Limits of Confidence in Diffusion

Apple Machine Learning Research Version 1 original current

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Discrete diffusion, including remasking and uniform-state samplers, generate a sequence by writing multiple token positions per step, drawing each from a per-position distribution and choosing which positions to write from those same distributions. For domains of general interest (pixels, phonemes, or words) there are inherent dependencies between tokens. …

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October 02, 2026 21:00
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