AI RESEARCH
MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling
arXiv CS.LG
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ArXi:2605.13711v1 Announce Type: new Multimodal irregular time series (MITS) consist of asynchronous and irregularly sampled observations from heterogeneous numerical and textual channels. In healthcare, for example, patients' electronic health records (EHR) include irregular lab measurements and clinical notes. The irregular timing and channel patterns of observations carry predictive signal alongside the numerical values and textual content.