Conservative Flows: A New Paradigm of Generative Models
Machine Learning
2026-05-11 v1
Abstract
Modern generative modeling is dominated by transport from a noise prior to data. We propose an alternative paradigm in which generation is performed by a discrete stochastic dynamics that leaves the data distribution invariant, initialized from data-supported states rather than from noise. The framework can utilize any pretrained flow model. We develop two probability-preserving sampling mechanisms, a corrected Langevin dynamics with a Metropolis adjustment and a predictor-corrector flow, that operate directly on existing checkpoints. We validate the framework on a synthetic Swiss-roll target, ImageNet-256 and Oxford Flowers-102, where our samplers consistently improve over the original generation procedures.
Keywords
Cite
@article{arxiv.2605.06905,
title = {Conservative Flows: A New Paradigm of Generative Models},
author = {Eshed Gal and Md Shahriar Rahim Siddiqui and Moshe Eliasof and Eldad Haber},
journal= {arXiv preprint arXiv:2605.06905},
year = {2026}
}