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Limits of Confidence in Diffusion

Research Multimodal & Generative

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TL;DR - This paper identifies a fundamental limitation in discrete diffusion samplers that generate multiple token positions independently per step: they reproduce the training distribution only when those positions are conditionally independent. This matters because standard per-sample metrics can look perfect while concealing substantial distributional error.

  • No product of per-position distributions can represent a jointly dependent group of tokens.
  • Per-position marginals cannot reveal group dependence, since different joint distributions may share identical marginals.
  • On the synthetic ScanAndAdd task, every multi-position group selected by confidence ranking is dependent.
  • The generated distribution’s total variation is (29\times) the sampling-noise floor despite per-sample metrics scoring (1.0).

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Limits of Confidence in Diffusion

arXiv cs.AI Russ Webb, Amitis Shidani, Alice Bizeul, Dan Busbridge 2026-09-17 arXiv:2609.20581
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-22 14:19:31.583217 UTC

TL;DR - This paper identifies a fundamental limitation in discrete diffusion samplers that generate multiple token positions independently per step: they reproduce the training distribution only when those positions are conditionally independent. This matters because standard per-sample metrics can look perfect while concealing substantial distributional error.

  • No product of per-position distributions can represent a jointly dependent group of tokens.
  • Per-position marginals cannot reveal group dependence, since different joint distributions may share identical marginals.
  • On the synthetic ScanAndAdd task, every multi-position group selected by confidence ranking is dependent.
  • The generated distribution’s total variation is (29\times) the sampling-noise floor despite per-sample metrics scoring (1.0).
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