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This paper introduces CAIR, a two-stage framework for imputing physiological time-series data under realistic missingness, outperforming existing methods by incorporating gap mechanisms and curriculum-aware training.
MuteBench is a benchmark for evaluating multimodal fusion models under modality missing and within-modality missing conditions across clinical datasets. It provides insights into architecture robustness and suggests that diffusion-based imputation can help.