Path-independent Flow Matching for Multi-parameter Generative Dynamics

ArXi:2605.13487v1 Announce Type: new Flow Matching is a powerful framework for learning transport maps between probability distributions. Yet its standard single-parameter formulation is not designed to capture multi-parameter variations where the resulting transport should be path-independent. Path independence is crucial because it ensures that transformations depend only on the initial and target distributions, not on the specific path. In this work, we