuned and evaluated. Does the results change when ran multiple times? There is a lot of bias risk even for the blind pairwise comparison part. There should be a human evaluation for all the metrics (\"Visual Design,\" \"Communication Effectiveness,\" \"Content Fidelity,\" and \"Blind Pairwise Comparison\") to make sure they align with the VLM’s scores. A correlation or alignment study between human and model judgments would make this more reliable.\n\nThe method uses many LLMs in different steps, so the cost of running the system must be very high, especially since all the models are commercial. Functions like Φprompt and Φerase should be clearly stated as either LLM-based or non-LLM components. The authors should include a pareto plot showing the trade-off between cost and performance across different LLMs so we could know what to choose under a fixed budget.\n\nThere is no ablation study showing why the \"erase-and-correct\" strategy is necessary or how much it improves the results.\n\nThere are no code or prompt descriptions for the LLMs used. The paper mentions several Φ functions and agents, but it does not show what the actual prompts or inputs look like.\n\nThe method is long and has many components, but the exact implementation details are unclear. The paper mentions the steps, but it does not show how each stage (concept extraction, refinement, validation, rendering) is actually carried out.\n\nThere are not enough qualitative examples of generated figures in the paper. The examples shown are very few. It would help to share a link with a larger gallery of generated figures (say a few hundred) to confirm that the figures are not overly simplistic and truly handle dense, multi-component scientific texts.\n\nThe results have no error bars, so it is not possible to know whether the performance differences between models are statistically significant.\n\n\nThe blind pairwise comparison setup is limited because it only allows for \"A, B, or Tie.\" It should also have options like \"both good\" or \"both bad,\" so we can tell how many figures are actually poor and would be discarded.\n\nFigure 3 is poorly structured and hard to match with the text in Section 4.  It would be better if each part of the figure was labeled with the corresponding stage headers (for exmple \"Concept Extraction,\" \"Critique and Refine\") exactly as they appear in the text. Keeping the same wording between the figure and the text would make it much easier for readers to follow and it is a very good practice.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7XK2H5dJMd", "reviewer": "Reviewer_LXSZ", "summary": "The paper introduces FigureBench, the first large-scale benchmark for generating scientific illustrations from long-form scientific texts. FigureBench comprises 3,300 high-quality text–figure pairs, encompassing a wide range of text-to-illustration tasks. In addition, the authors propose AutoFigure, an agentic framework that automatically produces high-quality scientific illustrations from long-form scientific descriptions. AutoFigure operates in three stages: conceptual grounding, iterative self-refinement, and erase-and-correct rendering. Automatic evaluations based on the VLM-as-a-judge paradigm and human expert assessments demonstrate that AutoFigure is capable of generating illustrations that are scientifically rigorous and aesthetically appealing.", "questions": "See the weaknesses", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- This paper represents an important early step toward exploring how AI can assist humans in the time-consuming process of scientific illustration creation. The topic is interesting and promising, with significant potential for advancing AI-assisted scientific communication.\n\n- AutoFigure is designed as a three-stage framework, where each stage addresses distinct challenges in the illustration generation process. These stages work in synergy, resembling how humans iteratively refine scientific figures — for instance, through the Critique-and-Refine step and the Erase-and-Correct strategy.\n\n- The model demonstrates quantitatively superior performance in most experiments, indicating its capability in generating high-quality scientific illustrations.", "weaknesses": "- In Figure 1, AutoFigure generates a scientific illustration for InstructGPT. However, the original InstructGPT paper does not mention any examples related to relativity, suggesting that AutoFigure may have extended the content beyond the source text. Moreover, there is an error in the generated example (“ravity” instead of “gravity”), indicating that in some cases, the framework may pay more attention to aesthetic appeal than scientific accuracy.\n\n- As mentioned in the paper, scientific illustrations are meant to help readers grasp the main ideas quickly and avoid misinterpretation. However, in Figure 4, the diagrams appear confusing — the corresponding subfigures are not well aligned (e.g., Step 2 in the Offline Phase), and the textual elements are cluttered and disorganized. In fact, an effective scientific illustration should focus on clarity and accuracy of information, rather than emphasizing visual decoration such as color or stylistic patterns.\n\n- The paper mentions a process of fine-tuning a large language model (LLM) using human-selected text–figure pairs. However, the details of this process remain unclear — specifically, like which LLM was used and what the input–output format of the fine-tuning procedure was. Moreover, given that the dataset contains only 300 samples, it raises concerns about whether such fine-tuning could improve the quality of the paper selected.\n\n- For the Style-Guided Aesthetic Rendering process, AutoFigure employs style description text to guide the rendering. However, as shown in Figure 4, the illustrations generated from diverse academic texts appear visually similar, often consisting of sub-block structures, similar color schemes and patterns. This raises concerns about whether the style cues truly exert a meaningful guiding influence on the rendering results.\n\n- Some details of this work are not clearly illustrated:\n(a) What are the prompts used for all the LLMs used in this work?\n(b) The VLM evaluates the generated images across three dimensions with eight sub-metrics, but these metrics are not explicitly defined or described in the paper.\n\n- Regarding the human evaluation, only 10 participants were involved, and they assessed merely 21 papers in total. Such a limited sample size may introduce considerable bias and undermine the reliability of the evaluation results.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "VGAgqTluii", "reviewer": "Reviewer_WvcZ", "summary": "The authors introduce the task of generating scientific figures from long-form text (e.g., the entire text of a paper). To do this, they introduce AutoFigure, an end-to-end model (or possibly a pipeline of models) that generates figures from long-form text. To evaluate their model, they also introduce a new dataset of long-form text paired with corresponding scientific figures. Their approach outperforms various baselines. The dataset also has a development split.", "questions": "I have no questions for the authors.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "* The authors tackle a relevant and challenging problem.\n* The paper is mostly easy to follow.\n* The created dataset, if released, may be useful for future work.", "weaknesses": "* It is not entirely clear to me what the authors understand by long-form text. It seems like they mean the entire text from a paper. But pairing a whole paper with just one figure extracted from it does not seem like a well-defined problem to me, as usually no figure captures the whole content of a paper.\n* Datasets that pair figures with captions and all mentioning paragraphs can also arguably be classified as containing long-form text (ACL-Fig [1], SciCap+ [2], etc.), but these are not discussed in the paper even though they are very relevant.\n* There is also a lot of other relevant previous work on scientific figure generation which the authors do not cite [3, 4, 5, 6]. Instead, they falsely claim that existing work on automated scientific visuals primarily explores the generation of artifacts like posters and slides (l.113).\n* I do not understand how the AutoFigure model works internally. Figure 1 mentions InstructGPT but doesn't mention how that relates to this work in any way. Still, it makes it seem like AutoFigure consists of a single instruction-tuned model which they train themselves (even though the instruction-tuning examples inside the figure are unrelated to the task at hand). But nowhere in the paper is a training process described. Instead, Sections 4.1 and 4.2 make it seem more likely that AutoFigure consists of a model pipeline of prompted VLMs and text-to-image models. The authors should be clearer about how their model, a core contribution of this work, actually works.\n* Furthermore, the fact that some of the baseline models mentioned in Figure 1 are actual model names while others are markup languages further contributes to confusion.\n* Some claims in the paper like \"Another line of work employs executable code as an intermediate state between scientific text and illustration, resulting in relatively visually unappealing diagrams\" (l.71) and \"This shift is evidenced by the growing acceptance of AI-generated papers at venues like the ICLR 2025 workshop and ACL 2025\" (l.135) are not backed by any data or citations and make it unclear how the authors arrived at such conclusions. \n* The authors advertise the development split of their dataset but do not mention how it is used or how it can be used.\n* In summary, I believe that the paper requires further justification (is the tackled task actually useful?) or adjusting the problem, and needs much more polished writing before it can be accepted.\n\n[1]: [ACL-Fig: A Dataset for Scientific Figure Classification](https://ceur-ws.org/Vol-3656/paper2.pdf)  \n[2]: [SciCap+: A Knowledge Augmented Dataset to Study the Challenges of Scientific Figure Captioning](https://ceur-ws.org/Vol-3656/paper13.pdf)  \n[3]: [Text-Guided Synthesis of Scientific Vector Graphics with TikZ](https://openreview.net/pdf?id=v3K5TVP8kZ)  \n[4]: [DeTikZify: Synthesizing Graphics Programs for Scientific Figures and Sketches with TikZ](https://proceedings.neurips.cc/paper_files/paper/2024/file/9a8d52eb05eb7b13f54b3d9eada667b7-Paper-Conference.pdf)  \n[5]: [TikZero: Zero-Shot Text-Guided Graphics Program Synthesis](https://openaccess.thecvf.com/content/ICCV2025/papers/Belouadi_TikZero_Zero-Shot_Text-Guided_Graphics_Program_Synthesis_ICCV_2025_paper.pdf)  \n[6]: [Learning to Infer Graphics Programs from Hand-Drawn Images](https://papers.nips.cc/paper_files/paper/2018/file/6788076842014c83cedadbe6b0ba0314-Paper.pdf)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8Jj9iNtvU7", "reviewer": "Reviewer_P6k4", "summary": "The paper introduces **AutoFigure**, a two-stage “reasoned rendering” pipeline that converts long scientific text into publication-ready illustrations, and presents **FigureBench** (3,300 text–figure pairs) with VLM- and human-based evaluations. AutoFigure decouples layout reasoning from aesthetic rendering and adds an OCR-based erase-and-correct step, outperforming baseline T2I, code-generation, and multi-agent methods. \n\nHowever, clarity and reproducibility are limited by broad gaps in reproducibility and transparency: key terms are undefined (e.g., IRR); InternVL-3.5–based statistics lack human-calibrated validation; generated-vs-reference statistical comparisons and full prompt/iteration transcripts are missing; analysis of the lower win rate on the Paper domain is limited; style controllability is unclear; compute/latency/cost reporting is absent; and the dataset release plan remains ambiguous.", "questions": "See weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 4, "strengths": "1. The paper delivers high-quality illustrations.  \n2. The methodology is clear and technically sound.  \n3. The problem addressed is inspiring, timely, and highly needed by the research community.", "weaknesses": "1. **IRR is used but not clearly defined.**  \n   - *line 90* Please expand IRR on first use (e.g., *Inter-Rater Reliability*) and specify the exact statistic.\n\n2. **Dataset Analysis relies on InternVL-3.5 without validating its accuracy.**  \n   - *line 208* You state that “all statistics are analyzed using InternVL-3.5,” but do not quantify its error on these measurements (text density, colors, components, shapes). If feasible, include a small human-audited benchmark to sanity-check these automated stats.\n\n3. **Missing comparison of statistics between AutoFigure outputs and original figures.**  \n   - Please add a side-by-side analysis (distributions and summary stats) of *generated vs. reference* figures for key metrics (text density, components, shapes, color count etc.), along with effect sizes and significance tests.\n\n4. **No concrete prompt examples for each stage/iteration.**  \n   - Include representative prompts for every stage and show how does the iteration help improve the prompts.\n   - Sharing a minimal working example in the appendix would aid reproducibility.\n\n5. **Paper category has notably lower win rates; insufficient error analysis.**  \n   - Provide a qualitative and quantitative breakdown of why “Paper” is harder.\n\n6. **Style uniformity across generated figures.**  \n   - Many results share a similar visual/“avatar” style. Clarify whether this is a bias of the rendering LLM/T2I model, default style prompts, or intentional curation. Demonstrate **style controllability** (e.g., minimal changes in the style descriptor yielding materially different aesthetics) and include a diversity study (multiple styles for the same layout).\n\n7. **Compute, latency, and token usage not reported.**  \n   - Please report average **tokens/time per figure** for each stage, number of refinement iterations, hardware, parallelism, and **cost** estimates. A throughput vs. quality curve (iterations N vs. score) would help practitioners plan budgets.\n\n8. **FigureBench availability unclear.**  \n   - Clarify whether the dataset is fully prepared for release and provide an access link or timeline.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)