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Flow Duality and Source Geometry for Categorical Generation

arxiv.org/abs/2609.10863

Updated 15 min ago · first seen 11 Sept 2026

paper_01M294FNTKPGM08ZXVSXDYXDH0

Published
11 Sept 2026
T1 · 15 min ago
arXiv
2609.10863
T1 · 15 min ago
Category
cs.LG
T1 · 15 min ago

Abstract

Continuous and discrete flow matching are usually treated as separate constructions. This paper identifies a duality between them: projecting continuous convex-interpolant paths with one-hot targets through a position-wise argmax yields discrete convex-interpolant paths. The result requires source laws with appropriate coordinate symmetry and boundary regularity, and it makes the continuous source distribution an explicit design choice for categorical generation. We derive the induced discrete interpolation behavior for Gaussian, bounded-uniform, and centered negative-exponential sources, showing that different source geometries lead to qualitatively different transition timing and vocabulary-size dependence. Small visual diagnostics and a short language-modeling pilot suggest that these source-design effects can also appear in learned transports and early generative quality.

Authors 1

Etrit Haxholli

Specification

Official page

Source:arXiv (Atom API + RSS)T1observed 15 min agohigh

Arxiv announce type
new

Source:arXiv (Atom API + RSS)T1observed 15 min agohigh

arXiv id
2609.10863

Source:arXiv (Atom API + RSS)T1observed 15 min agohigh

Categories
cs.LG, stat.ML

Source:arXiv (Atom API + RSS)T1observed 15 min agohigh

PDF

Source:arXiv (Atom API + RSS)T1observed 15 min agohigh

Primary category
cs.LG

Source:arXiv (Atom API + RSS)T1observed 15 min agohigh

Published
11 Sept 2026

Source:arXiv (Atom API + RSS)T1observed 15 min agohigh

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Provenance

Attributed facts

9

Source tiers

T19

Freshest observation

15 min ago

Conflicts

None