CoMMa: Contribution-Aware Medical Multi-Agents for Decentralized Oncology Decision Support

[2602.09159] CoMMa: Contribution-Aware Medical Multi-Agents for Decentralized Oncology Decision Support Skip to main content Search Submit Donate Log in Computer Science > Artificial Intelligence arXiv:2602.09159 (cs) [Submitted on 9 Feb 2026 ( v1 ), last revised 8 Oct 2026 (this version, v3)] Title: CoMMa: Contribution-Aware Medical Multi-Agents for Decentralized Oncology Decision Support Authors: Yichen Wu , Kailong Fan , Sangjoon Park , Yuhan Liu , Zhiyi Shi , Sekeun Kim , Dania Daye , Hana Farzaneh , Xiang Li , Raul Uppot , Yujin Oh , Quanzheng Li View PDF HTML (experimental) Abstract: Recent multi-agent frameworks have shown promise for oncology decision support, yet most assume centralized data access and rely on prompt-based assignment, limiting their applicability in privacy-sensitive clinical settings. We propose Contribution-Aware Medical Multi-Agents (CoMMa), a decentralized LLM-agent framework where specialists operate on partitioned clinical data streams. Unlike prior approaches that share inputs across agents, CoMMa enforces data decentralization to include stronger role specialization and further enhances this via agent-specific finetuning. To enable reliable and interpretable coordination, we introduce a contribution-aware aggregation mechanism that replaces stochastic, narrative-based reasoning with deterministic embedding projections to approximate each agent's marginal utility. This yields explicit credit assignment over agents, providing a stable and interpretable decision pathway aligned with clinical requirements. We evaluate CoMMa on multiple oncology benchmarks, including real-world multidisciplinary tumor board datasets from large academic hospitals in North America and East Asia, as well as public datasets, demonstrating strong performance and generalization across heterogeneous clinical settings. Comments: 9 pages, 5 figures Subjects: Artificial Intelligence (cs.AI) ; Multiagent Systems (cs.MA) Cite as: arXiv:2602.09159 [cs.AI] (or arXiv:2602.09159v3 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.09159 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yichen Wu [ view email ] [v1] Mon, 9 Feb 2026 20:04:58 UTC (7,168 KB) [v2] Tue, 25 Aug 2026 01:03:48 UTC (5,070 KB) [v3] Thu, 8 Oct 2026 19:22:55 UTC (5,070 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.AI < prev | next > new | recent | 2026-02 Change to browse by: cs cs.MA References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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