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ClinLens：面向纵向多模态临床数据科学的长时程 LLM 智能体
该论文提出 ClinLens，一个以患者为中心的基准，用于评估 LLM 智能体在长时程临床数据科学工作流中的表现。基准包含 126 个可执行任务，覆盖 5 种数据模态、4 类分析范围和 5 项分析能力，考察智能体在证据定位、多模态整合与分析流程生成执行等相互依赖阶段中维持患者特定分析上下文的能力。作者评测了通用、编程与生物医学智能体，任务平均耗时 36.93 步、8.06 分钟；最强智能体执行成功率 86.4%、最终答案准确率 54.0%，但严格通过率仅 29.4%。失败分析显示患者病程定位是最常见的失败类别。
AI推荐理由 arXiv 论文（v2，2026-09-26 修订），提出 ClinLens 患者中心基准，含 126 个可执行任务、覆盖 5 种数据模态、4 类分析范围与 5 项分析能力，用于评估 LLM 智能体在纵向多模态临床数据科学中的长时程工作流。摘要给出具体量化结果：任务平均 36.93 步、8.06 分钟，最强智能体执行成功率 86.4%、最终答案准确率 54.0%、严格通过率仅 29.4%，并指出患者病程定位是最常见失败类别。信息密度高、指标明确，对医疗 Agent 评测有实际参考价值；局限是仅摘要层面，未提供数据集规模细节与外部复现证据。
78
正文
Computer Science > Artificial Intelligence
arXiv:2607.26155 (cs)
[Submitted on 28 Jul 2026 (v1), last revised 26 Sep 2026 (this version, v2)]
Title:ClinLens: Towards Long-Horizon LLM Agents for Longitudinal Multimodal Clinical Data Science
Authors:Yuan Zhu, Ethan B. Liu, Frank Nie, Wei Fan, Haibo Pu, Jindong Han
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Abstract:Automating clinical data science requires agents to translate patient-centric objectives into executable workflows grounded in longitudinal multimodal evidence. Existing benchmarks provide limited coverage of two essential capabilities: grounding analyses in the correct patient episode and harmonizing multimodal observations. We introduce ClinLens, a patient-centric benchmark for evaluating large language model (LLM) agents on long-horizon clinical data science workflows. ClinLens comprises 126 executable tasks spanning five data modalities, four analytical scopes, and five analytical capabilities. The tasks assess agents' ability to maintain a coherent patient-specific analytical context across interdependent stages of evidence grounding, multimodal integration, and analytical pipeline generation and execution. We evaluate general-purpose, coding, and biomedical agents, with task runs averaging 36.93 steps and 8.06 minutes. The strongest evaluated agent achieves an execution success rate of 86.4% and a final-answer accuracy of 54.0%, but a strict pass rate of only 29.4%. Failure analysis identifies patient-episode grounding as the most frequently recorded failure category under the evaluation procedure. By jointly evaluating analytical outcomes and the validity of the supporting clinical context, ClinLens provides a testbed for advancing reliable agents for longitudinal clinical data science.
Subjects:
Artificial Intelligence (cs.AI)
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arXiv:2607.26155 [cs.AI]
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arXiv:2607.26155v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.26155
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arXiv-issued DOI via DataCite
Submission history
From: Jindong Han [view email]
[v1]
Tue, 28 Jul 2026 18:01:48 UTC (41 KB)
[v2]
Sat, 26 Sep 2026 16:39:01 UTC (2,180 KB)
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