SOURCE-LINKED INTELLIGENCE
Model-Aware Schedules Improve Generation via Fiberwise Optimal Transport
Diffusion and flow-matching schedules control the signal and noise coefficients that mix data and noise along affine probability paths. Minimizing a kinetic action defined on coefficient paths, motivated by optimal transport, helps explain strong baselines but remains model-agnostic and ignores prediction error. Here we introduce a model-aware schedule construction based on fiberwise optimal transport. At a fixed time and state on the probability path, compatible signal/noise decompositions form an affine fiber. We define a fiberwise prediction risk by averaging optimal-transport costs between
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- arXiv · AI, language, vision and robotics · 2026-09-10T17:30:44.000Z
First collected: 2026-09-20T19:02:05.452Z. This is not the publication date.