用多智能体生成有证据支持的医学新假说，还能迭代优化和评估未来潜力。
BioDisco: Multi-agent hypothesis generation with dual-mode evidence, iterative feedback and temporal evaluation
- 多智能体协作，结合知识图谱与文献检索双模式证据。
- 通过反馈循环迭代优化，生成假说新颖性提升37%以上。
- 适合生物医学研究者快速探索前沿科学假设。
发现新假说是科学研究的核心，但海量复杂信息易导致认知过载。现有自动化方法常难以生成兼具新颖性与证据支撑的假说，缺乏稳健的迭代优化机制，且极少进行未来发现潜力的严谨时间评估。为此，我们提出BioDisco，一种多智能体框架，融合基于语言模型的推理与双模式证据系统（生物医学知识图谱与自动文献检索），确保假说的可验证性；引入内部评分与反馈回路实现迭代优化；并通过开创性的时序评估与人类评估，结合布拉德利-泰瑞配对比较模型，提供统计学上可靠的性能判定。实验表明，相比消融配置和通用生物医学代理，BioDisco在假说新颖性和重要性上均显著更优。该框架具备高度灵活性与模块化设计，支持自定义语言模型或知识图谱的无缝集成，仅需少量代码即可运行。
原文摘要 · Abstract (English)
Identifying novel hypotheses is essential to scientific research, yet this process risks being overwhelmed by the sheer volume and complexity of available information. Existing automated methods often struggle to generate novel and evidence-grounded hypotheses, lack robust iterative refinement and rarely undergo rigorous temporal evaluation for future discovery potential. To address this, we propose BioDisco, a multi-agent framework that draws upon language model-based reasoning and a dual-mode evidence system (biomedical knowledge graphs and automated literature retrieval) for grounded novelty, integrates an internal scoring and feedback loop for iterative refinement, and validates performance through pioneering temporal and human evaluations and a Bradley-Terry paired comparison model to provide statistically-grounded assessment. Our evaluations demonstrate superior novelty and significance over ablated configurations and generalist biomedical agents. Designed for flexibility and modularity, BioDisco allows seamless integration of custom language models or knowledge graphs, and can be run with just a few lines of code.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。