CLIR-Bench: Benchmarking Multimodal Question Answering over Irregular Clinical Time Series

Jul 14, 2026

CLIR-Bench is a new benchmark designed to evaluate multimodal question answering on irregular clinical time series, constructed from de-identified ICU records. The dataset includes 6,600 QA instances across 11 clinical variables, structured into four capability dimensions and 11 tasks. Each question is linked to explicit temporal evidence and task-specific answer derivation rules. Experiments indicate that current generalist models have difficulty retrieving and reasoning over sparse clinical evidence.

Why it matters: This benchmark fills a critical gap in assessing AI models' ability to ground answers in irregular temporal clinical data, which is important for developing reliable clinical decision support systems.

Full story at: arXiv Computation and Language ↗