Adaptive Memory and Reflection Multi-Agent System for Medical Question Answering

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Pradeep Murugesan, Luoxiao Yang, Xueli Chen, Xinqi Fan
• Venue: arXiv: cs.AI
• Published: 2026-08-19
• Collected: 2026-08-20
• URL: http://arxiv.org/abs/2608.19029v1
• PDF: https://arxiv.org/pdf/2608.19029v1
• DOI: UNKNOWN
• Registry group: academic_apis_and_preprints
• Evidence role: canonical_academic_index
• Language: en
• Country / region: UNKNOWN

Plain-Language Brief

Researchers introduce a new multi-agent system called Adaptive Memory and Reflection (AMR) for medical question answering. This system aims to address the limitations of existing systems by using specialized agents with dedicated memory and reflection-based feedback to improve reasoning. The evaluation shows strong performance compared to several baselines.

What the Document Says

• The paper presents an Adaptive Memory and Reflection (AMR) agentic system for medical question answering.
• The AMR system is a multi-agent framework with specialized agents using dedicated memory and reflection-based feedback.
• Complexity assessment routes questions through solo, collaborative, or escalated workflows.
• Consensus and ethical overseer modules support reasoning consolidation and output review in the AMR system.
• 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.
• The findings suggest the potential of structured memory and feedback for developing more trustworthy medical agents.

Key Concepts Mentioned

• medical question answering
• adaptive memory
• reflection
• multi-agent system
• evaluation
• ablation studies

Claims Made by the Document

• The AMR agentic system improves medical question answering compared to existing systems.
• Combining agent-specific memory, reflection, and external retrieval yields the strongest performance.

Evidence Used

• 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.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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 https://github.com/mm-air/AMR-Agent.