[2603.27150] MediHive: A Decentralized Agent Collective for Medical Reasoning

Computer Science > Artificial Intelligence arXiv:2603.27150 (cs) [Submitted on 28 Mar 2026] Title:MediHive: A Decentralized Agent Collective for Medical Reasoning Authors:Xiaoyang Wang, Christopher C. Yang View a PDF of the paper titled MediHive: A Decentralized Agent Collective for Medical Reasoning, by Xiaoyang Wang and 1 other authors View PDF HTML (experimental) 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 ...