# PosterForest: Hierarchical Multi-Agent Collaboration for Scientific Poster Generation

arXiv: https://arxiv.org/abs/2508.21720 · HTML: https://mlsanity.com/paper/2508.21720/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 43 references that are in the map.
- First posted on arXiv 29 Aug 2025.

## Builds on (most cited first, 30 of 32)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://mlsanity.com/paper/2211.12588/) (2022)
- [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023)
- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://mlsanity.com/paper/2204.08387/) (2022)
- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generation](https://mlsanity.com/paper/2303.17189/) (2023)
- [Chain of Agents: Large Language Models Collaborating on Long-Context Tasks](https://mlsanity.com/paper/2406.02818/) (2024)
- [LayoutDM: Discrete Diffusion Model for Controllable Layout Generation](https://mlsanity.com/paper/2303.08137/) (2023)
- [OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation](https://mlsanity.com/paper/2505.23885/) (2025)
- [Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation](https://mlsanity.com/paper/2401.06591/) (2024)
- [LayoutTransformer: Layout Generation and Completion with Self-attention](https://mlsanity.com/paper/2006.14615/) (2020)
- [Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents](https://mlsanity.com/paper/2410.13185/) (2024)
- [PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides](https://mlsanity.com/paper/2501.03936/) (2025)
- [DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception](https://mlsanity.com/paper/2410.12628/) (2024)
- [Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning](https://mlsanity.com/paper/2504.17192/) (2025)
- [Attribute-conditioned Layout GAN for Automatic Graphic Design](https://mlsanity.com/paper/2009.05284/) (2020)
- [Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers](https://mlsanity.com/paper/2505.21497/) (2025)
- [PosterMaker: Towards High-Quality Product Poster Generation with Accurate Text Rendering](https://mlsanity.com/paper/2504.06632/) (2025)
- [ResearchTown: Simulator of Human Research Community](https://mlsanity.com/paper/2412.17767/) (2024)
- [Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models](https://mlsanity.com/paper/1702.06228/) (2017)
- [P2P: Automated Paper-to-Poster Generation and Fine-Grained Benchmark](https://mlsanity.com/paper/2505.17104/) (2025)
- [PosterO: Structuring Layout Trees to Enable Language Models in Generalized Content-Aware Layout Generation](https://mlsanity.com/paper/2505.07843/) (2025)
- [SciPostLayout: A Dataset for Layout Analysis and Layout Generation of Scientific Posters](https://mlsanity.com/paper/2407.19787/) (2024)
- [PosterSum: A Multimodal Benchmark for Scientific Poster Summarization](https://mlsanity.com/paper/2502.17540/) (2025)
- [Neural Content Extraction for Poster Generation of Scientific Papers](https://mlsanity.com/paper/2112.08550/) (2021)

## Built on by (most cited first, 8 of 8)

- [AI for Auto-Research: Roadmap & User Guide](https://mlsanity.com/paper/2605.18661/) (2026)
- [AutoDesign: Meta-Harness Optimization for Long-Horizon Agentic Design](https://mlsanity.com/paper/2608.13560/) (2026)
- [APEX: Academic Poster Editing Agentic Expert](https://mlsanity.com/paper/2601.04794/) (2026)
- [VideoAgent: Personalized Synthesis of Scientific Videos](https://mlsanity.com/paper/2509.11253/) (2025)
- [PaperX: A Unified Framework for Multimodal Academic Presentation Generation with Scholar DAG](https://mlsanity.com/paper/2602.03866/) (2026)
- [Demo2Tutorial: From Human Experience to Multimodal Software Tutorials](https://mlsanity.com/paper/2606.03951/) (2026)
- [ResearchStudio-Reel: Automate the Last Mile of Research from Paper to Poster, Video, and Blog](https://mlsanity.com/paper/2607.04438/) (2026)
- [From Transient Prompts to Persistent Control: Scientific Poster Generation via Recursive Semantic-Geometric Contracts](https://mlsanity.com/paper/2609.17326/) (2026)

## Abstract (arXiv)

Automating scientific poster generation requires hierarchical document understanding and coherent content-layout planning. Existing methods often rely on flat summarization or optimize content and layout separately. As a result, they often suffer from information loss, weak logical flow, and poor visual balance. We present PosterForest, a training-free framework for scientific poster generation. Our method introduces the Poster Tree, a structured intermediate representation that captures document hierarchy and visual-textual semantics across multiple levels. Building on this representation, content and layout agents perform hierarchical reasoning and recursive refinement, progressively optimizing the poster from global organization to local composition. This joint optimization improves semantic coherence, logical flow, and visual harmony. Experiments show that PosterForest outperforms prior methods in both automatic and human evaluations, without additional training or domain-specific supervision.