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A Mamba-augmented model is proposed that inserts selective state-space blocks at the convolutional bottleneck, combining local feature extraction with long-range temporal modeling at linear complexity, and substantially improves ST/T-change diagnoses, which depend on broad, context-sensitive waveforms.
Across modalities, the results show that explicitly encoding measurable cyclic structure improves physiological time-series restoration and proposes antithetic coupling of reverse trajectories to reduce sampling variance while achieving comparable performance with fivefold fewer network evaluations.
This framework identifies which law each loss in the literature actually optimizes, recovering MDM, UDM, SEDD, and GIDD as special cases; explains why denoiser and cavity coincide for masked diffusion but not for uniform diffusion; proves that a denoiser parameterization makes the uniform ELBO diverge at initialization...
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