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Adaptive Memory and Reflection Multi-Agent System f... | AI Research

Key Takeaways

  • Adaptive Memory and Reflection Multi-Agent System for Medical Question Answering introduces a multi-agent framework designed to improve the accuracy and reli...
  • 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.
  • 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 demonstrates strong performance compared with several baselines.
Paper AbstractExpand

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 demonstrates strong performance compared with several baselines. Ablation studies show that combining agent-specific memory, reflection, and external retrieval yields the strongest performance. These findings highlight the potential of structured memory and feedback for developing more trustworthy medical agents. The source code is publicly available at this https URL .

Adaptive Memory and Reflection Multi-Agent System for Medical Question Answering introduces a multi-agent framework designed to improve the accuracy and reliability of medical question answering (QA). By moving away from static, single-agent architectures, the system uses a structured, graph-based approach to route medical questions based on their complexity, allowing for specialized reasoning, persistent memory, and ethical oversight.

A Structured Approach to Medical Reasoning

The system, developed by Pradeep Murugesan, Luoxiao Yang, Xueli Chen, and Xinqi Fan, organizes medical QA as a pipeline rather than a single-prompt task. A "Moderator" agent assesses the complexity of each incoming question and routes it through one of three workflows:

  • Low complexity: Handled by a General Practitioner agent.

  • Moderate complexity: Resolved through multi-agent collaboration and consensus.

  • High complexity: Processed through iterative hierarchical refinement, involving draft generation and review by a senior "Decision Maker."
    This routing mechanism ensures that the depth of reasoning is matched to the difficulty of the clinical query.

Memory and Reflection Mechanisms

Unlike systems that rely on a single, undifferentiated memory store, this framework assigns dedicated, role-specific memory to each agent. These memories store question contexts, answers, and timestamps.
When the system produces an incorrect answer, it triggers a reflection loop. The agents generate a "reflection entry" containing corrective feedback and reasoning summaries. This information is stored in the agent's memory to inform future reasoning, allowing the system to learn from past errors without needing to update the underlying model parameters.

Performance and Safety

The researchers evaluated the system using the MedQA and MedMCQA benchmarks. The results indicated that the combination of agent-specific memory, reflection, and external retrieval (RAG) produced the highest performance, achieving 93.2% accuracy on MedQA and 90.0% on MedMCQA.
To address concerns regarding reliability, the framework includes an "Ethical Overseer" module. This component acts as a post-generation screening layer that reviews candidate responses for potentially unsafe medical advice or unsupported diagnostic statements. If a response is deemed non-compliant with safety criteria, it is flagged rather than released.

Current Limitations

The authors note several constraints regarding the current implementation:

  • Retrieval Quality: The system’s effectiveness is tied to the quality of the retrieved information.

  • Memory Scaling: As the volume of stored experiences grows, the system may face challenges with duplicate or irrelevant cases.

  • Clinical Validation: The Ethical Overseer relies on the knowledge of the underlying large language model and has not yet been validated against formal clinical rules or by human clinicians.

  • Scope: The current evaluation is limited to retrospective benchmarks, which may not fully capture the complexities of real-world clinical decision-making.

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