Mind the Tool Failures: Achieving Synergistic Tool Gains for Medical Agents
The article discusses the challenges faced by medical AI agents when using external tools for diagnosis and treatment. It highlights the limitations of existing approaches that assume tool reliability, especially in complex clinical scenarios. The authors propose a new framework that addresses these issues by focusing on instance-level tool selection and synergy learning.
- ▪Medical AI agents often rely on external tools for various tasks, but these tools can fail in real clinical settings.
- ▪The authors introduce a GRPO-based reinforcement learning framework to improve tool synergy and minimize risks.
- ▪Experiments demonstrate that their method achieves consistent improvements over existing baselines.
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Computer Science > Artificial Intelligence arXiv:2605.26691 (cs) [Submitted on 26 May 2026] Title:Mind the Tool Failures: Achieving Synergistic Tool Gains for Medical Agents Authors:Yunhui Gan, Tan Pan, Kaiyu Guo, Limei Han, Weimiao Yu, Guangnan Ye, Chen Jiang, Yuan Cheng View a PDF of the paper titled Mind the Tool Failures: Achieving Synergistic Tool Gains for Medical Agents, by Yunhui Gan and 7 other authors View PDF HTML (experimental) Abstract:Medical AI agents increasingly use external tools for diagnosis, treatment recommendation, and evidence retrieval, yet most existing approaches assume that task-appropriate tools are reliable within their intended scope.
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