发表机构
East China Normal University; City University of Hong Kong（华东师范大学; 香港城市大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
EMR通过分层临床经验库和自动经验挖掘，使医疗多智能体系统能从诊断成败中自我进化，在基准上超越现有方法，并支持跨专科与跨模型迁移。
AI 中文摘要
大型语言模型（LLM）驱动的多智能体系统在复杂临床推理中展现出潜力，然而现有方法依赖静态策略且缺乏持久的临床记忆，无法从先前的诊断成功与失败中自我进化。我们提出EMR，一种通过经验挖掘与复用实现自我进化的医疗多智能体系统。EMR引入了一个分层临床经验库，将积累的知识组织为三个层级：临床原则、诊断模式和代表性案例。在推理过程中，EMR模拟多学科会诊：规划智能体协调各专科智能体进行专业推理，而总结智能体将其分析综合为最终决策。关键的是，EMR从多智能体推理轨迹中自动提取正确的诊断见解和与失败相关的警告，增量更新经验库以指导未来病例。在医疗推理基准上的实验表明，EMR持续优于最先进的医疗多智能体基线。进一步分析揭示，分层经验实现了跨专科泛化和跨不同LLM后端的迁移，提供了一种可扩展且内在的解决方案。
英文摘要
Large language model (LLM) driven multi-agent systems have shown promise in complex clinical reasoning, yet existing approaches rely on static strategies and lack persistent clinical memory, preventing self-evolving from prior diagnostic successes and failures. We present EMR, a self-evolving medical multi-agent system via Experience Mining and Reuse. EMR introduces a hierarchical clinical experience library that organizes accumulated knowledge into three levels: clinical principles, diagnostic patterns, and representative cases. During inference, EMR emulates multidisciplinary consultation: a planner agent coordinates domain-specific department agents for specialized reasoning, while a summary agent synthesizes their analyses into a final decision. Critically, EMR automatically extracts correct diagnostic insights and failure-related warnings from multi-agent reasoning trajectories, incrementally updating the experience library to guide future cases. Experiments on medical reasoning benchmarks demonstrate that EMR consistently outperforms state-of-the-art medical multi-agent baselines. Further analysis reveals that the hierarchical experience enables cross-specialty generalization and transfer across diverse LLM backbones, offering a scalable and in
