用分层协作机制自动生成逻辑清晰、视觉均衡的学术海报
PosterForest: Hierarchical Multi-Agent Collaboration for Scientific Poster Generation
- 构建海报树结构，统一表达内容层级与图文语义
- 多智能体递归优化，从整体布局到局部元素逐步精炼
- 无需训练或领域监督，适合科研人员快速生成专业海报
自动化生成学术海报需要理解文档的层级结构并进行连贯的内容-版式规划。现有方法通常依赖扁平化摘要，或分别优化内容与版式，导致信息丢失、逻辑薄弱和视觉失衡。我们提出PosterForest，一种无需训练的科学海报生成框架。该方法引入海报树（Poster Tree）作为结构化中间表示，捕捉多层级的文档层次与跨模态语义。基于此表示，内容与版式智能体执行分层推理与递归优化，逐步从全局组织细化到局部构图。联合优化显著提升语义连贯性、逻辑流与视觉和谐性。实验表明，PosterForest在自动与人工评估中均优于现有方法，且无需额外训练或领域特定监督。
原文摘要 · Abstract (English)
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.
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