MediHive: A Decentralized Agent Collective for Medical Reasoning

arXiv cs.AI / 3/31/2026

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Key Points

  • MediHiveは、医療質問応答に向けた分散型マルチエージェント(D-MAS)フレームワークで、LLMエージェントが自律的に専門ロールを自己割当てします。
  • 共有メモリプールと反復的フュージョン機構により、エビデンスの不一致を条件付きの議論(debate)で検出し、複数ラウンドで合意形成を目指します。
  • 中央集権型MASのボトルネックや単一障害点、役割の混乱といった課題を、ピアツーピア相互作用で軽減することを狙っています。
  • 実験ではMedQAで84.3%、PubMedQAで78.4%を達成し、単一LLMおよび中央集権型ベースラインより高い性能を示したと報告されています。

Abstract

Large language models (LLMs) have revolutionized medical reasoning tasks, yet single-agent systems often falter on complex, interdisciplinary problems requiring robust handling of uncertainty and conflicting evidence. Multi-agent systems (MAS) leveraging LLMs enable collaborative intelligence, but prevailing centralized architectures suffer from scalability bottlenecks, single points of failure, and role confusion in resource-constrained environments. Decentralized MAS (D-MAS) promise enhanced autonomy and resilience via peer-to-peer interactions, but their application to high-stakes healthcare domains remains underexplored. We introduce MediHive, a novel decentralized multi-agent framework for medical question answering that integrates a shared memory pool with iterative fusion mechanisms. MediHive deploys LLM-based agents that autonomously self-assign specialized roles, conduct initial analyses, detect divergences through conditional evidence-based debates, and locally fuse peer insights over multiple rounds to achieve consensus. Empirically, MediHive outperforms single-LLM and centralized baselines on MedQA and PubMedQA datasets, attaining accuracies of 84.3% and 78.4%, respectively. Our work advances scalable, fault-tolerant D-MAS for medical AI, addressing key limitations of centralized designs while demonstrating superior performance in reasoning-intensive tasks.