用多智能体系统自动整合癌症研究证据，全程可追溯可审计。
Biomedical reasoning in action: Multi-agent System for Auditable Biomedical Evidence Synthesis
- 分角色智能体并行处理不同数据源的证据
- 证据到结论全程可追踪，输出一致性提升显著
- 适合需要透明推理的科研人员和医学研究人员
我们提出M-Reason，一个面向生物医学领域（聚焦癌症研究）的透明化、基于智能体的推理与证据整合演示系统。该系统利用大语言模型（LLMs）与模块化智能体编排技术，实现跨多样化生物医学数据源的证据检索、评估与综合。每个智能体专注于特定证据流，支持并行处理与细粒度分析。系统强调可解释性、结构化报告与用户可审计性，确保从原始证据到最终结论的完整可追溯性。我们讨论了智能体专业化、系统复杂度与资源消耗之间的权衡，以及确定性代码在验证中的集成。开放的交互式界面使研究人员能直接观察、探索与评估多智能体工作流程。评估显示系统在效率与输出一致性上均有显著提升，表明M-Reason不仅是高效的证据合成工具，也是科学研发中稳健多智能体大模型系统的测试平台，项目地址：https://m-reason.digitalecmt.com。
原文摘要 · Abstract (English)
We present M-Reason, a demonstration system for transparent, agent-based reasoning and evidence integration in the biomedical domain, with a focus on cancer research. M-Reason leverages recent advances in large language models (LLMs) and modular agent orchestration to automate evidence retrieval, appraisal, and synthesis across diverse biomedical data sources. Each agent specializes in a specific evidence stream, enabling parallel processing and fine-grained analysis. The system emphasizes explainability, structured reporting, and user auditability, providing complete traceability from source evidence to final conclusions. We discuss critical tradeoffs between agent specialization, system complexity, and resource usage, as well as the integration of deterministic code for validation. An open, interactive user interface allows researchers to directly observe, explore and evaluate the multi-agent workflow. Our evaluation demonstrates substantial gains in efficiency and output consistency, highlighting M-Reason's potential as both a practical tool for evidence synthesis and a testbed for robust multi-agent LLM systems in scientific research, available at https://m-reason.digitalecmt.com.
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