发生了什么: Limits of Confidence in Diffusion

Apple researchers identified fundamental limitations in discrete diffusion models where multi-position sampling steps violate training distributions due to token dependencies.

为何重要: Limits of Confidence in Diffusion

Engineers building or debugging discrete diffusion systems for images, speech, or text need to understand when per-position confidence scores fail to capture joint dependencies.

注意: Limits of Confidence in Diffusion

Per-position marginal distributions alone cannot determine if token groups are dependent; identical marginals can mask different joint distributions, complicating model validation.

新闻

Limits of Confidence in Diffusion

Apple Machine Learning Research · 发布于 2026年10月2日 · 阅读约3分钟

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发生了什么: Apple researchers identified fundamental limitations in discrete diffusion models where multi-position sampling steps violate training distributions due to token dependencies.
为何重要: Engineers building or debugging discrete diffusion systems for images, speech, or text need to understand when per-position confidence scores fail to capture joint dependencies.
注意: Per-position marginal distributions alone cannot determine if token groups are dependent; identical marginals can mask different joint distributions, complicating model validation.

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