mlsanityData to 14 Oct 2026

Generative Residual Factorization

arXiv 2609.34824 ↗First posted 28 Sep 2026cs.CVcited by 0

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Abstract (arXiv)

Under a shared-factor model, the conditional law of the next image patch factors into a posterior over the shared scene factor and a residual kernel given that factor. A sufficient statistic of the past replaces the raw past in the posterior and does not replace the kernel. The conditional entropy splits into residual entropy, which no observation of the factor can remove, and a posterior term, which a better representation of the past can remove. Next-embedding prediction is a directional likelihood on a shallow map, so the fiber of that map is unidentified and a constant embedding remains a minimizer. The same split is an equality in a scalar Gaussian model, evaluated in closed form.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 45 of its 61 references that are in the map. First posted on arXiv 28 Sep 2026.

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45 of 61 references.

References · 45

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