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Published, Version of Record (VoR) Open Access via Drexel Libraries Read and Publish Program 2026 Open CC BY V4.0
Journal article
MediHive: A Decentralized Agent Collective for Medical Reasoning
Journal of Healthcare Informatics Research, Forthcoming
27 May 2026
Featured in Collection : Research Supported by Drexel Libraries' OA Programs
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.
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Details
- Title
- MediHive: A Decentralized Agent Collective for Medical Reasoning
- Creators
- Xiaoyang Wang - Drexel University, College of Computing and InformaticsChristopher C Yang (Corresponding Author) - Drexel University, Information Science
- Publication Details
- Journal of Healthcare Informatics Research, Forthcoming
- Publisher
- Springer Nature
- Number of pages
- 18
- Grant note
- U.S. Department of Defense National Science Foundation
This work was supported in part by the National Science Foundation under Grants IIS-1741306 and IIS-2235548, and by the Department of Defense under Grant DoD W91XWH-05-1-023. This material is based upon work supported by (while serving at) the National Science Foundation.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science; College of Computing and Informatics
- Web of Science ID
- WOS:001778460400001
- Scopus ID
- 2-s2.0-105040237251
- Other Identifier
- 991022184775804721