PosterForest: Hierarchical Multi-Agent Collaboration for Scientific Poster Generation
This work solves the problem of generating high-quality scientific posters automatically for researchers, though it appears incremental as it builds on prior methods by adding hierarchical and multi-agent features.
The paper tackled automated scientific poster generation by introducing a training-free framework that addresses hierarchical structure and semantic integration, resulting in posters that outperform baselines and achieve quality closest to expert designs with superior information preservation and user preference.
We present a novel training-free framework, \textit{PosterForest}, for automated scientific poster generation. Unlike prior approaches, which largely neglect the hierarchical structure of scientific documents and the semantic integration of textual and visual elements, our method addresses both challenges directly. We introduce the \textit{Poster Tree}, a hierarchical intermediate representation that jointly encodes document structure and visual-textual relationships at multiple levels. Our framework employs a multi-agent collaboration strategy, where agents specializing in content summarization and layout planning iteratively coordinate and provide mutual feedback. This approach enables the joint optimization of logical consistency, content fidelity, and visual coherence. Extensive experiments on multiple academic domains show that our method outperforms existing baselines in both qualitative and quantitative evaluations. The resulting posters achieve quality closest to expert-designed ground truth and deliver superior information preservation, structural clarity, and user preference.