标题： BioDisco：具有双模式证据、迭代反馈和时间评估的多智能体假设生成 显示英文标题

标题： BioDisco: Multi-agent hypothesis generation with dual-mode evidence, iterative feedback and temporal evaluation

摘要： 识别新的假设对于科学研究至关重要，但这一过程可能因可用信息的庞大数量和复杂性而被淹没。 现有的自动化方法常常难以生成新颖且有证据支持的假设，缺乏稳健的迭代优化，并且很少进行严格的时序评估以预测未来的发现潜力。 为了解决这个问题，我们提出了BioDisco，这是一个多智能体框架，利用基于语言模型的推理和双模式证据系统（生物医学知识图谱和自动文献检索）来实现有依据的新颖性，集成了内部评分和反馈循环以进行迭代优化，并通过开创性的时序和人工评估以及Bradley-Terry配对比较模型来验证性能，以提供统计学依据的评估。 我们的评估表明，在新颖性和重要性方面优于代表现有智能架构的消融配置。 设计上具有灵活性和模块化，BioDisco允许无缝集成自定义语言模型或知识图谱，并且只需几行代码即可运行。 我们预计研究人员会将这个实用工具作为发现新假设的催化剂。 显示英文摘要

摘要： 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 representative of existing agentic architectures. 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. We anticipate researchers using this practical tool as a catalyst for the discovery of new hypotheses.