用多智能体框架读全文生成相关工作，更懂引用间关系。
Select, Read, and Write: A Multi-Agent Framework of Full-Text-based Related Work Generation
- 三智能体协作：选读、消化、撰写，共享记忆持续优化
- 图感知选读策略提升阅读顺序，使引用关系捕捉更准
- 在三个基模型上均达最优，适合写论文时自动生成参考文献
自动相关工作生成（RWG）可节省撰写文献综述的时间。但现有方法因仅使用参考论文的有限部分作为输入，且对每篇论文孤立解释，导致理解浅层、难以捕捉引用间的关联。为此，本文聚焦全文本基于的RWG任务，提出一种新型多智能体框架。该框架包含三个智能体：选择器决定下一步阅读的段落，阅读器消化内容并更新共享工作记忆，撰写器基于最终整理的记忆生成文献综述。为更好建模引用间关系，还提出两种图感知的选择策略，以图结构约束优化阅读顺序。大量实验表明，该框架在三种基模型和多种输入配置下性能稳定提升，图感知选择器显著优于其他方案，达到当前最佳效果。代码与数据已开源。
原文摘要 · Abstract (English)
Automatic related work generation (RWG) can save people's time and effort when writing a draft of related work section (RWS) for further revision. However, existing methods for RWG always suffer from shallow comprehension due to taking the limited portions of references papers as input and isolated explanation for each reference due to ineffective capturing the relationships among them. To address these issues, we focus on full-text-based RWG task and propose a novel multi-agent framework. Our framework consists of three agents: a selector that decides which section of the papers is going to read next, a reader that digests the selected section and updates a shared working memory, and a writer that generates RWS based on the final curated memory. To better capture the relationships among references, we also propose two graph-aware strategies for selector, enabling to optimize the reading order with constrains of the graph structure. Extensive experiments demonstrate that our framework consistently improves performance across three base models and various input configurations. The graph-aware selectors outperform alternative selectors, achieving state-of-the-art results. The code and data are available at https://github.com/1190200817/Full_Text_RWG.
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