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Flow Duality and Source Geometry for Categorical Generation

arxiv.org/abs/2609.10863

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Updated 6 h ago · first seen 11 Sept 2026

paper_01M294FNTKPGM08ZXVSXDYXDH0

Published
11 Sept 2026
T1 · 6 h ago
arXiv
2609.10863
T1 · 6 h ago
Category
cs.LG
T1 · 6 h ago

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9 claims · 9 properties

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https://arxiv.org/abs/2609.10863currentcurrentarXiv (Atom API + RSS)T1highdeterministic

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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.currentcurrentarXiv (Atom API + RSS)T1highdeterministic

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newcurrentcurrentarXiv (Atom API + RSS)T1highdeterministic

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2609.10863currentcurrentarXiv (Atom API + RSS)T1highdeterministic

Authorsauthors1

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Etrit HaxhollicurrentcurrentarXiv (Atom API + RSS)T1highdeterministic

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cs.LG, stat.MLcurrentcurrentarXiv (Atom API + RSS)T1highdeterministic

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https://arxiv.org/pdf/2609.10863currentcurrentarXiv (Atom API + RSS)T1highdeterministic

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cs.LGcurrentcurrentarXiv (Atom API + RSS)T1highdeterministic

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11 Sept 2026currentcurrentarXiv (Atom API + RSS)T1highdeterministic

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