LongMedBench: Benchmarking Medical Agents for Long-Horizon Clinical Decision-Making
Prior evaluations of LLM-based medical agents have largely emphasized short-context knowledge QA and tool use. However, real-world medical care is inherently longitudinal, and clinicians must aggregate evidence across repeated visits, tests, and evolving treatments. Therefore, long-horizon interaction is essential for realistic assessment.
- ▪Prior evaluations of LLM-based medical agents have largely emphasized short-context knowledge QA and tool use.
- ▪However, real-world medical care is inherently longitudinal, and clinicians must aggregate evidence across repeated visits, tests, and evolving treatments.
- ▪Therefore, long-horizon interaction is essential for realistic assessment.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2607.09322 |
| Publication time | Mon, 13 Jul 2026 00:00:00 -0400 |
| Retrieval time | 2026-07-13T04:20:37.625Z |
| Last seen | 2026-07-13T06:15:33.219Z |
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Computer Science > Artificial Intelligence arXiv:2607.09322 (cs) [Submitted on 10 Jul 2026] Title:LongMedBench: Benchmarking Medical Agents for Long-Horizon Clinical Decision-Making Authors:Yanzhen Chen, Zihan Xu, Xiaocheng Zhang, Zhiting Fan, Weiqi Zhai, Hongxia Xu, Zuozhu Liu View a PDF of the paper titled LongMedBench: Benchmarking Medical Agents for Long-Horizon Clinical Decision-Making, by Yanzhen Chen and 6 other authors View PDF HTML (experimental) Abstract:In this work, we introduce LongMedBench, a real-world EHR-based benchmark for long-horizon clinical decision-making. Prior evaluations of LLM-based medical agents have largely emphasized short-context knowledge QA and tool use.
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