Constraint-Aware Flow Matching: Decision Aligned End-to-End Training for Constrained Sampling

ArXi:2605.12754v1 Announce Type: new Deep generative models provide state-of-the-art performance across a wide array of applications, with recent studies showing increasing applicability for science and engineering. Despite a growing corpus of literature focused on the integration of physics-based constraints into the generation process, existing approaches fail to enforce strict constraint satisfaction while maintaining sample quality. In particular