Limits of Confidence in Diffusion
Quick summary
arXiv:2609.20581v1 Announce Type: new Abstract: 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. We show that a step matches the training distribution only when the positions it writes are conditionally independent given the tokens already fixed, that no product of per-position distribution
Key takeaways
- arXiv:2609.20581v1 Announce Type: new Abstract: 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.
- We show that a step matches the training distribution only when the positions it writes are conditionally independent given the tokens already fixed, that no product of per-position distribution
Why it matters
The importance of “Limits of Confidence in Diffusion” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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