Limits of Confidence in Diffusion
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...
What happened
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...
Why it matters
The development may change operating conditions or market expectations around AI. Further confirmation and measurable outcomes matter.
Affected entities
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1 reports · 1 original report · 1 independent
- Apple Machine Learning ResearchPrimary source · Supports · EN · 100%Limits of Confidence in Diffusion ↗
Claims
- Limits of Confidence in Diffusion Observed
Conflicts
No material conflict detected in the available evidence.
Timeline
- First reported
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