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CLIR-Bench is a benchmark for multimodal question answering over irregularly sampled clinical time series, constructed from ICU records with 6,600 QA instances across 11 clinical variables. It reveals that existing generalist models struggle with sparse temporal evidence, highlighting the need for stronger irregular time-series reasoning methods.
This paper proves two necessary conditions for optimal inference in a mesh of sovereign agents with irregular, non-stationary observations: an adaptive timescale and gap-dependence, which are satisfied only by liquid (continuous-time) networks.
Presents a diffusion-based approach for generating irregular clinical time series that jointly models laboratory values and their observation patterns, using the DACMI benchmark from MIMIC-III. The model captures clinically meaningful dependencies between patient physiology and testing behavior under MNAR-like missingness.
SurF is a generative model for multivariate irregularly sampled time series using the Time Rescaling Theorem to transform event sequences into i.i.d. exponential noise, achieving state-of-the-art results across multiple real-world benchmarks.