[2508.08115] TeamMedAgents: Pareto-Efficient Multi-Agent Medical Reasoning Through Teamwork Theory

Computer Science > Artificial Intelligence arXiv:2508.08115 (cs) [Submitted on 11 Aug 2025 (v1), last revised 31 Mar 2026 (this version, v3)] Title:TeamMedAgents: Pareto-Efficient Multi-Agent Medical Reasoning Through Teamwork Theory Authors:Pranav Pushkar Mishra, Mohammad Arvan, Mohan Zalake (University of Illinois, Chicago) View a PDF of the paper titled TeamMedAgents: Pareto-Efficient Multi-Agent Medical Reasoning Through Teamwork Theory, by Pranav Pushkar Mishra and 3 other authors View PDF HTML (experimental) Abstract:Complex medical reasoning has historically required frontier language models to achieve clinically-acceptable accuracy, creating computational barriers that limit deployment in resource-constrained clinical settings. We present TeamMedAgents, a modular multi-agent framework that translates Salas et al.'s evidence-based teamwork theory into computational mechanisms--shared mental models, team leadership, team orientation, trust networks, and mutual monitoring--enabling Small Language Models to perform multi-step clinical reasoning efficiently. Evaluation across 8 medical benchmarks demonstrates that TeamMedAgents advances the Pareto efficiency frontier by 1-2 orders of magnitude, achieving competitive accuracy at substantially lower token cost than MDAgents, MedAgents, DyLAN, and ReConcile. The framework exhibits the lowest cross-dataset variance among multi-agent approaches, enabling deployment without per-task tuning. Our results establish that theory-g...