Limits of Confidence in Diffusion is a research paper analyzing conditional independence constraints in discrete diffusion models. The authors demonstrate that current per-position sampling methods fail to match training distributions when generating dependent tokens like pixels or words. This finding helps AI researchers understand the mathematical limits of non-autoregressive generation and points toward better sampling techniques.
Opening Kapyn…