Langevin-Informed Transfer Learning: Replacing Target Samples by Black-Box Feedback

Excerpt

arXiv:2610.01522v1 Announce Type: new Abstract: Many scientific and machine learning systems, from molecular dynamics to diffusion models and beyond, are governed by stochastic dynamics with low-dimensional structure, evolving on slow timescales. However, target trajectories, used to identify and interpret such dynamics, are often inaccessible: only biased or static samples that explore the underlying manifold are available. We introduce Langevin-Informed Transfer Learning (LITL), a framework fo