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Research PaperResearchia:202609.30004

Breakdown of Local Denoising as Semantic Speciation

Guangkuo Liu

Abstract

The dynamics of generative models exhibit two apparently distinct temporal windows: a speciation window, in which a sample commits to a semantic class, and a nonlocality window, in which local context windows become insufficient for generation. Motivated by evidence of their near-concurrence in a variety of frontier models, we investigate their relationship through the spatial distribution of semantic information. Under a "common cause" hypothesis, we prove that the nonlocality window must lie i...

Submitted: September 30, 2026Subjects: Machine Learning; Data Science

Description / Details

The dynamics of generative models exhibit two apparently distinct temporal windows: a speciation window, in which a sample commits to a semantic class, and a nonlocality window, in which local context windows become insufficient for generation. Motivated by evidence of their near-concurrence in a variety of frontier models, we investigate their relationship through the spatial distribution of semantic information. Under a "common cause" hypothesis, we prove that the nonlocality window must lie in the speciation window. This hypothesis postulates that semantic labels explain a fraction of the correlations between distant tokens, a condition that is natural for many real datasets. We further give conditions under which both windows shrink to a single limiting time as system size grows, defining a "phase transition", and verify this behavior analytically in Gaussian mixtures. Together, these results identify conditions under which semantic information explains the concurrence of speciation and nonlocality, connecting two complementary perspectives on the emergence of semantic structure in generative modeling.


Source: arXiv:2609.38176v1 - http://arxiv.org/abs/2609.38176v1 PDF: https://arxiv.org/pdf/2609.38176v1 Original Link: http://arxiv.org/abs/2609.38176v1

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Date:
Sep 30, 2026
Topic:
Data Science
Area:
Machine Learning
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