Apple Researchers Show Limits of Confidence in Diffusion Models
Apple researchers analyze discrete diffusion samplers, including remasking and uniform-state samplers, which write multiple token positions per step using per-position distributions. They show a step matches the training distribution only when written positions are conditionally independent given fixed tokens, and that no product of per-position distributions can match a dependent group.
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