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Source note2 min read

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

Source

arXiv

arxiv.org/abs/2608.19029v1

Read the source

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 have developed an adaptive memory and reflection multi-agent system for medical question answering. This system, which uses specialized agents with dedicated memory and feedback, aims to improve the performance of existing systems that often lack adaptability, persistent memory, and structured decision-making. The system evaluates complexity and provides consensus and ethical oversight, resulting in strong performance compared to several baselines as demonstrated on MedQA and MedMCQA datasets.

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 that utilizes specialized agents with dedicated memory and reflection-based feedback.
  • These agents retrieve relevant prior cases to improve subsequent reasoning.
  • Complexity assessment routes questions through solo, collaborative, or escalated workflows.
  • Consensus and ethical overseer modules support reasoning consolidation and output review.
  • The system shows strong performance compared to several baselines in evaluations on MedQA and MedMCQA datasets.
  • Ablation studies indicate 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 and reflection
  • Multi-agent system
  • Specialized agents
  • Dedicated memory
  • Reflection-based feedback
  • Complexity assessment
  • Solo, collaborative, and escalated workflows
  • Consensus and ethical overseer modules
  • Performance evaluation
  • Baselines
  • Ablation studies
  • Structured memory and feedback
  • Trustworthy medical agents

Claims Made by the Document

  • The AMR system can improve the performance of existing medical QA systems.
  • Combining agent-specific memory, reflection, and external retrieval yields the strongest performance.

Evidence Used

  • Results from evaluations on MedQA and MedMCQA datasets.
  • Findings from ablation studies.

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.