Adaptive Memory and Reflection Multi-Agent System for Medical Question Answering· 医疗问答中的自适应记忆与反思多代理系统
Accurate and responsible medical question answering (QA) is important in healthcare, where complex cases require factual knowledge and nuanced reasoning. Existing medical QA systems, typically based on single-agent architectures and static retrieval, often lack adaptability, persistent memory, and structured decision-making. This work introduces an adaptive memory and reflection (AMR) agentic system, a multi-agent framework in which specialized agents use dedicated memory and reflection-based feedback to retrieve relevant prior cases and improve subsequent reasoning. Complexity assessment routes questions through solo, collaborative, or escalated workflows, while consensus and ethical overseer modules support reasoning consolidation and output review. Evaluation on MedQA and MedMCQA demons
引入自适应记忆与反思的多代理系统，提升医疗问答的准确性与责任感。
- 核心方法
- 构建多代理框架，包括专用内存和反思反馈机制，以及复杂性评估、共识和伦理监督模块。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 现有医疗问答系统在处理复杂案例时缺少适应性、持久记忆和结构化决策能力。
- 关键实验
- 在MedQA和MedMCQA数据集上进行评估，未提供具体实验细节。
- 主要贡献
- 提出AMR代理系统，有效提高医疗问答的精准度和责任感，增强系统的适应性和持久记忆能力。
- 意义与局限
- 对医疗领域的智能化问答具有重要意义，但需进一步验证其在真实医疗环境中的效果。