Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants

ArXi:2512.10857v2 Announce Type: replace-cross Transport-based methods have emerged as a leading paradigm for building generative models from large, clean datasets. However, in many scientific and engineering domains, clean data are often unavailable: instead, we only observe measurements corrupted through a noisy, ill-conditioned channel. A generative model for the original data thus requires solving an inverse problem at the level of distributions. In this work, we