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Each question is linked to explicit temporal evidence and task-specific answer derivation rules, enabling evaluation of both answer accura","title":"CLIR-Bench: Benchmarking Multimodal Question Answering over Irregular Clinical Time Series","url":"https://arxiv.org/abs/2607.09880","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.09880v1 Announce Type: cross \nAbstract: Clinical time series are central to patient monitoring, risk assessment, and clinical decision support. However, they are often sparse, irregularly sampled, and asynchronous, making it difficult for models to identify the temporal evidence required for clinical Question Answering (QA). Existing benchmarks primarily focus on regularly sampled time-series QA or medical QA over static data, and therefore rarely assess whether models can faithfully ground their answers in irregular temporal observations. 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