CoMMa: Contribution-Aware Medical Multi-Agents From A Game-Theoretic Perspective

• URL: http://arxiv.org/abs/2602.09159v1
• Date: Mon, 09 Feb 2026 20:04:58 GMT
• Title: CoMMa: Contribution-Aware Medical Multi-Agents From A Game-Theoretic Perspective
• Authors: Yichen Wu, Yujin Oh, Sangjoon Park, Kailong Fan, Dania Daye, Hana Farzaneh, Xiang Li, Raul Uppot, Quanzheng Li,
• Abstract summary: We propose Contribution-Aware Medical Multi-Agents (CoMMa) to tackle oncology decision support tasks.<n>Specialists operate on partitioned evidence and coordinate through a game-theoretic objective for robust decision-making.<n> Evaluated on diverse oncology benchmarks, CoMMa achieves higher accuracy and more stable performance than data-centralized and role-based multi-agents baselines.
• Score: 17.875369977050926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent multi-agent frameworks have broadened the ability to tackle oncology decision support tasks that require reasoning over dynamic, heterogeneous patient data. We propose Contribution-Aware Medical Multi-Agents (CoMMa), a decentralized LLM-agent framework in which specialists operate on partitioned evidence and coordinate through a game-theoretic objective for robust decision-making. In contrast to most agent architectures relying on stochastic narrative-based reasoning, CoMMa utilizes deterministic embedding projections to approximate contribution-aware credit assignment. This yields explicit evidence attribution by estimating each agent's marginal utility, producing interpretable and mathematically grounded decision pathways with improved stability. Evaluated on diverse oncology benchmarks, including a real-world multidisciplinary tumor board dataset, CoMMa achieves higher accuracy and more stable performance than data-centralized and role-based multi-agents baselines.

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