P2P: Automated Paper-to-Poster Generation and Fine-Grained Benchmark
Authors: Tao Sun
Enhao Pan
Zhengkai Yang
Kaixin Sui
Jiajun Shi
Xianfu Cheng
Tongliang Li
Ge Zhang
Wenhao Huang
Jian Yang
Zhoujun Li
Venue: ICLR 2026 Poster (2026)
Keywords: Poster Generate
LLM-as-a-Judge
Multi Agent
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We present P2P, a LLM-based multi-agent framework that turns research papers into polished HTML posters, backed by a 30k-example instruction dataset and establish a fine-grained benchmark for rigorous evaluation.

Abstract: Academic posters are vital for scholarly communication, yet their manual creation is time-consuming. However, automated academic poster generation faces significant challenges in preserving intricate scientific details and achieving effective visual-textual integration.
Existing approaches often struggle with semantic richness, structural nuances, and lack standardized benchmarks for evaluating generated academic posters comprehensively. 
To address these limitations, we introduce P2P, the first flexible, LLM-based multi-agent framework that generates high-quality, HTML-rendered academic posters directly from research papers. P2P employs three specialized agents—for visual element processing, content generation, and final poster assembly—each integrated with dedicated checker modules to enable iterative refinement and ensure output quality. 
To foster advancements and rigorous evaluation in this domain, we argue that generated posters must be assessed from two complementary perspectives: objective fidelity and subjective quality. 
So we establish P2Peval, a comprehensive benchmark featuring 1738 checklist items and a dual evaluation methodology (Fine-Grained and Universal). 
Our Fine-Grained Evaluation uses human-annotated checklists to objectively measure the faithful preservation of verifiable content from the source paper. Concurrently, our Universal Evaluation captures subjective, holistic quality by training a model to align with human aesthetic preferences across key design principles. We evaluate a total of 35 models.
To power these advancements, we also release P2Pinstruct, the first large-scale instruction dataset comprising over 30,000 high-quality examples tailored for the academic paper-to-poster generation task. Furthermore,  our contributions aim to streamline research dissemination while offering a principled blueprint for evaluating complex, creative AI-generated artifacts. 
The code is on the GitHub, https://github.com/multimodal-art-projection/P2P.

Published as a conference paper at ICLR 2026
P2P: AUTOMATED PAPER-TO-POSTER GENERATION AND FINE-GRAINES BENCHMARK
Tao Sun' 12, Enhao Pan', Zhengkai Yang', Kaixin Sui', Jiajun Shi', Xianfu Cheng?, Tongliang Li", Wenhao Huang', Ge Zhang"? Jian Yang." Zhoujun Li
Shenzhen Intelligent Strong Technology Co.,Ltd. 4 College of Computer Science, Beijing Information Science and Technology University (buaast, jiaya, lizj] @buaa edu.
ByteDance, China 2 M-A-P
ABSTRACT
Academic posters are vital for scholarly communication, yet their manual creation is -ime-conuuming However, automated academic poster generation faces signif- icant challenges in preserving intricate scientific details and achieving effective visual-textual integration. Existing approaches often struggle with semantic rich- ness, structural nuances, and lack standardized benchmarks for evaluating generated academic posters comprehensively. To address these limitations, introduce P2P, the first flexible, LLM-based multi-agent framework that generates high-quality, HTML-rendered academic posters directly from research papers. P2P employs three specialized agents-for for visual element processing, content generation, and final poster assembly each integrated with dedicated checker modules to enable iterative refinement and ensure output quality. To foster advancements and rigorous evaluation this domain, we argue that generated posters must be assessed from two complementary perspectives: objective fidelity and subjective quality. So we establish P2PEVAL, a comprehensive benchmark featuring 1738 checklist items and dual evaluation methodology (Fine- Grane-Grinned and Universal). Our Fine- Grained Evaluation uses human-annotate checklists to objectively measure the faithful preservation of verifiable content from the source paper. Concurrently, our Universal Evaluation captures subjective, holistic quality by training model to align with human aesthetic preferences across key design principles. We evaluate a total of 35 models. To power these advancements, we also release P2PINSTRUCT, the first large-scale instruction dataset comprising over 30,000 high-quality exam- ples tailored for the academic paper-to- tosttterr generation task. Furthermore, our contributions aim streamline research dissemination while offering principled blueprint for evaluating complex, creative Al-generated artifacts. The code is on the https: //tpth// com/ multtuttttttt imodal art projection/P22
INTRODUCTION
Academic posters serve as a vital tool in scholarly communication, distilling complex research into visually accessible formats to foster knowledge dissemination. However, manually creating these posters is time-consuming and skill-intensive process, demanding blend of content refinement and design proficiency. Automating academic poster generation thus offers a significant opportunity to streamline research dissemination, but it is complex challenge that goes far beyond simple summarization or image placement.
The difficulty is twofold. First, the generation task itself requires system simultaneously master multiple sub-tasks (Qiang et al., 2016): (1) content distillation to identify and synthesize the most critical information; (2) visual textual integration understand and contextually place figures and tables; and (3) structural reorganization to transform a linear document into a coherent, two- dimensional layout. Second, and equally challenging, is evaluation. poster's quality is difficult to assess directly, as it requires balancing two distinct axes: objective fidelity and subjective quality.
"Corresponding Authors

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Published as a conference paper at ICLR 2026
Objective fidelity, the faithful preservation of scientific claims and data, can be verified against the source paper. Conversely, subjective quality, which encompasses layout coherence and visual appeal, is inherently matter of human aesthetic judgment. A robust evaluation must therefore address both (Que et al., 2024; Viswanathan et al., 2025).
Existing approaches to poster generation primarily rely on template-based rule-driven methods (Xu & Wan, ). which often struggle to capture the semantic richness and structural nuances of academic documents (Qiang al., 2019), typically decomposing the task into isolated subtasks like content extraction (Cheng et al., 2024b), panel attribute inference (Huang et al., 2022), and layout generation (Lin et al., 2024). Although recent advances in multimodal large language models (MLLMs) and large language models (LLMs) show promise in understanding document structures and visual-textual relationships (Jaisankar al., 2024), their application to academic poster generation remains limited due to insufficient quality control mechanisms and the absence of standardized benchmarks for systematic evaluation.
To overcome these limitations and explore underexplored research direction, we introduce foun- dational ecosystem for automated academic poster generation, built on three synergistic contributions. The first is P2P, a novel and flexible multi-agent framework. It employs three specialized agents for visual processing, content generation, and layout orchestration, each integrated with a checker reflection loop. This iterative refinement mechanism mimics the human design process of drafting and revision, ensuring both scientific accuracy and structural integrity. The second contribution is P2PINSTRUCT, the first large-scale (30,000+ examples) instruction dataset for this underexplored domain, designed to train end-to-end models. The third is P2PEVAL, a comprehensive benchmark featuring 1738 checklist items, which implements our dual evaluation philosophy. Fine-Gras Graine Evaluation measures objective fidelity by using LLM-as-a-Judge (Gu et al., 2024) to score generated posters against detailed, human -nnotated checklists of verifiable content. While its Universal Evaluation captures subjective quality by training XGBoost (Chen & Guestrin, 2016) to emulate human annotators' holistic scores across universal design and content criteria. We evaluate a total of 35 LLMs and MILMss
Our contributions are as follows:
We propose P2P, the first multi-agent architecture for academic poster generation featuring an innovative checker-reflection mechanism for iterative refinement, providing robust design pattern for complex document transformation tasks.
We construct and release P2PINSTRUCT, the first large-scale (30K+) instruction dataset specifically designed to train models for the complete paper-to-pe to-poster generation workflow.
We establish PALEVAL, a new and comprehensive benchmark (1738 checklist items and 121 pairs) with novel dual- -vallatatttionn framework that integrates and predictive scoring model for robust, multifaceted analysis of poster quality.
checklists
METHODOLOGY
Our method, P2P, is multi-agent framework that automates making posters. As model-agnostic orchestration pipeline, it can be instantiated with various LLMs. It breaks down the complex job into small, manageable steps, with specialized agent handling each one. A core innovation is the integration of a checker-reflection mechanism at each stage, enabling iterative refinement and ensuring high-quality output. The robustness of P2P also facilitates the creation of P2PINSTRUCT, large-scale instruction dataset derived from the intermediate outputs of P2P.
2.1 P2P: MULTI-AGENT FOR PAPER-TE SEERR-TO-PORRE POSTER GENERATION
As illustrated Figure 1, the P2P workflow is orchestrated by three collaborative agents: the Figure Agent, the Section Agent, and the Orchestrate Agent. Each agent operates in conjunction with dedicated checker module that triggers a reflection loop if its output fails to meet quality standards.
Problem Formulation. Given research paper D in digital format (e.g., PDF), the task of P2P generation is to automatically synthesize an academic poster P in a web-native format (HTML and CSS). We formalize this task as sequential operation orchestrated by our specialized agents.
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Section Agent
Section Checker
Universal Score
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Figure 1: The multi agent architecture of P2P: papers are processed by the Figure Agent for extraction and description visual elements, the Section Agent for structural and content generation, and the Orchestrate Agent for poster assembly and HTML rendering. Each agent employs checker modules and reflection loops for iterative enhancement.
The overall process can be expressed as: P Aocs F), where F = Arig( (D). Here, the process begins with the Figure Agent, ArG. which processes the input paper D) to extract and describe its visual elements, producing an intermediate set of figures and tables F. Subsequently, Asee F) by summarizing the
the Section Agent, Asec generates the poster content P. poster paper D while strategically referencing the visuals in F. Finally, the Orchestrate Agent, Aorch assembles the textual content P posterr_txxt text and visual elements F into the final designed poster P.
Figure Agent. The Figure Agent is responsible for processing all visual elements within the input re- search paper. Its Figure Extractor component employs DocLayout- YOLO (Zhao et al., 2024), state- of-the-art document layout detection model, to extract figures and tables. Concurrently, the Figure De- scriber identifies corresponding captions via spatial relation analysis. These components collaborate to synthesize semantic visual units by combining each extracted graphical component with its associ- ated caption, yielding
of described visual elements Fa = ((v1, Ci desc.), (Uns Cn desc,)) ) Here, Y denotes the raw visual element (the cropped image file and its metadata), e its original caption, and desc; a detailed description generated by an MILM, figure. The Figure Checker then validates this output by: (1) preventing duplicate extractions, (2) verifying the capture of all significant visual elements, and (3) confirming accurate visual-caption pairings. To ensure reliable pairings, an initial confidence threshold is applied to detected elements; this threshold is incrementally lowered if discrepancies arise between the counts of identified figures and captions, an iterative process repeated until sufficient alignment is achieved.
Section Agent. The Section Agent focuses generating the textual content of the poster. Initially, the Section Generator analyses the input paper (D) to dynamically infer a detailed structural schema (S) for the target poster. This schema, represented as JSON object, delineates crucial sections (e.g.. Introduction, Methods, Results) and their intended content focus. Subsequently, the Content Generator synthesizes semantically coherent textual content for the poster, P Pposterietts by utilizing the structural schema S, the original input paper D. and the detailed descriptions and indices of visual elements Fa provided by the Figure Agent. This textual content generation can be formally described as: Pposter_ies, texi Mesu D, S.F)). where Mies is a LLM specialized in text generation. Miext employs prompts not only to generate text but also to strategically integrate Markdown-style references to figure indices from Fa at optimal textual positions, ensuring contextual relevance and visual-textual alignment. The Section Checker scrutinizes the generated Pposter_oor for: (1) coherence and logical flow, (2) completeness in covering paper's ' findings, and (4) correct and relevant referencing of visual elements. inadequacies are detected, reflection loop initiates revision of the section structure content by the Section Agent.
contributions, (3) faithfulness to the original
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ar iv
Paper
Poster
Figure 2: An example of the raper-to-poster transformation achieved by P2P, showing direct correspondences between elements in the imput paper (left) and the generated academic poster (right).
Orchestrate Agent. The Orchestrate Agent integrates the visual and textual components into cohesive and professionally formatted poster. The HTML Generator utilizes the Markdown-formatted text Ppowerr text from the Section Agent and the actual visual elements (images/tables Fu., where each figure is additionally provided with its width, height, and aspect ratio supplementary information) extracted by the Figure Agent, to produce the poster in HTML and CSS. The Orchestrate Agent deliberately omits original captions from Fa in the final embedded visuals to improve visual clarity and maintain a concise academic presentation. The rendering process adheres to three principles: (1) Strrent-Structure Decoupling: Decouple semantics from presentation via modular CSS. (2) Institutional Identity Alignment: Customize color schemes to align with the logo of the institution or conference. (3) Responsive and Balanced Layout Generation: Use CSS flexbox for adaptive column structures and whitespace optimization. The Poster Checker evaluates the rendered poster for layout aesthetics and structural integrity, triggering iterative adjustments (via reflection) to resolve issues like unbalanced spacing misaligned elements until the design meets professional standards.
Figure 2 illustrates the core transformation process facilitated by P2P. On the left, multi-page academic research paper, sourced from repositories such as arXiv or conference proceedings like ICLR, serves as the input. On the right, the corresponding academic poster, generated by P2P, is displayed. The red arrows explicitly map key elements from the original paper, such the title, specific figures, and sections, to their respective locations and representations in the final poster.
2.2 P2PINSTRUCT: A LARGE-SCALE INSTRUCTION DATASET
The P2PINSTRUCT dataset is derived from the P2P support training of models for poster generation. Following P2P, we collect 30,460 high-quality instruction-response pairs spanning the complete poster generation workflow. For visual element processing, we prompt Claude generate 16,848 figure-deseription pairs through the Figure Describer component, yielding descriptive texts averaging 192 tokens per visual element. For textual content generation, collect 13,612 instruction-respons, pairs from the Section Generator, Content Generator, and HTML Generator components. These examples average over 3,300 tokens per response, demonstrating the complexity and richness of the generated content. A detailed account of its generation process, quality validation, and mitigation of potential biases is provided in Appendix D.
P2PEVAL: A FINE-GRAINED BENCHMARK FOR POSTER EVALUATION
As shown in Fig 3, present benchmark called P2PEVAI for evaluating academic posters.
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Evaluation Criteria
Eval Results MLLM: M
Up Uniqueness
U:: Balasced
Space
Universal Checklist
MILLMMasooJudge
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Usol Xu
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Score Human:
Universul Score
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(0-90)
Fine-Grained Score
Human Amnotated
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Checklit Mot Soore: Ma
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Figure 3:
Overview of the poster evaluation framework used in P2PEVAL. P2PEVAL includes Universal and Fine-Grained Poster Evaluation, human annotators, and XGBoost for scoring.
3.1 BENCHIARK CONSTRUCTING
Checklist Design. The core of P2PEVAL lies in its detailed, human-curated and fine- graiggaindd checklists, designed to capture the essential elements of a poster. The annotation focuses on creating detailed checklists using official posters ground truth. Our checklist design incorporates the following elements: (1) Visual Elements: Each figure or table present in the official poster constitutes an individual checklist item, evaluated based upon its presence and accurate representation. (2) Content Analysis: Each visual element is assessed regarding its textual consistency with the original poster and its visual prominence within the poster layout. (3) Structural Components: Annotators identify critical sections such as task definitions, experimental methodologies, and research conclusions within each poster panel. (4) Research Emphasis: Essential research findings, methodological details, and explicitly highlighted motivations (often noted by bold or prominent placement) form individual checklist items. (5) Scoring System: Each checklist item receives an importance-basee score ranging from 1 to 5 5-minor details are rated 1, core elements as 3, and critical components central to the paper as 5. The checklist items and their importance scores (I-5) meticulously crafted by human experts with domain knowledge. Checklist format is listed in Appendix E.
Annotation Protocol. The annotation process was governed by a strict protocol to ensure quality and consistency. Each paper-poster pair processed by four members of our annotation team. Three annotators independently create checklists, while a fourth, senior annotator serves as a verifier and integrator. This fourth annotator's role is to reconcile the three independent annotations by creating a superset (a union) of all identified checklist items. They also normalize the importance scores by calculating the average for each item across the initial annotations and rounding it to the nearest integer, ensuring final, consensus-based score. Posters with major initial disagreements are excluded from the final test set to ensure data quality. For quality assurance in developing our checklists, we consult with researchers who have previously created posters (while maintaining anonymity). Their feedback confirms that our checklist design accurately reflects their poster creation priorities and decision-making process. Further details about our annotation team are provided in Appendix C.
Dataset Collection and Statistics. P2PEVAL consists of 121 paper-posted pairs collected from the ACL conference series (from 2022 to 2024) under CC4.0 license and from SciPostLayout (Wang et al., 2024a), which contains posters from ee00Ore under the CC-BY license. For each pair, P2PEVAL preserves the original research paper in PDF format and the corresponding academic poster in both PDF and PNG formats. As shown the Fig 4, P2PEVAL encompasses a broad range of research categories and disciplines. The annotation process results in 1738 checklist items, with 775 associated with visual elements. The scoring system yields an average per-item maximum score of 4.07. To ensure fairness and diversity, the benchmark includes papers from wide range of scientific fields, as detailed in Appendix E.2. Our analysis in Appendix E.3 shows that model performance varies across topies, indicating the benchmark does not unfairly favor single domain.
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Research Categories
Scientific Domains
Other
Science
2B
Benchmark
Biology
Psychology
15.7%
Survey
Medical Sciences
22
Humanities Social Sciencus
Methodology
Agriculture
of Papar-Poster
Figure 4: Distribution of P2PEVAL.
3.2 POSTER EVALUATION FRAMEWORK
Our evaluation pipeline consists of two complementary methodologies: Fine-Grained Poster Evalua- tion and Universal Poster Evaluation. We describe these two evaluation methods in detail below.
3.2.1 FINE-GRAINED POSTER EVALUATION
We design a Fine-Grained Poster Evaluation pipeline to measure generated poster's fidelity to the key content selected by the original author. This metric focuses the accurate representation of scientific information, not the imitation of a specific layout. The evaluation a deterministic process that uses an LLM as an automated verification tool. The process consists of two steps:
Step 1: Automated Verification. For each item on the human-authored checklist (Section 3.1), prompt an LLM (GPT-4o) perform strictly defined check and return relevant score: verify whether that specific, verifiable fact is present and accurately represented in the generated poster. The LLM's role is not subjective judgment. Each checklist item's maximum score consensus-derived from multiple annotators, ranging from minor visual elements assigned a score of to core research components scored reflecting their relative importance. This numan-centren approach ensures that the scoring system inherently embodies human preferences and domain expertise.
Step Deterministic Scoring. Then we programmatically aggregates these results from LLM. We formally define the final fine-grained evaluation score S fine as S
Σ 5; -i-1 M; 100, where Stin is the normalized fine-grained evaluation score on 0-100 scale, 8; is the assigned score for the ith checklist item represented in the generated poster, Md, denotes the corresponding maximum possible score for that item, and n signifies the total number of checklist items. Consequently, the Fine- Grained Poster Evaluation score comprehensively assesses generated poster's capability to faithfully preserve the original research's 's essential content and visual priorities. By emphasizing explicit fidelity to the original author's intended communication goals rather than generic quality alone, the approach enables clear comparative analyses across diverse poster generation methodologies.
3.2.2 UNIVERSAL POSTER EVALUATION
Universal Poster Evaluation employs unified set of evaluation criteria, each evaluated independently on a discrete scale ranging from 0 to These universal criteria (U, through U1o) include:
U1: Authorship and Title Accuracy
Up: Dimension Appropriateness
U2: Image Uniqueness and Quality
Ux: Visual Consistency
Uy: Balanced White Space
Ug: Content Fidelity
U4: Contextual Relevance
Ug: Information Flow Logic
Us: Optimal Visual-to-Text Ratio
UIo: Self-Contained Explanation
While LLMs can score individual criteria. they often fail to replicate the complex, non-ling non-linear weightings humans apply when forming holistic judgment. To address this, developed two-step

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Table Experimental results of different models on P2PEVAL. Higher scores indicate better,
Model
Size
ROUGE-1 ROUGE-2
ROUGE-L
BERT'
Judge' FineGrain'
Universal
Closed-Source Models
Claude-3, 1
0.2745
0.0830
0.2527
0.8109
0.5537
65.3962
37.2474
Claude337-Someet
0.2734
0.0848
0.2516
0.8111
0.6281
65.8848
35,5062
Claudo-3.--Somnc
A
0.2367
0.0615
0.2185
0.8081
0,2810
47.7385
30,2544
GPT-4.1-2025-04-14
A
0.2459
0.0685
0.2281
0,8113
0,4793
60.2879
34,4700
LPT-4.1-mini-2225-044
0,2616
0.0741
0,2407
0.8125
0,3388
$5,3493
31.0697
GPT-411-Dum-2022-06-.1
0,2169
0.0557
0,2066
41,3446
27.7149
GPT-40-2024-11-20
0.2395
0.0668
0.2217
0.8114
0,4959
55,4380
34,3888
GPT1-4o-mini-2024-07-11
0,2362
0.0732
0,2198
0.8167
0.2314
48, 88.8879
30,8409
OpenAl-o1"
0.2385
0.0611
0.2200
0.8088
0,3103
56,8504
34,1659
SccdI.S-VL
0,2160
0.0539
0,2026
0.8041
0.4050
62.4702
33,9840
Weed-Thinking-v1.55
0.4711
61,9632
34,6882
Sced-Thinking-v//
0.2493
0.0767
0.2315
0.8116
0.3719
57.1457
33,2461
Doubao- 1,5 .i-vision
0.2586
0.0849
0,2409
0.8089
0.0354
45.9282
14.0841
YuanBao
0.0083
57.8677
31,5754
6B+ Models
VL3
8B
0.1980
0.0618
0.1847
0.7994
0.0776
33.3900
22, 22.2245
Qwen
0.0859
0.2373
0.8152
0.1835
45.0272
28.8107
Qwen3Kr
0.2231
0.0619
0.2082
0.8125
0,2545
53.6611
32.4912
QW
7B
0.1090
0.0414
0,1020
0.7645
0.0083
13.7417
13,0597
12B+ Models
Gemma-3
12B
0,2411
0.0764
0,2250
0.8096
0.0940
46. 7903
27.3686
V11emV1133
14B
0.2437
0.0736
0.2253
0.8132
0.0756
45.5513
25.6062
27B+ Models
Gemma-3
27B
0,2500
0.0794
0,2346
0.8133
0.2857
50.893
28.7410
Gemma-3
27B
0,2372
0.8132
0.2417
52.1716
28.5901
InternVI.
38B
0,2440
0.0756
0,2258
0.8143
0,2333
52.6634
29.5850
Qwen
3/30B
0,2270
0.0637
0,2125
0,8120
0,2562
52.2125
31.1930
Qwen3
32B
0.2314
0.0659
0,2168
0.8090
0.1736
46,0383
28.9477
Qwen2.5-Coder
32B
0,2666
0.0949
0,2487
0.8167
0.3884
55.9441
32.7935
72B+ Models
Deepseek-RR
37/671B
0.1927
0.0461
0.1795
0.8015
0.5333
62.5013
33,9701
Deepseek-V33 V3T
37/671B
0,2232
0.5041
59.6805
VL.
78B
0,2424
0.0789
0,2245
0.8152
0,2773
51.2962
28.9230
Qwen
22/235B
0,2278
0.0625
0,2141
0.8077
0.3967
53.7927
31.4551
QWLn2.5-VL
72B
0.2577
0.0909
0,2400
0.8148
0,2833
55.7929
32.3105
1lama-4-Senn
17/1098
0.1208
0.2625
35.7872
Qwen3-P2pts
8B
0,2882
0.0955
0.2675
0.8135
0.4587
57.6622
32.4996
Qwen2.5-VL-PPP
7B
0.1939
0.0609
0.1797
0.7926
0,3140
37.3078
25,0337
IntemVL.3 P2ps
8B
0.2744
0.0883
0.2551
0.8117
0,3772
51,9670
31.6206
Drsasoning/Thinkigg Mode.
Because they are text-only LLMs, we use Claude-3, 303..7750000 7-Sonnet as the provider of Figure The rate at which LLM-as-a-Judger prefer generated posters over original author- SSoores Universal Evaluation. Postees generated Our model, built Qwen 8B, Qwen2 5-VL-85 or InteraVL3-3B
Describer. Fl scores of BERTScore. Fine-Giraine,
produced posters. by Tencent's 's Al application called YuanBao. fine-tuned using P2PINSTRUCT respectively.
Poster
hybrid approach. First, we use an L.MM to generate scores for the 10 discrete criteria. Second, we train an XGBoost model to predict final holistic score from these 10 features, using 1,701 human ratings as ground truth. This hybrid methodology was a principled design choice, as it combines the LLM's strength in stable feature extraction with XGBoost's proven ability to learn complex, non-linear human preference functions. Specifically, the XGBoost model undergoes training with 10-fold cross-validation and utilizes 200 trees. The resulting predictive model exhibits strong performance. achieving an R² of 0.92, thus validating the reliability and effectiveness of our Universal Poster Evaluation pipeline. More details about Universal Poster Evaluation can be found in Appendix F.
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Table 2: Results of pairwise human preference evaluations. Table 3:
Performance comparison of
P2P across different output format.
Comparison
Preferred or Tied
Preferred (%)
(A vs. B)
(%)
Output
FineGrain
Universal
P2P / YuanBao
83.05
54.35
HTML
65.3962
37.2474
P2P / Original
57.63
35.59
SVG
52.7408
30.6648
YuanBao / Original
20.34
12.40
LaTex
56.8756
25.2585
4 EXPERIMENTS AND ANALYSIS
4.1 EXPERIMENTAL SETUP
We conduct comprehensive experiments evaluate P2P against several MLLMs on P2PEVAL. Specif- ically, we compare these different model series: GPT (Achiam al., 2023), Claude (Anthropic, 2024), Doubao (Seed et al., 2025). Qwen (Bai al., 2025), Intern (Chen al.. 2024), Gemma (Team et al., 2025), Deepseck (Guo et al., 2025; Liu et al., 2024). Additionally, we include in our evaluation poster images generated by Tencent's AI application, YuanBao (https: pesttss//yyannee //yuanbao. tencent com/), which directly produces academic posters in image format and Chinese. We also fine-tune our model Qwen Qwen2-VI-P2P and InternVL3-P2P using P2PINSTRUCT. The detail of training is shown in Appendix G. And input processing is in Appendix I.
4.2 EVALUATION METRICS
Beyond the human-validated Universal Poster Evaluation (max 50) and Fine-Grained Poster Eval- uation (max 100) using GPT-4o in P2PEVAL, we supplement analysis with objective metrics: (1) ROUGE (Lin, 2004), which measures n-gram overlap between generated and reference poster content, thus capturing lexical similarity. (2) BERTScore (Zhang al., 2019). which leverages contextual embeddings to assess semantic similarity. During evaluation, all image links are removed from the text to ensure fair comparison of purely textual content. And the "Judge" metric reports how frequently VLLM-based automated evaluators prefer P2P's posters over original author-created versions. We also have aesthetic quality evaluation in Appendix
4.3 RESULTS AND ANALYSIS
Main Results. Table summarizes the experimental outcomes of various models evaluated using P2PEVAL. Our analysis reveals several key findings: (1) Closed- vs. Open-source Models: Closed-source models, notably Claude-3. 3. achieve superior performance qualitative assessments like the Universal and Fine-Grained evaluation. And leading open-source models Claude as the provider of Figure Describer), demonstrate strong
such as BacpaseekRRllusiii competitiveness. (2) Impact of Reasoning Capabilities: Models employing reasoning thinking modes such as Claude-3. 7-Sonnet and Qwen3 consistently show enhanced performance, especially in the outputs that are more aligned with human preferences and detailed content requirements. (3) Efficacy of P2PINSTRCTT: Fine-tuning models P2PINSTRUCT dataset yields substantial and statistically significant improvements (see Appendix H). The Qwen3-P22 P2P-83 achieves the highest ROUGE scores across all evaluated models, significantly outperforming its base version and even leading closed-source models in these lexical metrics. It also demonstrates considerable gains in FineGrain and Universal scores over Qwen3. The larger gains on lexical metrics like ROUGE reflect the dataset's focus on teaching foundational text generation subtasks, while the consistent improvements on our flagship metrics confirm that these skills translate to higher-level fidelity and quality. These results underscore the value of P2PINSTRUCT. (4) Supplemental Observations: A divergence among evaluation criteria is also evident excellenceee in lexical overlap (ROUGE) does not uniformly correlate with detailed fidelity (FineGrain), emphasizing the comprehensive nature of P2PEVAL. The strong performance of text-only models utilizing Claude for figure description points to the effectiveness of modular, hybrid approaches in this complex generation task.
evaluation. This suggests that advanced reasoning aids in generating
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Table Ablation study results by Claude-3, 7-Sonnet.
Mulli Agent Figure Describer
Rellection
FineGrain
Universal
65,3962
37.2474
64.4556
34,2229
63.7388
35.1107
63.5806
33.1458
60.7233
34,2554
Analysis of Human Preference Evaluation. To complement our P2PEVAL, we conduct pairwise human preference evaluations, the results of which are presented in Table Participants compare posters generated by P2P using C1 Claude-3, 7-Sonnet, Tencent's YuanBao, and the original author-created posters. The "Preferred proportion of instances where method A is judged superior equivalent to, and strictly superior to, method B, respectively. The results demonstrate clear preference for P2P-generated posters over those from YuanBao. Notably, P2P also shows competitive performance against original posters, suggesting its capability to produce posters of superior quality a significant number of cases.
Tied (%)" and "Strictly Preferred (%)" quantify the
Analysis of Output Format. Our investigation of different output formats reveals HTML as the optimal medium for academic posters using c1 aude-3, -Snonee. As documented in Table 3, HTML-based poster outputs consistently outperform SVG and LaTeX alternatives across both fine- grained and universal metrics. The inherent flexibility of HTML and CSS for layout structuring and content decoupling, coupled with the robust rendering capabilities of modern browsers, contributes to this performance. Furthermore, our experiments suggest that current LLMs exhibit greater proficiency in HTML code generation compared to equivalent SVG or LaTeX implementations, resulting in fewer rendering errors structural inconsistencies in the final poster artifacts.
Ablation Study. The results of the ablation study of P2P in Table 4 demonstrate that the full system consistently outperforms reduced configurations. When reflection mechanisms (implemented through checker modules) are removed, we observe moderate decline in universal metrics, suggesting these iterative feedback loops enhance overall poster quality and aesthetic coherence. Similarly, ablating the Figure Describer component, which transforms visual elements into textual descriptions, results in performance degradation. This indicates that directly feeding raw images to MLLMs for content integration can be less effective than providing them with semantically rich textual summaries. These descriptions appear to reduce the interpretative burden on the MLLMs and facilitate a more accurate contextualization of visual information within the poster. Removing all specialized components (resulting in a direct paper-to-poster pipeline without intermediate processing) leads to the greatest performance drop in fine-grained evaluation. This confirms our hypothesis that poster generation benefits significantly from modularized, specialized processing that mimics the distinct cognitive steps humans undertake when creating posters from research papers.
Analysis of Layout without Reflection. A comparative analysis of poster layouts generated by Claude and GPT models, summarized Table 5, reveals distinct structural tendencies inherent in content segmentation and spatial organization for each model when operating without reflection mechanisms. Claude-generated posters typically exhibit a more fragmented structure, utilizing a greater number of columns. These layouts also demonstrate tendency towards imbalanced spatial distribution, with taller content often concentrated towards the right and greater variability in column heights. This often results in higher proportion of blank space, suggesting less efficient spatial utilization. In contrast, GPT-generated posters generally present more uniform and compact layouts. These findings suggest challenges achieving consistent content allocation across the poster layout, critical aspect for visual appeal and readability in academic posters.
5 LIMITATIONS AND FUTURE WORK
While P2P demonstrates significant advances, we acknowledge several limitations that offer avenues for future work.

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Table 5: Comparison of layout statistics in posters generated by Claude Sonnet and
GPT-4o-2024-11-20
Layout Statistic
Claude
GPT
Layout Statistic
Claude
GPT
General
Tallest Column
Total columns
376
293
Height (px)
7272.22
5794.42
Balance
Text length (char)
2057.37
1554.44
Relative position'
0.55
0.51
Number of images
2.93
2.30
Height coefficient of variation
0.21
0.18
Shortest Column
Height ratio (max/min)*
1.73
1.61
Height (px)
4379.82
3979.53
Blank space proportion*
19.16%
14.92%
Text length (char)
1392.26
1296.84
Number of images
1.74
1.59
Index relative column positions within posters; values closer to 0.5 indicate more centered, balanced layouts. Measure of height consistency across columns; lower values indicate more uniform column heights.
Ratio between tallest and shortest columns; values closer to indicate more even column heights.
Percentage of total poster urea occupied by blank space.
Output Format: Our framework currently optimizes for HTML rendering due to its flexibility and the strong capabilities of modern LLMs in generating HTML. As shown in our ablation study (Table 3), generating other formats like LaTeX incurs a performance cost. This presents a practical constraint for users academic environments where LaTeX or PowerPoint are prevalent. Future work could explore improved code generation for these formats or robust HTML-ttoPDD//PTT converters.
Computational Cost: The multi-agent framework with its iterative reflection loops putationally more intensive than single-past generation pipeline. While we demonstrate that the cost is affordable (Appendix K), the latency may be a consideration for real-time applications. The number of reflection iterations serves as a tunable parameter to balance quality and cost.
Reasoning Boundaries: As discussed in Appendix B.2, the checker-reflection mechanism is highly effective at correcting structural and syntactic errors. However, its ability to resolve deep semantic complex compositional reasoning errors is ultimately bounded by the capabilities of the underlying LLM. For instance, correctly arranging highly intricate, domain-specifie multi-panel figures remains challenge if the base model lacks the necessary visual-spatial reasoning. Our escalation strategy mitigates this but does not climinate this fundamental boundary.
CONCLUSION
We introduced P2P, multi-agent framework that effectively transforms research papers into visually coherent and informationally faithful posters. By modularizing the task and integrating checker- reflection loops, our system demonstrates strong performance, producing outputs that often rival human-created examples. We supported this work by creating P2PINSTRUCT, large-scale instruction dataset, and P2PEAAL, a comprehensive evaluation benchmark. Crucially, this work argues that evaluating complex, creative artifacts like academic posters requires dual approach that decouples objective, verifiable fidelity from subjective, holistic quality. Our P2PEVAT. benchmark embodies this principle, combining fine-grained checklists for content preservation with universal scoring model trained to emulate human aesthetic judgment. This
evaluation, alongside the P2P framework and P2PINSTRUCT dataset, not only provides robust solution for automated poster generation but also offers a principled methodological blueprint for future research automated scientific communication and other complex creative AI domains. This foundation promises enhance research accessibility and dissemination efficiency for the entire academic community.
ETHICS STATEMENT
Our work adheres to the principles outlined in the ICLR Code Ethics. The primary goal of this research is to contribute societal and human well-heing by developing tools that streamline
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and accelerate scholarly communication, making research more accessible and saving researchers valuable time.
In line with the principle of upholding high standards of scientific excellence and avoiding harm, acknowledge the potential risk of automated systems misrepresenting or "hallucinating" scientific content. To mitigate this, cally designed to evaluate the factual fidelity generated posters. Our dual-evaluation framework, combining Fine-Grained checklist-based verification for objective accuracy and Universal score for holistic quality, represents a direct effort to ensure the trustworthiness and reliability of the output.
contribution includes the P2PEVAL. benchmark, which is specifi-
To respect the work required produce new ideas and artefacts, all papers and posters used to construct our P2PEVAL benchmark were sourced from publicly available repositories (ACL, Sci- PostLayout) under permissive licenses (CC-BY, CC4.0), and we give full credit the original authors.
In developing our benchmark and datasets, we engaged human annotators. We are committed to fairmess and respect for all individuals involved. As detailed in Appendix C, our annotation team consisted of qualified domain experts who were compensated at rate exceeding the local statutory minimum wage. We established rigorous annotation protocol, including training and verification steps, to ensure data quality and consistency, while also protecting the privacy and well-being the participants.
We acknowledge the environmental cost associated with training large models and have reported our resource consumption in Appendix J. We believe the potential benefits of our work in enhancing the efficiency of scientific dissemination for the broader research community justify this computational cost. Finally, discuss the limitations of our system in Section Q to provide transparent account of its current capabilities.
REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, we have made comprehensive efforts to provide all necessary components. All code for the P2P multi-agent framework, the P2PEVAL evaluation pipeline, and the fine-tuning process will be made publicly available in the anonymous GitHub repository linked in the paper.
The datasets created for this research will also be released. This includes P2PINSTRUCT, our large-scale instruction-tuning dataset of over 30,000 examples, and P2PEVAL, which comprises 121 paper-poster pairs and 1738 human-annotated checklist items in structured YAML format.
The paper provides detailed descriptions of our methodology. The architecture of the P2P framework detailed in Section 2 and illustrated in Figure 1. The construction of the P2PEVAL benchmark and the full evaluation protocol are described in Section For implementation-sper sppccifcc details, Appendix G documents the fine-tuning hyperparameters and setup using the LLaMA-Factory framework. Appendix describes the input processing pipeline for both text and figures. Furthermore, we include the exact prompts used for each agent and evaluation step in the Appendix allow for full replication of our generation and evaluation logic.
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Appendix
CONTENTS
Related Work
18
B More Details of P2P and Implementation
18
B.1 Agent Implementation and Model Agnosticism
18
B.2 Checker-Reflection Mechanisms in Detail
18
Annotator Team
19
More Details of P2PINSTRUCT
20
E
More Details of P2PEVAL
20
E.1
Checklist Format
20
E.2 Benchmark Statistics
21
E.3 Analysis of Performance Breakdown by Topic
22
F
More Details of Universal Poster Evaluation
22
F1
Details of the Human Rating Protocol
22
F22 Details of Evaluation Criteria
23
F3 Other Methods Performance
24
G Training Details
24
H Statistical Significance of Results
25
I Input Processing
25
Resource Consumption
25
K Computational Cost and Latency
25
Aesthetic Quality Evaluation
26
M The Features of Fine-Grained Poster Evaluation
27
N The Features of HTML Format
28
Examples of Poster Generation
28
Prompt
30
Limitations
32
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A RELATED WORK
Poster Generation. Academic poster generation involves creating a poster that summarizes the key information from academic paper. Paramita et al. Paramita & Khodra (2016) develop model that extracts essential sentences into templates to generate text-based posters. Qiang et al. Qiang et al. (2019) propose more comprehensive method, decomposing poster generation into three subtasks: content extraction Mihalcea & Tarau (2004); Xu & Wan (2021); Cheng et al. (2024b;a), panel attribute inference Zhong et al. (2019); Li et al. (2020); Huang et al. (2022), and panel layout generation Lin et al. (2024); Zhang et al. (2023). Postdoc Jaisankar et al. (2024) utilizes MLLMs to generate template-basee posters but cannot produce flexible layouts with more dynamic integration of figures and text. Additionally, existing academic poster datasets Yao et al.; Xu & Wan (2021); Qiang al. (2016); Wang et al. (2024a); Saxena et al. (2025) lack fine-graid fne-grained evaluation metrics necessary for comprehensive quality assessment.
HTML Code Generation and Multi-Agent. Recent research automated front-end development
focuses on generating HTML from diverse inputs such screenshots, prototypes and natural language. This has spurred the creation of benchmarks like Design2Code (Si et al., 2024; Yang et al., 2025), Websight (Laurençon et al., 2024), WebCode2M (Gui al., 2025a), and Web2Code (Yun al., 2024). Code generation methodologies vary, including direct translation, structured approaches such as DCGen's (Wan al., 2024b) divide-and-conquee strategy and UICopilot's (Gui et al., 2025b) hierarchical generation. Applications target mobile UIs (Xiao et al., 2024b; Zhou et al., 2024), multi-page websites (Wan et al., 2024a), and web design (Xiao et al.. 2024a; Li et al., 2024; Zhang et al., 2024), with model fine-tune tuning (Liang et al., 2024) enhancing performance. Multi-agen agent systems are increasingly adopted for complex tasksHan et al. (2024); Liu et al. (2025); for instance, agentic workflows can convert designs to code (Ding et al., 2025; Islam et al., 2024), and some systems employ distinct agents for sub-tasks with iterative human feedhack (Wang et al., 2024b).
LLM a Judge. The use of LLMs as evaluators, termed "LLM-as-a-Juuge"" is well-studied and has demonstrated high consistency with human judgment, with early work focusing on LLMs evaluating other LLMs, research introduced systems like AUTO-J Li et al. (2023a). leveraging pairwise and single-response evaluations to achieve strong agreement with human assessments Bai et al. (2023); Li et al. (2023b); Yang et al. (2024b); Li et al. (2023c); Sun et al.; Yang et al. (2021); Sun et al. (2025). With the rise of MLLMs, their potential as evaluators in multimodal tasks is being explored, traditional metrics often fail to capture the nuances of complex multimodal outputs Antol et al. (2015); Liu et al. (2)23a;d:e:b). To enhance LLM evaluation capabilities. techniques such Chain-of-Thought Wei et al. (2021); Chu et al.: Chai et al. (2025); Sun et al. (2024) and Training- fraining-free instruction following Brown al. (2020): Wei et al. (2021) have been proposed, addressing the need for more robust evaluators in both unimodal and multimodal contexts.
in JudgeLM Zhu et al. (2023); Yang et al. (2024a). Subsequent
B MORE DETAILS OF P2P AND IMPLEMENTATION
B.1 AGENT IMPLEMENTATION AND MODEL AGNOSTICISM
As stated in the main paper, P2P is a model-agnostic orchestration framework. For any given experiment in Table 1, all core generative components (the Figure Describer, Section Generator, Content Generator, and HTML Generator) are powered by the same underlying L.LM being evaluated. This experimental design ensures fair, end-to-end benchmark of each model's 's capabilities on the complex paper-to-postee task. The only fixed components (shown in Appendix I) are programmatic utilities like PyMuPDF for text extraction and DocLayout-YOke YOLO for initial figure detection.
B.2 CHECKER REFIEERRONF MECHANISMS IN DETAIL.
The checker-reflectin mechanism is central to P2P's quality assurance. As shown in Fig 5, here we detail each checker's implementation, failure triggers, and the nature of the reflection loop.
Figure Checker. This module is rule-based and programmatic for efficiency. Its reflection loop is triggered if the count of detected figures mismatches the count of detected captions (paired using
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Fig.:
Cap:
Fig:
Cap:
Figure Checker
Threshold - 0.05
Section Checker
Reflection
LACK OF BENCHMARK
Whitopeee Batio 0.300 Nurber o'luage
Whitopace Bailo 0.8 - D. Narber of Iragge 190L2
Poster Checker
14=15
Figure 5: Illustration of the Checker-Reflecte Mechanisms.
Manhattan distance). The initial confidence threshold for the DocLayout- YOLO detector is set to a high 0.85 to minimize false positives. If a mismatch occurs, the threshold is iteratively decreased by 0.05. This allows the Figure Extractor to capture elements that may have been initially assigned a lower confidence score (e.g., tables that visually resemble plain text), ensuring all significant visuals are reliably captured.
Section Checker. This module uses an LLLM-as-a-ludge to assess semantic qualities. After content generation, the checker prompts the L.MM to evaluate the text against the source paper on coherence, completeness, faithfulness, and correct referencing. The LLM returns binary decision ("OK" or "Problem"). ") If a problem is detected, it also provides actionable feedback for revision (e.g.. "The conclusion section omits the key limitation mentioned in the paper."). The Content Generator then re-runs with this new feedback, ensuring iterative refinement.
Poster Checker. This module is also rule-based. It programmatically parses the generated HTML check for structural integrity and layout aesthetics. For example, reflection loop is triggered if the proportion of blank space exceeds 10% or if column heights are severely imbalanced. The checker then feeds these failed metries (e.g., ""whitespace_ratd ratio": 0.15") and previous HTML code back to the HTML Generator, instructing it to regenerate the layout. On average, this loop runs for 2.32 iterations for GPT-4.1 and 3.47 for Qwen-2.5-VL-7B.
Failure Boundaries and Escalation. The reflection paradigm is highly effective at correcting structural and syntactic errors. However, it can struggle with deep semantic or complex compositional reasoning errors. For instance, if paper contains numerous, intricate multi-panel figures that require a highly specific arrangement to be coherent, the LLM might struggle to generate a logical HTML structure. The Poster Checker may detect the resulting layout imbalance (e.g., high whitespace), but if the root cause is the LLM's inability to reason about the complex spatial relationship between sub-figures, reflection alone may not solve The LLM, lacking a better strategy, might simply reshuffle the same flawed components. To mitigate this, we employ an escalation strategy: if checker fails after retries, the reflection loop can escalate to an earlier agent, prompting a more fundamental revision. For example, persistent layout failure in the Orchestrate Agent trigger the Section Agent to regenerate its content entirely, attempting to provide a simpler or more structured input that the Orchestrate Agent can handle. This provides multi-level recovery mechanism, though it is ultimately still bounded by the LLM's core reasoning ability.
C ANNOTATOR TEAM
Our team comprised 12 annotators, all holding university degrees, with six possessing research-baad based master's 's degrees and six holding doctorates. This composition guaranteed that for any given paper,
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least three annotators possessed relevant domain expertise. Before the formal rating process, each annotator completed a one-hour training session.
We have identified the following potential risks and their corresponding mitigation measures:
1. Privacy and Confidentiality Risks: may lead to privacy breaches. To mitigate this, all data will be anonymized and stored securely, with access restricted authorized research personnel only.
The sharing of personal information by participants
2. Psychological Risks:
Participants may experience discomfort stress during task execution. To address this, we provide detailed task instructions and debriefing sessions to ensure participants feel supported throughout the process. Additionally, participants have the right to withdraw from the study at any stage without penalty.
3. Physical Risks: Although our research procedures do not involve significant physical risks, we will closely monitor participants for any signs of distress and provide necessary support promptly.
Furthermore, regarding data annotation, we have paid annotators wage higher than the statutory minimum wage the country the data annotators, as sign of respect for their labor.
D MORE DETAILS OF P2PINSTRUCT
Data Source and Generation. P2PINSTRUCT was constructed using the training split of the SciPostL Layout dataset, which is separate from test We chose this source for its permissive CC-BY license and topic diversity. And we avoided using papers from sources like ICLR after consulting with legal professionals, as their copyrights would require author-by-author permissions. To mitigate the risk of "imprinting bias" from a purely synthetic loop, we used "teacher-forcing: each stage of data generation, the model was guided by the corresponding part of the original, human-created poster. For example, the HTML Generator received the generated Markdown but was also shown the ground-trutt poster's image as a visual layout reference. This process anchors the synthetic data to human design principles.
like" approach.
Quality Validation. The "high-quality" claim is supported by three pillars: (1) Built-in Quality Control from the checker-releectin loops during generation; (2) Manual Verification of a random sample of 20 pairs from each stage; and (3) Empirical Validation, where models fine-tuned on P2PINSTRUCT consistently and significantly outperform their base versions (Table 1), providing strong evidence of the dataset's effectiveness.
E
MORE DETAILS OF P2PEVAL
E. CHECKLIST FORMAT
All checklist annotations, including unique paper identification, detailed evaluation criteria, reference figures (when applicable), and established maximum scores, are documented in YAML format.
Each item is a distinct, verifiable component with a description, figure, and max score. For clarity, here is an example that is similar to the following simplified version:
name: paper_ic id
checklist:
description: Does the introductien section highlight the limitations current methods in code generation tasks and motivate the use xxxxx?
max_score: 4
description: Does the introduction section provide xxxxx examples for visual demronstration?
figure:
max_score: 5
Listing 1: A YAML checklist for paper evaluation.
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Paper Pages
Paper Tokenss
14
12
10
15-
10-
10
20
30
40
10000
20000
30000
400000
50500
60000
70000
Pages
Toxens
Figure 6: Distributions of Paper
Among them, XXXXX refers to what was proposed in paper _eer__dd,___ which has been anonymized for double-blind review; figure: 0 indicates there is reference image (the image is taken from the original paper); max_score represents the weight assigned by human annotators. More examples are provided in the anonymous GitHub repository linked to supplementary materials.
E.2 BENCHMARK STATISTICS
P2PEVAL is composed of 121 complete paper-poster pairs. Each sample in the benchmark is comprehensive, including the original research paper ('ppdf), the author-created poster ('-pdf' and :pgg",, and our detailed, human-annotate annotated 'checklist.yaml file. The statistics presented in Table highlight the complexity and diversity of the data, underscoring the challenges of the paper-to-poster task.
Table Key statistics of the P2PEVAL benchmark dataset.
Metric
Value
Total Paper-Poster Pairs
121
Total Checklist Items
1,738
Total Visual Elements in Checklists
775
Average per Sample
Paper Length (Pages)
15.13
Paper Content (Tokens)
17,104
Poster Content (Tokens)
2,050
Checklist Content (Tokens)
548
Images per Sample
7.25
The source documents are substantial academic works, with an average length of 15.11 pages and approximately 17,100 tokens per paper. The corresponding posters are significantly more concise, averaging around 2,050 tokens. This reflects textual summarization ratio of approximately 8.3-to-1, demonstrating the extensive content distillation required for poster generation.
The multimodal nature of the task is evident from the data. On average, each sample contains 7.25 visual elements (figures and tables), with some papers including as many as 28. The richness of annotations is highlighted by the 1,738 unique checklist items across the dataset, averaging 548 tokens of descriptive metadata per sample. This fine- ann-ngrined annotation provides robust foundation for the detailed evaluation of mener-eenerated posters. The distribution histograms show that the
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majority of papers range from 8 17 pages, and most contain between and 10 images, representing a typical cross-section of academic publications.
E.3 ANALYSIS OF PERFORMANCE BREAKDOWN BY TOPIC
Table 7: Performance breakdown by research category.
Metric
Methodology
Survey
Benchmark
Other
Fine-Grained Score
64.24
65.88 40.95
68.78 42.46
65.92 40.63
Universal Score
42.21
Table 8: Performance breakdown by scientific field.
Metric
Comp. Sci.
Biology
Psychology
Medical Sci.
Hum. & Soc. Sci.
Fine-Grained Score
66.59
64.17
73,42 41.45
66.51
62.84 39.64
Universal Score
42.80
42.21
39.85
We analyze the performance of our best-performing model (Claude-3.7-Sonne)) across different research categories and scientific fields from our P2PEVAL.
Performance by Research Category: The tab shows that our P2P performs exceptionally well Benchmark papers, which typically have a highly regular structure (e.g.. Task Definition, Dataset, Results). This structure is more easily parsed and summarized by LLMs. Methodology and Survey papers, which can be more conceptually dense, also yield strong results, demonstrating the model's versatility.
The tab shows that P2P achieves the highest Fine- Grained score in Psychology. likely because papers in this field often use standardized charts (bar graphs, scatter plots) that are well-understood by MLLMs. Computer Science papers achieve the highest Universal score, potentially due to the abundance of CS papers in training data. The slightly lower scores in Humanities & Social Sciences may be due to the reliance on denser, narrative text over structured figures, highlighting potential area for future improvement.
Performance by Scientific Field:
F MORE DETAILS OF UNIVERSAL POSTER EVALUATION
F.1 DETAILS THE HUMAN RATING PROTOCOL
As stated in the main paper, our Universal Poster Evaluation score is not directly generated by an LLM. Instead, it is predicted by an XGBoost model trained to replicate human judgment. This was a deliberate design choice,
found that while LLMs can reliably score discrete, objective criteria, they struggle to capture the complex, non-linear weightings that humans apply when forming a single holistic impression of poster's 's quality. The following protocol used to collect the human scores that serve as ground truth labels for training this XGBoost model.
Annotator Qualifications and Training. Details our annotation team are shown in Appendix C. During the training session, reviewed the 10 universal criteria (detailed in Section F.2) and provided scoring guidelines using concrete examples. For instance, sward-winning NeurIPS, ICML and ICLR posters were presented examples deserving full score (50/50), while posters with significant clarity issues, poor visual design, or logical gaps were used to demonstrate how deductions should be made.
Rating Procedure and Scale. For Universal Poster Evaluation, annotators produced single holistic score in [0, 50], referencing U1-U10. We provided explicit anchor points to reduce scale ambiguity:
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45-50: Outstanding. Meets or exceeds expectations most criteria; highly professional.
35-44: Strong. Minor issues on few criteria; clearly presentation-reatt
25-34: Adequate. Several weaknesses, but overall understandable and usable.
15-24: Weak. Multiple violations (e.g.. poor fidelity, cluttered layout, weak flow).
0-14: Poor. Severe issues (e.g.. illegible, misleading, not self-contained).
Each poster was rated independently by three annotators. Similar to the annotation of the checklist, any posters with substantial initial scoring disagreements were excluded from the final training set for the XGBoost model to maintain high data quality. We utilize both powerful models like GPT-4o and lighter models such as Qwen- Qwen-VL-2255 VL.-2.5-32B, ensuring the trained annotators are exposed to diverse samples enhance generalizability. We collected total of 1, 1,701 human ratings across the dataset. To avoid any leakage when training the scoring model, these ratings were collected on outputs produced by an ablated system variant without the checker-reflection mechanism.
Reliability and Agreement. The rigor of our protocol is validated by strong inter-annotator agreement statistics. We achieved a Krippendorff's Alpha of 0.95, a Spearman correlation of 0.96 and Cohen's Kappa of 0.71, indicating exceptional reliability and consistency among our raters' judgments. These high-quality human scores formed the ground-truth labels (y)) used to train our final XGBoost scoring model. We also observed low divergence between predicted and human score distributions (KL divergence: 0.1093; JS divergence: 0.0239), supporting the reliability of our scoring pipeline.
F.2 DETAILS OF EVALUATION CRITERIA
The 10 universal criteria form the input feature vector for our XGBoost scoring model. For each generated poster, an LL--as-a- -uddge-- (GPT-4o) is prompted to score the poster each of the following 10 dimensions on discrete scale from to Below are the detailed descriptions for each criterion used in the evaluation prompt.
U₁: Authorship and Title Accuracy Description: Does the poster clearly and accurately display the complete paper title and the full names of all authors without any spelling errors, omissions, or formatting mistakes? Scoring Guideline: A score of requires a perfect match. Deductions are made for typos, missing authors, or an incomplete title.
Uz: Image Uniqueness and Quality Description: Are all images in the poster unique (i.e., no unintended duplications)? the visual quality of each image (resolution, clarity) sufficient for professional presentation? Scoring Guideline: A score of 5 requires all images to be distinct and high-resolution. Points are deducted for blurry, pixelated, or duplicated figures.
U3: Balanced White Space Description: the negative or "white" space distributed effectively across the poster? Does the layout avoid areas that look visually overcrowded or, conversely, excessively empty? Scoring Guideline: A high score indicates a layout that feels balanced and guides the eye naturally.
U4: Contextual Relevance Description: Do the visual elements (figures, tables) align logically with the adjacent text? Is their placement and thematic connection clear, enhancing the reader's s comprehension? Scoring Guideline: A score of means every figure is placed next the text that describes or references it.
Us: Optimal Visual-to-Tes Ratio Description: Does the proportion of the poster's 's area covered by images effectively serve the research content? Is there good balance between visual evi- dence and textual explanation? Scoring Guideline: The optimal ratio is context-dependent, but a high score reflects a poster that uses visuals impactfully without overwhelming the text, or vice-versa.
Ug: Dimension Appropriateness Description: Are the overall dimensions (width and height) of the poster suitable for its content and a typical presentation environment? Does it avoid extreme aspect ratios that would make it difficult to read or display? Scoring Guideline: A score of indicates standard poster dimensions (c.g., portrait AO, landscape 48" x8"x.""). Extreme, banner-like shapes receive lower scores.
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Uy: Visual Consistency Description: Do the design clements-sute such as color schemes, typography (fonts, sizes), and section heading styles maintain a cohesive and consistent identity throughout the poster? Scoring Guideline: A high score is given for poster with a unified visual theme. Using a chaotic mix of fonts or colors results in a low score.
Ug: Content Fidelity Description: Are the data representations, mathematical formulas, key ter- minology, and scientific findings presented on the poster identical to those in the original research paper? Scoring Guideline: score requires zero "hallucinated" or misrepre- sented facts. Any deviation from the source paper's content results in score deduction.
Ug: Information Flow Logic Description: Is the content organized in logical and intuitive se- quence (e.g.. Introduction - Methods Results Conclusion)? Can a viewer easily follow the research narrative from start to finish? Scoring Guideline: High scores are for posters with clear, linear, or grid-based flow. Disorganized or confusing layouts receive low scores.
U1o Self-Contained Explanation Description: Can the poster be fully understood on its own, without requiring verbal explanation from a presenter? Does provide enough context and detail for knowledgeable reader to grasp the core concepts and contributions? Scoring Guideline: A score of indicates a poster that is fully self-sufficient. poster that just collection of figures with cryptic captions would score poorly.
F.3 OTHER METHODS PERFORMANCE
Our choice of a hybrid MLLM-Feeturize and XGBoost model for the Universal Score was a principled design decision to create more robust and human-aligned evaluation. Directly asking an LLM for a single holistic score is often noisy and inconsistent. Our approach combines the strengths of different models:
Stable Feature Extraction (LLM)): We use the LLM for what it does best: scoring 10 discrete, well-defined criteria (UI-U10).
Learning Human Preferences (XGBoost): We use XGBoost to learn the complex, non- linear function of how humans weigh these criteria, training it on 1,701 human preference scores.
To empirically validate this, we conducted ablation study comparing method against simpler baselines. As shown in Table 9, our hybrid approach significantly more aligned with human judgment than end-to-end LLM scoring based on Qw
Table 9: Ablation study the Universal Score methodology, validating the choice of the MLLM- Featurizer and XGBoost approach. Metrics are correlations (R?) and distributional similarity (KL/JS Divergence, lower is better) against human scores.
Method
R²
KI.-Divereecce
JS-Divergence
Direct LLM Scoring (End-to-Endl
0.27
1.60
0.62
LLM as Regressor (Replaces XGBoost)
0.51
0.34
0.22
Fine-Tuned LLM as Aggregator
0.70
0.06
0.14
Our MLLMMFeaturizer + XGBoost
0.92
0.11
0.02
Additionally, experiment with other methods, including Ordinary Least Squares ( 0.66), Random Forest( R22 0.83), and various regularization techniques(R2 = 0.89); however, these approaches yield suboptimal performance compared to XGBoost.
G TRAINING DETAILS
We utilized the unified and efficient LLaMAFactory framework (Zheng et al., 2024) for all fine-tuning experiments. We use our P2PINSTRUCT dataset with a learning rate 5 10-5 for 3 epochs and employing AdamW (Loshchilov & Hutter, 2017). To ensure optimal instruction-followinn
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the native chat template for each respective model (e.g., Qwen3's template). Training is conducted with BF16 mixed-preee -eexedppreiiion to accelerate the process. No sequence packing was used, and we set a maximum sequence length of 8000, truncating longer examples.
H STATISTICAL SIGNIFICANCE OF RESULTS
We performed statistical significance testing for our key experimental comparisons. Following modern best practices (Wasscrstein & Lazar, 2016; Jeppesen et al., 2021; Brunoni et al., 2013), we report Effect Size (Cohen's d), which quantifies the practical magnitude of observed difference. A large effect size (Id) > 0.8) indicates a meaningful, substantial improvement. As shown in Table 10, fine-tuning P2PINSTRUCT yields large effect size, and our ablation study confirms that removing key components has a medium-tt-llare negative effect on quality.
Table 10: Effect size (Cohen's d) for fine-tuning and ablation studies. Positive values indicate improvement over the baseline; negative values indicate degradation.
Comparison
FineGrain (d)
Universal (d)
Effect of Fine- Fine-Tining on P2PINSTRUCT
Intern Intern VL3-P2P vs. Base
2.13
1.72
Qwen2.5-VL-PP2P vs. Base
4.18
3.00
Qwen3-P2P vs. Base
1.21
0.60
Effect of Ablating Components (vs. Full P2P)
w/o Reflection
-0.15
-0.61
w/o Figure Describer
-0.26
-0.43
Multi-Agent Only
-0.28
-0.82
End-to-Ena Only
-0.72
-0.60
INPUT PROCESSING
All models are configured with the temperature of and the maximum output token length of 8000 to ensure fair comparison while maintaining generation diversity. To isolate the benefit of our architecture from backbone capacity, we also include text-only backbones that consume pre-extracted figure descriptions(usingg Claude-3, 3.uuee3..7-ooreee 7-Sonnet as the provider of Figure Describer).
And all models receive the same inputs:
PDF text extracted with PMMPDDFLoder(ttpps::// /ttp:::::///ttttte com com/pymupdf/
P PyMuPDF) for Section Agent.
Figures/tables detected with DocLayout- -OLL (Zhao et al., 2024) for Figure Agent.
For text-only LLMss, which cannot process images, figure descriptions generated using Claude-3. 3.ude-3.7-Sonnes as the provider of Figure Describer.
J RESOURCE CONSUMPTION
The training phase consumes GPU compute equivalent to 80 A100-GPU hours, whereas evaluating multiple models via the APIs offered by our provider, OpenRouter and Volcano Engine, incurs cost of more than $500.
K COMPUTATIONAL COST AND LATENCY
To help users assess practical deployability, we provide a detailed cost and latency analysis for generating a poster for average paper (based on Table 6: 17.104 text tokens and 7.25 images). We report metrics for GPT-4.1 (representing powerful closed-source models) and Qwen-2.5-V6 VL-BBBB (representing efficient open-source models). The average runtimes, using multi-threading and parallel
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optimizations, 209.32 seconds for GPT-4,1 and 55.61 seconds for Swen-2255VL--7B Below is an approximate breakdown of token consumption and cost, accounting for the retry factors measured during experiments. The cost of the Figure Extractor (YOLO detection) is excluded as it is fixed, local computation.
GPT-4.1 Breakdown
Figure Agent: Avg. input 391.84 (image tokens) 7.25; Avg. output 206.40 7.25.
Section Agent: Avg. imput (17, 104 x 2) 338 (prompt) + (206.40 7.25); Avg. output
2, 158.84. This stage has retry factor of 1.05x.
Orchestrate Agent: Avg. imput 17, 104 + 2, 158.84 + 1, 145 (prompt); Avg. output
2, 505.84. This stage has retry factor of 2.32
Total: This results in an average total 88,031 input tokens and 9,577 output tokens.
Based OpenRouter pricing for GPT-4.1 (S2/M input, $8/M output):
Cost = (88, 031/1, 000 x $2) + (9, 577/1, 000, 000 x $8) = $0.25 per poster.
Qwen-2.5-VI--75 Breakdown
Figure Agent: Avg. imput 391.84 (image tokens) 7.25; Avg. output 148.96 7.25.
Section Agent: Avg. input (17, 104 x 2) 338 (prompt) + (148.96 7.25); Avg. output = 479.500 This stage has retry factor of 1.03
Orchestrate Agent: Avg. imput 17, 104 + 1, 479.50 + 1, 145 (prompt); Avg. output
1, 637.47. This stage has retry factor 3.47x.
Total: This results in an average total 107,993 input tokens and 8,286 output tokens.
Based OpenRouter pricing for Qwen-2.5-VLL-7B (S0.20/M for both input/output): Cost = (107, 99/11, 000, 000 x $0.20) + (8, 286/1 000, 000 x $0.20) x $0.02 per poster.
This analysis demonstrates that P2P offers tunable cost perffommoe trade-off and is practically affordable for users, with costs ranging from a couple of cents to a quarter per poster depending on the chosen backbone model.
L AESTHETIC QUALITY EVALUATION
To complement our existing metrics, we introduce quantitative evaluation of aesthetic principles standard in graphic layout analysis (Deng al., 2017: Datta et al., 2006; Lu et al., 2015; Marchesotti et al., 1). We analyze the generated posters using suite of metrics measuring alignment, balance, symmetry, and other design fundamentals. As shown in Table 11, our best best-performing model produces layouts with strong balance and color harmony, providing an objective measure of aesthetic quality.
Table 11:
Quantitative aesthetic evaluation of generated posters. Results show mean + 95% CI. Higher is generally better. Best results are bold.
Metric
Higher is better?
Claude-3.7-Sonnet
GPT-44-mini-2222407711
Alignment
Yes
0.455 + 0.005
0.476 + 0.007
Balance (Left/Right)
Yes
0.894 0.014
0.744 # 0.032
Balance (Top/Bottom)
Yes
0.790 0.022
0.784 + 0.020
Symmetry
0.573 0.016
0.447 + 0.026
Whitespace Ratio
Moderate (target-dependent))
0.719 0.007
0.684 + 0.015
Rule of Thirds
Yes
0.666 + 0.015
0.668 + 0.017
Contrast (RMS)
Yes
0.257 0.007
0.247 + 0.009
Color Harmony
Yes
0.908 0.010
0.846 0.014
Simplicity (1 Clutter)
Yes
0.784 0.004
0.779 1 0.007
We define each metric used in evaluation follows:
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Alignment:
This metric quantifies the proportion of strong lines in the image that aligned with the horizontal and vertical axes. It is calculated by first computing the image gradients using Sobel filter. Then, it measures the magnitude-weighhe direction is within a small tolerance (+100) of 0, 90, or 180 degrees. This sum normalized by the total gradient magnitude across the entire image. A high score indicates strong presence of structured horizontal and vertical elements, which is a key principle of organized design.
sum of pixels whose gradient
Balance (Lelt/Right): This measures the distribution of visual weight between the left and right halves of the image. Visual weight is approximated by the density of edges detected by Canny edge detector. The score is calculated 1 [Ex- [EnlEnl sum of edge pixel intensities in the left and right halves, respectively, A score of 1.0 signifies perfect left-right balance.
E2+Ep where F., and F.R the
Balance (Top/Bottom): Similar to Left/Right Balance, this measures the equilibrium between the top and bottom halves of the image based on edge distribution. The score is calculated as [E Enl where Er and EB are the sum of edge pixel intensities the top and bottom halves. A score of 1.0 signifies perfect top- bottom balance.
Er+
Symmetry:
This metric assesses horizontal reflectional symmetry. It computed by calculating the normalized cross-correlation (cosine similarity) between the grayscale left half of the image and horizontally flipped version of the right half. The score ranges from 0 to I, where indicates perfect symmetry between the two halves.
Whitespace Ratio:
This metric calculates the proportion of the image area that constitutes the background or "whitespace." " The background color estimated by taking the median color of the image's 's border pixels in the perceptually uniform CIELAB color space. The score is the fraction of total pixels that are perceptually close (within a fixed threshold) to this estimated background color.
Rule of Thirds:
This evaluates the composition's adherence to the Rule of Thirds. It first identifies the center of mass (centroid) of the image's edge content. It then measures the shortest distance from this centroid one of the four "power points" (intersections of the rule-of- thirds grid). The score is inversely proportional to this distance, normalized so that centroid located directly on power point yields high score.
Contrast (RMS):
This measures the global contrast of the image, calculated as the Root Mean Square (RMS) contrast. It is defined as the standard deviation of the pixel intensities in the grayscale version of the image, normalized to [0, 1] range. A higher value indicates a wider dynamic range and stronger tonal separation.
Color Harmony:
This metric, derived from the 'hue thue_peak__g _ assesses the simplicity and harmony of the color palette. It computes histogram of the image's hue channel and sums the normalized frequencies of the two most dominant hues. A high score suggests the palette is concentrated around a small number of primary hues, often leading to a more cohesive and harmonious visual experience.
Simplicity Clutter): This metric quantifies the visual simplicity of the poster by scoring the inverse of clutter. Clutter is defined as a weighted sum of two components: 1) structural complexity, measured by the overall density of edges, and 2) color complexity, measured by the entropy of the image's 's saturation and value (brightness) channels. The final score is clutter, where higher value corresponds cleaner, less cluttered design.
M
THE FEATURES OF FINE-GRAINED POSTER EVALUATION
The Poster Evaluation pipeline offers several distinct advantages:
Each checklist item references specific elements from the official academic posters and corresponding papers, ensuring accurate evaluation aligned with the original author's intent.
Ground-Trun Alignment:
2. Domain-Specifie Emphasis:
The pipeline captures domain-specific expectations and con- ventions, which universal criteria may overlook, reflecting discipline-specific priorities.
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3. Essential Research Component Verification: Critical content such key figures, method- ology details, and conclusions is explicitly accounted for using detailed scoring mechanisms, ensuring comprehensive evaluation.
4. Human Preference Integration: Carefully calibrated by four human annotators, check- list item scores inherently encode domain expertise and human judgment regarding item significance and presentation quality.
N THE FEATURES OF HTML FORMAT
We compare the advantages of HTML for SVG and LaTeX:
Universal Accessibility and Portability: HTML posters can be easily viewed on any device with web browser, requiring no specialized software (unlike LaTeX, which needs compilation, or potentially specific viewers for complex SVGs).
HTML,, often combined with CSS and JavaScript, allows for the seamless integration interactive elements such as hyperlinks (to papers, datasets, author profiles), tooltips, expandable sections, or even embedded multimedia. This level of in- teractivity is more cumbersome achieve and less natively supported in LaTeX or static SVG.
Rich Interactivity:
Flexible and Modern Styling: CSS provides powerful and flexible control over the visual presentation, enabling modern, responsive, and aesthetically engaging designs that adapt to various screen sizes. This offers more design freedom than typical LaTeX layouts and better structural organization for complex content than single SVG.
Ease of Web Integration: As the native language of the web, HTML posters be effortlessly embedded into websites, shared via links, and are inherently well-suited for online conference platforms and digital dissemination.
EXAMPLES OF POSTER GENERATION
Examples of poster generation shown in Fig 7, Fig 8 and on. A wide range of examples features spanning different formats (e.g., landscape, portrait, multi-column, spanning columns) and scientific disciplines, effectively highlighting the P2P's flexibility and versatility.
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P2P: Automated Paper-to-Poster Goneration and Fine-Grained Benehmark
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Figure 7: The poster for this paper, powered by P2P.
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P РROMPT
Section Extraction
You expert in academic paper analysis.
Please analyze the paper content and identify the sections that should be included in the poster.
For each section, provide simple description what should be included. First, determine type of paper. If it is methodology research paper, focus on the method description, experimental results, and research methodology. If it is a benchmark paper, attention to task definitions, dataset construction, and evaluation outcomes. For survey/review papers, emphasize the significance of the field, key timelines or developmental milestones, critical theories and techniques. current challenges, and emerging trends. above are just references; the specific section names should depend on the paper's content.
Relevant sections comparison can be combined. There should not be too many sections. The acknowledgement and references section should not appear.
Return the result as JSON object with section names keys descriptions as values.
Ensure the JSON is flat, without nested dictionaries complex structures.
Example Format:
(JSON Format Example.)
Paper Content:
(Content of Paper.)
Image Description
You are an academic image analysis expert. Your task is to provide detailed descriptions of academic figures, diagrams, charts, images. Describe what the figure shows, its potential purpose in academic paper, and any key data or trends visible. The description should be concise and to the point, and should not exceed 100 words.
Image Data:
(Base64 PNG Image Data.)
Text-based Poster Generation
You helpful academic expert, who specialized in generating text-based paper poster, from given contents.
Figure Description:
(Figures Description.)
Paper Content:
(Content of Papex.)
If the content of the poster can be described by figures, the relevant text-based content must be simplified to avoid redundancy. Important mathematical formulas be appropriately placed to assist in understanding.
All sections should be detailed in markdown format. Do not use headings.
Image-based Poster Generation
You helpful academic expert, who is specialized in generating paper poster, from given contents and figures.
Figure Description: (Figures with Description.)
Text-based Poster:
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(Text-based Poster Content.)
Paper Content:
(Content of Papex.)
Help me inside insert figures into my poster content using my figure index as *[[ig-- [tr_ddssrrpptooongguure__ jinguuree_ndd"""""e"_
figure_inder index starts from 0 and MUST be an integer, and don't 't use any other string in the figure index Each figure can only used once, and its placement should precise and accurate. Use pictures and tables based on their importance.
Poster Rendering
You are professional academic poster web page creator and your task is to generate the HTML code for nicely laid out academic poster web page based the object provided.
Object Description:
The object contains several fields. Each field represents section, except for the title, author and affiliation fields. The field name is the title of the section and the field value is the Markdown content of the section.
image Markdown is given format ![alt_txxt, text, width = original_ width, height original height, aspect ratio aspect ritie] index).
HTML Structure:
Only generate the HTML code inside <body>, without any other things.
Do not tags other than <div>, <p>, <ol>, <ul>, <li>, <img>, <strong>, <em>.
Do not create sections that are not the object.
Place title, author and affiliation inside <div class=" poster-header">> Place title inside <div class="poster "oass="poter-tttle""" author inside <div class= "poster--uthor and affiliation inside <div class= plass="posterafflliation
Place content inside <div class="poster-content
Place each section inside <div class= "section">> >> Place section title inside <div ssectin- ttle"> and section content inside <div class=" class="section "section-conten"..
Use <p> for paragraphs.
Use <ol> and <li> for ordered lists, and <ul> and <li> for unordered lists.
Use <img src="image_iiae iinexx alt="alt_text">> for images.
Color Specification:
Do add styles other than color, background, border, box-shadow
Do not add styles width, height, padding, margin, font-size, font-weight, horder-radius,
Pick at least 2 colors from the visual identity of the affiliation. If there are multiple affiliations, consider the most well-known affiliation.
For example, Tsinghua University uses #660874 and #d93379, Beihang University uses #005bac and #003da6, Zhejiang University uses #003188 and #b01124. These are just examples, you must pick colors from the visual identity of the affiliation.
Add text and background color to poster header and section title using inline style. Use gradient to make the poster more bcautiful.
The text background color of cach section title should same.
Layout Specification:
Optionally, inside <div clas=="pstterccontemm... group sections into columns using <div dayle="display flex: gap: Irem"> and <div class= maas=="pster-column" style="fiee 1">.
You must determine the number and flex grow of columns make the poster more balanced. If the height of column too large,
sections into other columns.
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Optionally, inside <div dlas="sectionnconteed group texts images columns using <div style="display: flex; gap: 0,5rem >> and <div class= "scction-column" style= "flex: 1">.
For example, if there two images in two columns whose aspeet ratios are 1.2 and 2 respectively, the flex grow of two columns should be 1.2 and respectively, to make the columns have the same height.
Calculate the size of each image based on columns and aspect ratios. Add comment <!- width wwithh, height display_heights before each image.
Rearrange the structure and order of sections, texts and images to make the height of cach column in the same group approximately the same.
For example, if are too many images one section that make the height the column too large, group the images into columns.
DO NOT LEAVE MORE THAN 5% BLANK SPACE IN THE POSTER.
Existing Style:
(Existing CSS Style.)
Object:
(Poster Object.)
Q LIMITATIONS
While P2P demonstrates significant advances automated academic poster generation, several limitations warrant acknowledgement. Our approach currently optimizes for HTML rendering, which may present compatibility challenges in academic environments where LaTeX or PowerPoint formats remain prevalent. Additionally, the system's effectiveness is constrained by the visual understanding capabilities of underlying MLLMs, occasionally resulting in misinterpretation of complex scientific visualizations specialized notations. The multi-agent architecture, while comprehensive, introduces computational overhead that may limit accessibility for resource-constrained researchers.
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Mostl Seact Ao Taclirtinn Senci L.M Boodd Mirile
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Figure 8: Examples of academic poster design for Deng et al. (2024).
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Bench::nch: Towards Holistic Evaluation of Watermarks for Large Language Models
Skarssing Tuf, Viiay San" Yads Bai Jifas Ya, Lei Hine, Li
Deparmcntt of Corpuerr Scieece and Technology Tenghas Unowrray & School Corpeter Sciecce and lagnaoring Beitang Univerity
Introdaction
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We introdace nenel eppmach ewart für ampuriam. betooen watemnuiing mettedic (1) Detine waummrrting strongh as True Postive Rate (TFR), (2) Pertbon grid search dzeeriie loperpauretion
We evokunz wakmmarks sxing: (1) Detectire metroc Thie Portov Rine (TPR) for watemnarked was detection and Thee Negative Rute (TNR) uneetemerked diaallfcaiio, mesare (P1, RUL for quctalized bokks and cIPr4- for intruction followii todo.
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Figure 9: Examples of academic poster (Tu et al., 2023), powered by P2P.
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Quantitative comparison of within-sample heterogeneity scores for DNA methylation data
Michaol Scharer, Alme Nebel, Andre hrakk, Mrs Walte: Theras Louzan, Chriaaph Book, Fabias Miller, Marica Las Max Plaack Instase Inforrttss Startodd Univesit, Univenity Asania headeny Scleaoe Unowerry, Univensty Munkh
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The partenss frund wore distict fron those using everage reefty katis,, doneestviing infermaiion provided betenaencily
applications
rovel negilatory negiona ner cignoed by sandadd axslysle Score selector choele be talloeed to specffe reseadh quesises dan choreceristies
Figure 10: Examples of academic poster (Scherer et al., 2020). powered by P2P.
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Published as a conference paper at ICLR 2026
ProteinBERT: A Universal Deep-Learning Model of Protein Sequence and Function
Nadas Rondeet, 0frt, You Prkg. Nedw Reppopnn Michal Liaal" Comoponding The Hicrew Univesit of Jenaden lanel Deep Treling Ltt. Halfa, brneh Bes Guisen Univaslt of the Neges Taxel
conriruaioo)
Intesdaction
Prstraising Methodalog
Esperimental Resuls
While poscis accumiaae exprecetiill, unk then (ancione romnita challenging. reb-eepeviic desp bengaage motch tave revilaienteed aatual lingaage preceviing ong and roceetly opplled Tollogicll sagurses. However,
PootciBERT pretuised 105M svo-cclankot
smalles sise, PastinBRRT selieves parkmmuss ompamnble excooding moncch serres dirans benchmante
prokita iven Unille00 using two dinalannes tada:
Ridirectional Language
2. G. Aneotation Prediction: Predkcing 9,943 Oraingg
Modeling Recescring
urchieettrzs dasigted fir lurnar kingagee wilhout creaskerng pricct-ppecffic chunccanetzs We prasce Pheeinilfiit, skep bagrage model specifically designed prorctoo neglodslogy thai signtlicessll enyly inguore po clliciancy while mainaaning stec--fffhh--n ari performance.
comptial smin
raskmrll
apaaa
fxectivon, Nelogial
grecesos and aheelhlirr
loottions
Pretaieing dgsifiizaaly impeoead perfomanne
roorsay sracte sccersey
0.74 hanoingg detertic 0.66
ProtciaBERT Architectare
Uniiko staddad Thansonners, ProtisBERT
Retaros daal procesing peñls local reprosettation Ooogemaooeeecl and glotd iqpocsiatita (posin-lovdd) The archilsctrr compeace information dense byye global anneatioon.
Tinining farmrnal for 6:4 spucts, parisdically sericting betwore sequrece lenghte of 126, 512, and 1004 toloce to impeove groeniization The loss fucetion contined categerioll coos- eutropy sequecee recevery and coss-stingy
boyee processtty
prodicion
Bexchmarks
We evauuee ProeenlliT on mow dhrene benchrants oveting preeis snutemn pos-madiiioonl rxdiDcations Neplosked propeties
Longh pretraningg top to 6003 sampled) progressivets inprovel perfortanue sltuccaar prodiction
Global szectin visualaation reveils PaneiBBRRT atienian before after poprica prodecton exsee:
Protcis Structure:
Post-trantatieaal Medificalins
rqgiteo
Seamdary stractuee
predecion (catigorcal,
Signal pepaide prodiction
Diandar presiction (hirary, lncal)
Majer PTN (hinayy lual)
Ramoce henology devetton (omegotica)
Neuroppeiia (bingy, leal)
Key inaooatioza inciaal::
alool)
Global Atteetion:
Efficcecyy Only 16M
Fold (cungorical ginbal)
Propertio:
Linze complesity
pasarctins venia
Flacescence prodiction (ountinous, globe))
silfattovice
Leagth Pledbilliyy
modals
Stabiity psalitions
(contmaras, global
Patls (B-Lidd
Lixeur wouling
global gindd global)
erabiis
Crew-L enget Generalizatios
reprsseratons with
proteine of length
exchange
coomiiled inforration
ProtciBERT elfectidy gesaulico scrce poscis kegtla wifhusl signiiicnt performanes digrndation plaa
Conclusions
provides
poreis poodiction with soveral
lifictencs: Fire- tering skks only
Accmstilitt: Madh emaaler mobil she erobies broader
mizette everage
per beadamakk on
odestion pootiis
Secondary
Fre teettonnngg steetien
postiive sttation incroses siazifiantl dhe sigral peptide skanngs
Ulaverrattty
Per fires well accoss
diverse protein
Flestbille:
sequenecs any
Effecteeyy baafles
prodiction tigh AUC 93-0.00 acnoss al sequesoo legts
prodictiaa perfoctt best wiis 515-krath secsenco:
perfat pei formance
prodicios souches sna-
(-tl ALK: with 1804. knngh seguences
Defl pazdl) shows
bascite pultiass arsoa
proión sequereo.
Fins Finiigning inghte
parformance dagrabetie
Tais generlitation
poodig skifts atverton
Dufferett ancerios heads speciaiize is diffrestt ragkes (cau.
cinstuue lae nced to split
towasl aod the signal papside regionn
soqucnaa into
By providing a ceespact vei powezifal model the approches permiied hiips. www.lddd..nnnaaaaaaa www.eeaeaaatttaaaaaae.ooooooo pootii beri
Inioriat tnditiool
heginaingt)
peraat soquenas mudlys,, Prontetift
sansfonter models
available
This intagnszable atianice muchanies provida inight intn madel predicices and hasiggial ftatio.
Figure 11: Examples of academic poster (Brandes et al., 2022), powered by P2P.
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Published as a conference paper at ICLR 2026
Hypergraph- connectivity measures for signaling pathway topologies
Nexda Frneew, Acan Criee, Nt Mand, Amea tinz
Biiloyy Riputten and Eoeera Sclane Depurtert, fledd Dap
screce ICTAS caser for syaaus (ladagy of Enggeend
Migra
Introdaciie Frofine Sutnnest
rela Detasse Methol
Connediniy Analysi oo Rourices
gualing pabwaas deutth comples redacdlar
lerodace B volocatio démaca, pack parctteteed
compuned by acaes Reactera
noo re lcclle "coenscto" in pathory butnods
ripurto pragh presectirty D-camenn coammnoiity reguise all
sades 204 stard, compoand groble and
Thbrsaie mute ay moel en
now fai caden) le prowe reuiiin
kenoupaaie rimem inpurtoy prodo
upmaker dreant
comedte
rignaiing
mecess Drrected
ovenrted
(peas whi byynigg da vos andivily
sesce anle v, cadoo veloa live mave
will se% made pass ansieeelll yellani Teaggh
mnetiiyyy Reauucce ssing fou ieprosetiioa (Pa dinced graplo toakor medaaaio pd
la esseeiii billing the hypeigine connnaa for others
satwalt, Comppond and' partio grayns dinw
(Pts 5-1%, diani nan ato iim.
mudiraa oxmuctivit (18,0%% roudanis mads puind.
rellctey rentrnelly
cresuctiityy 5 codo conned over lle 20 elees Burkiaurio
hypeggeehe tiie
vemples rdahhaakon biscii groph Oep an
hew urlen of sagesible Buucka Ave distase ollene
(ogp aeci pany reli meniilip These rxp aepcenakkea col adl-areadinu bccurkor the acteotk (fa 2)-
pernisste comediiity (Fig eaiiliing sataced
keo) doneriielly alles ooaarrinyy pareess (Pig
yob pabhaa ruleiamtipp. pu.
Alter (ikenng bac shsptaerrdde Becinattc
shrved
eonningi teasaien oxnectiity as tacmasdd
To qualffy Aneasima letwoes pe drveps, docreaed anheree scur 415.11 meuarrs here well
moulles
aaig E-odiaation dittnce ln
skis.T) (84675)) 71:05 n PTE/ BAGPSTUS
palking evetaa The deverisake aerad auclly sed aokil paba-a ovatap and indiatuaaas conectiiiy
cieaacc i Ear warch alreaty
VaRdelie
hevilie whad comeutiviyy relse
STRING, de drabtur of faxciaa and poeds leanctiee
permutatin boak prasere peilaay ave Ayplbing cessue booe araace (Flg diy wese descce in mndiial aprocmatinn.
chandds. trone pan drveaazed to b-ovacrdd
Rearree dereadigtifane hightr STRING seoes
th purtas gragls (Fig 7V, For bue cach and sove chured, deceecded parn shoving pabrray cemnudivity sptarcill lighs foloe fawilaad and treaiiica cni saninn scew eu slonet tolndd werk pormunng mumner coreaced pen neaty Sieclar
Malegiicll Implications
deale providng
is dipaling
experesani en indveninng olancc concattaing pertocessly carltt kaniknally noutel prokinn.
bodagielly menengg oid res ane coneiiivyyy that bypoopuph warie momes Our opproac ii (1) Vanailio mitericlly ageifiaat crven palling infermer that dinutaal
popeets the spcnii restee nopemeti dee for perhoayy miaseealip, and (4) grodido tyslnnil sxcled to explare pathway drabuan The gepulogical henemtea ingremsic of ig sctorrs and poteraa jufrway woottal
andiing
goordaak othar hyperyygh lured alpetti mpttee
Our mertaaad incuttood dpeitices rebstmneepe
Mat NET Siyuaing yu 117% hyghar oberndl tehacee rocamed bed, where all dowarzun sado from Meil MET satway member SAA The lypmaggh visu lortic iFig ii morke lel fur Hopia (MPS)) fooes joo kial and IaF frams wocer fig fie llweys Enouge clord untetee contasion This diges will
ofheen-wosss brough RWAD poeas ar calir racher then the liget lnve Alichww ownlen the YOR sppttrr by fua legann partisaaae and Alleersiio thrrugh
afferna bor intermmeettd SMAD- dependert agnning covendes
Source Pathway MSTI
Source Pathway BMP
Source Pathway Activin
Nooks
500
Nodes
Reddes
500
ENKLITIIO PESCC
200-
100
Seunce Turget Pothensys
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Ripentie Graph
Hypergeaph
Sepninng by Niet Sggabng by MET
1035
by 8.78
Sigrallehyy Actite Sygnaleggyy TCyy
8.5
Syealin by Siguligggyy TG55
Figure 12: Examples of academic poster (Franzese et al., 2019), powered by P2P.
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Published as a conference paper at ICLR 2026
XFORMPARSER: A Simple and Effective Multimodal Multilingual Semi-structured Form
Parser
Niaia Chergt. Herg Zengtt Jaa Yag, Xarg L, Waice Zanu, Fei Lis, Rai Wa, Xasggaa Gues, Too Sun Xiajo Wi, Toagling LP, Zaojas t2+ CCSE, Belarg Univeriity Bejing Langage Cahare Litiveraty Bejina inferation Ssiance Tastndogyy Univarity Shordan Incllyen Stne Technology CocLtd.
Probiem and Medivattio
XPaemianeer Archllecture
Esperimeanal Resuts
Laggaa--ppeche Stae-tsingg XFo actievs prrvio 80Th modes.
XivenPamererppp etploys inditndd Transfrrnee urlinctare tha procsses faoe at medalitios
ang 55% impoosemant)
Teenorenk fee valrn
Image Sever
Text inputic Word crbcihings pracewed throagh rekenization
RT 91 15% mng Pl (19W66 ieprovmment)
Malti-lanazgge
(muined
Documerr Al, pansing aetiistrrenueed fimms remuiie
Poottin informttion: confitrre of blocks
challonging degete advacess in pre baaned asahiendal
moskle Carrna apprescho facs sipnificnt limitaties mukiiing paoinng
aeg F1 92% (mprovener))
Vasaa festures irags umbeediigs fiven docamen. repice
RE 95.89% ung Ei (+ (4.4444% improvenen)
underaal contects
vioral
diverse dacasan fonnas (PD7, Work insago) mokkle inui comprehoul socual centiall anal eparial Leyout
eleneers Eareeting stoctured informttioa Son
The actiteture comiats
LayestXLM M mittlngaal meckl thes ovmattalioo
Pro-cared makinsodal
Forts parsing is essonially an easlyy relatio minisg taak connecing Narsed Eatiy Recegniise (NER) layonts,
epprserations
Semuniic Eatity Recegaition chasificttinn types
warned docamonts complecity of corppio strcaure make it difficllts to represecr and uoderstan instratuued information using geseric rulcs.
QUISTION, ANSWER 011112)
Zero slxt Tmaater (waaae on FUNSD, sested on. oden lagasgo):
Reletion Extraction: Bi LSTM decader with leso'ual MLPs end balfie dissifie to idestii
SER: 71. 15%1%% #1 (1 ingrovemnnt)
RE $1, 81.18% P1 (+13 99% leprovenent)
Xivemeheeraadee wilrracee thas challengse by imagrating
eeroraic enttyy mncogniiee and rederiin scrmction frin
Satap
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uxiteed farnevock, inpooviag perfeenince
updizationa
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Tratning Methodslagy
schedaler
LINEAR
wacab dize
250002
Abssionn Study Jenoentates custibution cfecc compuser
Xivemt everr prtegrnon boo tube through joint miiiing
Max Steps
512
Johat Law Finecion:
Inital leaming rite
Band size
Se-5
R
by -1l Ranoving Soft Labels Pertonnnne decresses for bott Lado
Decoder
Lave
Trunning spociss
200
Where both compocetts ase cross entropy.
Exslution mecricc
ref, B.4
Optiral par parfermance achiovad with at spach
Warm-ap Soft Label Mecharinm::
dedign wiloon addiiood infreccee ovelhed,, ssiable fe deplyment coserno-oratriinnl cmntrmmetts.
estraction
The demsaatrakss robuat cnos-linasal
erarsliatios and aquric perfornance achau buck SER and It ailo.
epuch
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91.22 91.20
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30
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50
91.43
We intodaes I&DPumuSTT saperiied fan- using dabad sperifislly addroinn induarial iiem puring needs
Irtagraken
take joint
raged
lobel mechanion
Early unisiug Idada quick convergenez Mid bas traiiigg Sont thuts prostiing richer information
Corppittinn 563 Jan images ceross majo
BicLSTM dooder sggiffurlly exhances relation. cutration capisliies
inhocial aplicatio secturso
Achieven eote-of-tto-er performance without odtztinal infrrmac ovathedd
Languagec
Taggish
Tranitioa peried Gradually isceasing litel weigle
Partitie 422 mistng samples. 70 velidation samples, 70 tea sanple
Indentrilsstrangh Datasct
minl), tep- op peortttipppppoooooo som)
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LE LE Jani label (pppat
Divase relationkis types and exity catogocis
Li tobel (2--0) L.B hard Jabel ston)
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like GiP and carcfully varihal by hurraee
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6.1 pootingil no) 7.)
Seperior maata langugge spacific mitilegall and mon-ehet suntags
where the modistal abcl erboaking from SEIt ceabingg information starig between cados
Sigtifeent improvenctts but 74%)
Relation One one (dontrant), oce to three one man relatinsligs
(3.899) ansodit
Robee coaeltnaa pascslication capabilitios
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Centinans
XFommPaner denvastaoes thoughtfily designed acliisture with appropiate traingg mehhodaoog out.
The dwase Soloos the fommat of XPUND with cell
signifcaelyy cutpetioo mare complex meekl in
The abvation moayy decceevrttss remoniing decoder significeely impars RE perfrmanc (-1155) while zonoving sofi ldads decrosos ovenal mool
giantriiy ixclding obsotute soortiazes, acl
mittmndt mablingud decement undoreanding taik
effecivence.
Figure 13: Examples of academic poster (Cheng et al., 2024a), powered by P2P.
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Published as a conference paper at ICLR 2026
The Revolution of Multimodal Large Language Models: A Survey
Dovik Caflagre, Vederian Erecki", Laca Bavellrnit, Nichola: Moraclll", Fura Sant", Lorane Harakirr, Loranna flariii, Marcolla Garris and Riis Eazchiraa Univeraty Modera and Reggio Evills, Itay, Unoorsty Pfa, Iul, CNR, Itly
Introfuction and Siguifiencce
Mutrinedal Connection Appreackes
Donais Speeific Adagtatiies
Metizeedal Large Language (MLLM))
Sevend appoaahes excerged effectively connect legaage
inorasingly talkedd for apeciiiiiaed
saarlosdy y imagraing vinual lestall medaliion while providing dishege haard reuthonn with instudio felliwing copehilties Building on the sascess trazaleenes beaed
Mi Projections- Staightthewd approsc
usiag two liner layers to prgjett
Decement Anelysic
Embodied AE
eapéziliiks
mobliiess
reedel
(ca. LiaV 1.5, SPHINX)
Modele lik mILLLG-
shrough imager and sect The feld hos soen explostve goollt with cuecels like GPT-Y and Genas deccraoatiig sfat-effthe-at perfermanes gencniing
cnabling more manural humunnommpper intrrctiin
querios eieeeii self intraciSLiP))
O-Former Transfrme-sased adapters with (eg, B8
2 handle tecolieennivee
visual kapets
vical perception
plansing talk
inagrais
csocution
M Architutuure Companent
Crus- Attention Layare Additional easo attention
exining LLM loyees reclasions Planiags
undestasding
cotraction,
Suppart
aavigatie tolks
Medical
ML concer of thece axerttt componarta working comcont
Leppit simpicity laarr projetioes seraine highly
12. Mad and Oflin-
Modkls Uie Dappiin
Med odgeeed disgrotics
lncepeot totlle
Vireal Exceders- Prinaily ssing CLIP or EVA
usdesitaal
variatte estract mensistff vissal reprosstaticss from tragen
canditeeo
Tritsed queisiized
Proces mutttple camerr
Medel Backbone- Commonly from
dats
ELaMiA family. Vioaa, other in inbratioontteed model tua serve the resasrigg eegine
domin knowldlge
desisoon apport
Language Adapoers with Dhe text enlookling
Socuring opriniiztion ettens connection mechasismo and language model fine saring.
Cure Tasks and Applicationn
MLLMa cnod muliiplc tada screm the vazal- lmngang specnare
Challenges and Future Directios
Sevena) importee chillenges rectals fo advancing MLLMs.
Vasal Understaading:
Insqee
Goneration Vdditing
ceptioning and mulli- tum conversttio about
Queaios anmaering.
Test image
Mattinodal Retrieva-- Augarstte Generation:
Ethical C Coniiderations
geacurios deoogh
Provesting laarfidd Hinol, suppurpiits
imagce
aecaratias with
Eazezing RAG scchiques to
Virual Grunding
Bogion-lvel undozttanding
Trsmecticr-awe hased imaage
odding and inteleeved
addenntion
Incoppoute vissal
conter genaranie
Easeiin wtrry model stilty
lest image geneution
enckaring and localisatio (gnonding))
generatoo pipeitae
Lnd-to and traitabli
Halluctnation Correction:
Computational
Trrining dotledodegies
Expessied coediatts test derug's dullcnted
Vidco Underttaniigg
Mitgating the iealoney of MLLMM to peneraa fine infermatin about
Redacing the saheantid
Tempera renceing and compedeeniion
MLLMs typiallly usinol tloogh cides sinle Inteobaa approadho:
decoders
Des cheping nlust
for burking and inference
Single Stage Treining:
Two Stage Troining:
Frums-ledd fisture comettion coebhinad gooting speciaized
vatiicatie molazinta
detual accurney
Developina more
aechineneee and
Joindy teir imago-
Find align vinual
toiring stringles
tect porre and Insmuctions sinullasoualy
fottree with mat
loge uncunted CC
exzeelding spice using
Ardinesing those dhallcagos will enezial for building mone trutworrty and accessii muttooda All sysoess
Ofhin cxepkae
pemmccnntffeent line- mexing (PLITT) redluiqees
Seornd stage extonces
instrutioo wiiiingg with curkea
Leverages interleaved
dataach (ous, Webl.l,
MMCC4) modals
Insrect, LIN- Instuction)
diaaete (LLa/h-
capporting
Viead inttrsttion taning becomz the deminant perddggn eaabling model to fellow complex madianoda diections.
Figure 14: Examples of academic poster (Caffagni et al., 2024), powered by P2P.
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Published as a conference paper at ICLR 2026
Evidence for Evolutionary and Nonevolutionary Forces Shaping the Distribution of Human Genetie Variants near Transeription Start Sites
Gingpo inveripameeess di Bistsfomanca Bollogo Compuatteaak, Univensis deg) Stutt di Nipoil Teeeeoo IP, Paplle, Inly
Introduction
Distibution Variants Froqucncy
Evolutionary
Trasseiption elose
samording
repents
Using
scoms, selectics
regioes citiial
thek
lecatio relutive TS5s,
sabjot tunccipiin-sdadd mraagannc prososses
daczet putlen bdtwan CGI-TS and sCGI-TSS:.
(C TSS, foad arong prollin condaaioo
This aasly celeeee the gereno-wiikk dainbazion of smple neclovide polymopphrr (Shing the 10th
CGI- daswed is aigifiian poetsmna cllas le all
(pel 725) between CIEX soao and ISVF-kdia
thepancc clmess with matted depeasize fire
(dittinnce between nee corrnce varsee fispanctec)
regioes flanking TSS so reaimais gesdff vuribiiity flese fiactionally imporiant
somplex
200g
Converelyy (CG) TSS.
with betwor GERP and BVF- F-dlla in regiens 7700g from
poilivv coctlation (y-0.774)
showel posiiceal effeda only for variana, with seaaler 12-aii downsrran tareas Common vartante sboredd sharp pakk near OGi--1 1SS.
1555, eapaciing puriling oclothos provuvv
Methedsllggy
compliudly
arggentin
frouandeen comdirat with thiir cosociation with bozsckesskn getes fast requize strinen conensiikn.
fanctional regioen by proventing defeurices matarices reching
mookroorayy
We anabyurd 27 A83 Iaarnn TSS, camporiri
modoo cntttioe.
constraiors
into 155t
rusde islanda (CGI- 1886,, SPG)
and those wusids (xCG)- T8S,, 47%). We clawetee
-2 nillier Shits iiw four meegernc group miner allck fraquancy (MAF); rus (MAF34 59u10" rtádl (4.59-10 '0.01
cakadiai coemalleed hord
haaaasyy (BVP) We
examined commintions with mackessme poemoeing soocs, exelstooary sonservation (GERP) GC bussed gane convasioo (gROC), veriani iebeerionnsss (CADOS,
Narkooone Oceepancy and Rare Varients
We fiond stong poetin commiaaion harwarn madeessrme posilining and ras varkon teaslyy in CC))- T'5s ip 1,81), and weaker but aggiffoux contatio in CGl- cutitidd a percessed
We ideetiiiee bxased gene conversion (gBGC) Paauandis noe 158. CGl- 1886 lourd
178s maimained rilatvely coniiamn red recloncess
negeis combletze (o-d T54 betwoen gilC wooes and BVT dela withi the froe T00kp regits aound inderres faa
desilty These findings siggest that musscrpticn relond intatiood pheaseaau woald be laked la reduoed DMA
paticakely cocpetts prsionniaally raclvng GE kzkmnoogpzas GL-LIC homooryaete during grea comomioss turrbyy increadin the finquency variinss that night oherwie be selotee egains..
rxpuir clicknscy is paaloowes--eccaiial regiena,
Matea Mg Mirbo
with wathe nackoeore appooocinatlly 10% the of acadd DKA.
Variant Dehrerionsseon
Coucladens
Afabnis ofAD seorenoovalll dhi vuriants deac to TS8 azz potentidly more ikdciaiis thee more disars, dekterinenee rureadng swand tighe
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40

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Published as a conference paper at ICLR 2026
P2P: Automated Paper-to-Pood Poster Generation and Fine-Grained Benehmark
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Figure 16: The horizontal poster for this paper, powered by P2P.
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Published as a conference paper at ICLR 2026
P2P: Automated Paper-to-Poster Goneration and Fine-Grained Benchmark
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Figure 17: The vertical poster for this paper, powered by P2P.
42

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Published as a conference paper at ICLR 2026
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43


Reviews:
[{"review_id": "5PGWJtqoNL", "reviewer": "Reviewer_c6H3", "summary": "This work introduces a technically sound framework for automating academic poster generation, supported by a novel dataset and a well-designed evaluation benchmark. The paper is well-organized and addresses a meaningful problem in academic communication. However, several methodological details require further clarification to ensure the robustness and generalizability of the proposed approach.", "questions": "Some suggestions for improvement\n- Include statistical significance testing for key comparisons in Table 1 to strengthen claims of improvement.  \n- Expand the failure analysis section to provide qualitative examples of error correction via reflection loops.  \n- Discuss the computational overhead of the full P2P pipeline (with reflection) to help users assess practical deployability.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper makes three core contributions:  A multi-agent system that decomposes poster generation into specialized sub-tasks (figure extraction, content summarization, and layout assembly) with integrated reflection mechanisms for iterative improvement.  A large-scale instruction dataset containing over 30,000 examples, designed to support training and fine-tuning of models for the poster generation task.  A benchmark featuring fine-grained checklists and a universal evaluation metric, combining objective fidelity checks with learned subjective quality assessment.  \n\n- The modular design of the P2P framework demonstrates a clear understanding of the complexities involved in translating academic papers into visual summaries.  \n- The release of P2PINSTRUCT and P2PEVAL represents a valuable resource for the community, enabling reproducible research and direct comparison of future methods.  \n- The dual evaluation strategy (fine-grained and universal) effectively captures both factual accuracy and aesthetic quality, addressing a critical challenge in evaluating generative tasks.", "weaknesses": "1. The checker-reflection paradigm represents a significant architectural innovation, but its failure boundaries remain unclear. Could you provide a typology of errors that persist despite reflection cycles? Specifically, we're interested in cases where the system's compositional reasoning breaks down - for instance, when reconciling complex multi-panel figures with nuanced methodological descriptions. Understanding these limitations would help define the theoretical ceiling of this approach.\n2. The improvements from P2PINSTRUCT fine-tuning are clear, but we should examine whether this comes at the cost of creative diversity. Are fine-tuned models converging toward a \"P2P house style\" that prioritizes template adherence over adaptive design? Quantitative analysis of layout diversity and qualitative assessment of creative risk-taking in generated posters would help address concerns about potential over-standardization of academic communication.\n3. The recursive nature of P2PINSTRUCT generation raises fundamental questions about knowledge distillation in synthetic datasets. Beyond potential error propagation, we should examine whether this approach creates an \"imprinting bias\" where the dataset becomes increasingly optimized for the P2P framework's specific architectural assumptions. Could you provide analysis showing how the statistical distribution of generated examples evolves through this process and whether it converges toward a local optimum that might limit future model innovation?\n4.  While P2PEVAL's scale is commendable, the selection methodology for the 121 paper-poster pairs warrants deeper scrutiny. More importantly, have you investigated whether the benchmark exhibits structural biases toward certain paper formats (e.g., NLP papers with standardized experimental sections) that might disadvantage models trained on more diverse corpora? The field would benefit from understanding how benchmark composition affects comparative model performance beyond aggregate scores.\n5. The choice of XGBoost for universal scoring, while pragmatic, raises questions about the conceptual framework for evaluating creative artifacts. Have you conducted cross-architecture validation to test whether your learned metric captures fundamental design principles versus simply recognizing patterns characteristic of P2P-generated content? The community needs assurance that this evaluation approach generalizes to novel generation paradigms.", "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": "hfscNm2Bcg", "reviewer": "Reviewer_Fp4t", "summary": "This paper introduces a foundational ecosystem for automated academic poster generation, addressing key limitations in prior work, such as the failure to capture semantic richness and the lack of standardized benchmarks. The work is built on three main contributions:\n\n1.  **P2P**: A flexible, LLM-based multi-agent framework designed to generate high-quality, HTML-rendered academic posters directly from research papers.\n2.  **P2PINSTRUCT**: The first large-scale instruction dataset (30,000+ examples) specifically tailored for the paper-to-poster generation task.\n3.  **P2PEVAL**: A comprehensive benchmark featuring 1738 checklist items and a novel dual evaluation methodology (Fine-Grained and Universal) to assess poster quality.\n\nUnlike prior methods that often struggle with semantic nuances and lack systematic evaluation, this paper offers a complete system from generation to evaluation, providing a principled blueprint for assessing complex, creative AI-generated artifacts.", "questions": "### 1. Unclear Notations\nSome technical notations are ambiguous and hinder a clear understanding of the model's inputs and outputs.\n* L134: \"$v_i$ denotes the visual\". Does \"visual\" refer to the raw image file (e.g., a PNG) or a high-dimensional feature vector extracted from the image?\n* L142: \"a detailed structural schema ($S$)\". What is the data type of $S$? Is it a textual representation of the poster's structure (e.g., a JSON object) or a numerical feature vector?\n\n### 2. Lack of Detail on the P2PINSTRUCT Dataset\nThe paper introduces P2PINSTRUCT as a \"high-quality\" large-scale dataset, but more details are needed to fully assess its scope and quality. Section 2.2 mentions it's derived from the P2P framework's intermediate outputs, including prompting Claude for figure descriptions. The authors should provide more details on:\n\n* **Generation Process**: Beyond the brief mention of Claude, what were the specific prompts or processes used to create the 13,612 instruction-response pairs for the Section, Content, and HTML generators?\n* **Quality Validation**: Given its synthetic origin and the \"high-quality\" claim, what steps (if any) were taken to manually or automatically verify the factual accuracy, coherence, and overall quality of these generated pairs?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1.  **Innovative P2P Framework**\n    The P2P multi-agent architecture is a novel contribution to complex document transformation tasks. The inclusion of a **checker-reflection mechanism** is particularly strong, as it mimics the human design process of drafting and revision. This iterative approach helps ensure both scientific accuracy and structural integrity in the final output.\n\n2.  **Valuable Dataset (P2PINSTRUCT)**\n    The paper introduces P2PINSTRUCT, the first large-scale (30K+) instruction dataset specifically designed to train models for the complete paper-to-poster workflow. This resource could be highly valuable for the community and spur further research in this domain.\n\n3.  **Comprehensive Benchmark (P2PEVAL)**\n    The authors have clearly put significant effort into creating P2PEVAL, a new and comprehensive benchmark (1738 checklist items, 121 pairs). The **dual-evaluation framework** is a key strength, thoughtfully separating objective fidelity (via human-annotated checklists) from subjective quality (via a predictive scoring model). This provides a robust and multifaceted analysis of poster quality.", "weaknesses": "### 1. Lack of Detail on P2P Checker Mechanisms\nThe paper states, \"Each agent operates in conjunction with a dedicated checker module that triggers a reflection loop if its output fails to meet quality standards\". However, the manuscript provides no concrete details on how these critical checker modules are implemented.\n* What is the core component of each checker? Is it an LLM-as-a-judge, a set of programmatic rules, or a trained classifier?\n* **Figure Checker**: The paper mentions \"an initial confidence threshold\". What is this threshold value, and how was it determined?\n* **Section Checker**: This checker reportedly evaluates four complex metrics: coherence, completeness, faithfulness, and correct referencing. How are these metrics automatically and objectively measured?\n* **Poster Checker**: How is this checker implemented to evaluate \"layout aesthetics and structural integrity\"?\n\n### 2. Missing Implementation Details for P2P Agents\nThe paper describes the multi-agent architecture but omits all implementation details about the agents themselves.\n* What specific models are used for the main agents ($\\mathcal{A}_{Fig}$, $\\mathcal{A}_{Sec}$, $\\mathcal{A}_{Orch}$)?\n* What models are used for their generative sub-components (e.g., Figure Describer, Section Generator, Content Generator, HTML Generator)? This is crucial for reproducibility.\n\n### 3. Contradictory Description of Fine-Grained Poster Evaluation\nI am very confused about how the Fine-Grained Poster Evaluation is actually performed, as the paper seems to contradict itself.\n* In one section, the paper states it uses \"LLM-as-a-Judge... to score generated posters against detailed, human-annotated checklists\".\n* However, in Section 3.2.1, it explicitly states, \"This is a **deterministic scoring methodology, not a trained model**\".\n* These two statements are mutually exclusive. This core evaluation method must be clarified.", "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": "NIKxlA3xvK", "reviewer": "Reviewer_HwQu", "summary": "This paper addresses the time-consuming task of creating academic posters from research papers. The authors present a comprehensive, three-part contribution:\n\n1. **P2P:** A flexible, LLM-based multi-agent framework that generates HTML-rendered posters. It employs three specialized agents (Figure Agent, Section Agent, Orchestrate Agent) and a novel \"checker-reflection\" mechanism to iteratively refine the output.\n2. **P2PEVAL:** A new, comprehensive benchmark for evaluating generated posters. Its core innovation is a dual-evaluation methodology, assessing both **objective fidelity** (via a \"Fine-Grained\" score based on 1738 human-annotated checklist items) and **subjective quality** (via a \"Universal\" score from an XGBoost model trained to emulate human aesthetic preferences).\n3. **P2PINSTRUCT:** The first large-scale (30,000+ examples) instruction dataset for the paper-to-poster task, derived from the intermediate outputs of the P2P pipeline.\n\nExperiments show the P2P system, particularly when powered by Claude-3.7-Sonnet, outperforms 35 other models. Notably, human evaluations found the P2P-generated posters to be competitive with, and in many cases preferred to, the original posters created by the authors themselves.", "questions": "1. In Table 1, models fine-tuned on P2PINSTRUCT (like Qwen3-P2P) achieve the absolute best scores on ROUGE and BERTScore. However, the improvement on the paper's own flagship metrics, Fine-Grained and Universal, is limited. Does this mean there is a bias in the way this dataset was constructed?\n2. Could the authors more clearly state in the paper that the purpose of the \"Fine-Grained\" metric is to measure fidelity to \"the key content selected by the authors\" rather than imitation of \"the layout design of the authors\"? \n3. The \"checker-reflection\" mechanism is interesting. Can you provide a specific example? For example, when the Poster Checker detects a \"spacing imbalance,\" what kind of \"reflection\" instructions does it send to the Orchestrate Agent to correct it? How many iterations does this process typically take?\n4. Could the authors provide an ablation experiment or justification to demonstrate that the MLLM-XGBoost process is superior to the simpler, end-to-end MLLM-as-a-Judge baseline?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The authors have built an end-to-end solution, from the generation framework (P2P) to the large-scale data for training (P2PINSTRUCT) and a novel benchmark for evaluation (P2PEVAL). This is a significant and impressive amount of work.\n2. The authors conducted comprehensive experiments, including evaluation on 35 models and a human preference study. The P2P framework is shown to be effective via an ablation study.", "weaknesses": "1. There is a lack of analysis for the effectiveness of the P2PINSTRUCT dataset. \n2. The methodology of this metric measures fidelity to a specific human instance, which itself may be suboptimal (as shown by human preference research). The authors should discuss this tension between \"imitation\" and \"fidelity\" more openly.\n3. The two-step, MLLM-Featurizer-plus-XGBoost-Regressor methodology for the universal score is not justified over simpler, more direct MLLM-as-a-Judge approaches. This adds a \"Rube Goldberg\"-like complexity without a clear benefit.", "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": "4cb4UdlaqD", "reviewer": "Reviewer_4iW7", "summary": "This paper presents a comprehensive and well-executed study on the automated generation of academic posters from research papers. The work is timely, addressing a clear need in the academic community. The proposed P2P framework, the P2PINSTRUCT dataset, and the P2PEVAL benchmark constitute a significant contribution to the field of document AI and scientific communication. The paper is generally well-written and the experimental evaluation is extensive. However, some aspects of the methodology and presentation could be strengthened to improve clarity and impact.", "questions": "1.  The P2PINSTRUCT dataset is generated by the P2P framework itself. Could there be a risk of a \"closed loop\" where the dataset inherits and potentially amplifies any systematic biases or error modes of the P2P pipeline? How was the quality of this synthetic data validated?\n2.  The Universal Evaluation uses an XGBoost model to map LLM-generated criterion scores to a human holistic score. Was there any exploration into using a fine-tuned LLM as the holistic judge directly? What was the rationale for choosing a simpler model like XGBoost over an LLM for this final aggregation step? Furthermore, how does the performance of this XGBoost model generalize to posters generated by future, potentially very different, methods not seen during its training?\n3.  The checker modules are a critical component for iterative refinement. Could you provide more concrete examples of typical failure cases that the checkers identified and how the reflection loops successfully corrected them? Conversely, are there systematic errors or types of source paper content that the checkers consistently fail to catch, leading to persistent issues in the final poster? \n4.  The P2PEVAL benchmark is constructed from 121 paper-poster pairs. Could you provide more detail on the stratification and diversity of this benchmark? For instance, how are papers from different scientific fields (e.g., computer science, NLP, CV) and types (e.g., methodological, theoretical, survey) distributed? Is there a risk that the benchmark over-represents certain fields, and how might this affect the fair evaluation of a model's general capabilities?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.   This is one of the first works to systematically tackle end-to-end academic poster generation using an LLM-based multi-agent framework. The problem is relevant and underexplored. The creation of P2PINSTRUCT and P2PEVAL fills a critical gap, providing essential resources for future research in this domain.\n2.   The dual-evaluation framework of P2PEVAL is a major strength. The argument for decoupling objective fidelity (Fine-Grained) from subjective quality (Universal) is well-motivated. The use of human-annotated checklists and a learned model for holistic scoring is sophisticated and convincing. The high R² score (0.92) for the Universal Evaluation model adds credibility.\n3.  The paper evaluates a vast number of models (35), including both closed-source and open-source LLMs/MLLMs. The ablation studies effectively demonstrate the contribution of each component (multi-agent, figure describer, reflection). The human preference evaluation and analysis of output formats (HTML vs. SVG/LaTeX) provide valuable practical insights.\n4.   The authors commit to releasing code, datasets (P2PINSTRUCT, P2PEVAL), and detailed prompts, which will greatly facilitate reproducibility and future work. The ethical considerations and resource reporting are appropriate.", "weaknesses": "1.  While the paper extensively compares different LLM backbones, it lacks a direct comparison to prior dedicated poster-generation systems (e.g., Qiang et al., 2019; Jaisankar et al., 2024 - Postdoc). A qualitative or quantitative comparison against such established, non-LLM-based methods would better situate the performance of P2P and highlight its advancements. The current comparison feels more like a model benchmark than a system-level comparison.\n2.  The relationship between the P2P *framework* and the specific LLMs used within it (e.g., Claude-3.7-Sonnet) needs to be clarified. Is P2P best viewed as a prompting/orchestration strategy that can be deployed on top of any powerful LLM? The results in Table 1 show \"P2P\" achieving high scores, but this seems to be the result of using the P2P *pipeline* with Claude-3.7-Sonnet as the core LLM. The fine-tuned models (Qwen3-P2P) are a separate contribution. This distinction should be made more explicit to avoid confusion.\n3.   The paper convincingly argues for HTML's advantages (flexibility, interactivity). However, the limitation regarding the prevalence of LaTeX/PPT in academia is buried in the appendix (Section M). This is a significant practical constraint that deserves more prominent discussion in the main text. How might P2P be adapted or extended to output these more traditional formats? Are there plans for a PDF/LaTeX export feature?\n4.   The multi-agent framework with iterative reflection loops is computationally expensive. While resource consumption is mentioned in Appendix G, a more detailed discussion in the main limitations section about the inference-time cost and latency of the full P2P system would be valuable for potential users.", "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)