ClinLens: Towards Long-Horizon LLM Agents for Longitudinal Multimodal Clinical Data Science
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...
| Authors | Yuan Zhu, Ethan B. Liu, Frank Nie et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 July 2026 |
| Last updated at source | 26 September 2026 |
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