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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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.26691 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | hXEDyEtn_fnc |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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