Flowing with Confidence

In three linesFlow Matching with Confidence (FMwC) adds per-sample confidence scores to generative models at standard sampling cost. By injecting input-dependent multiplicative noise and propagating variance through the ODE, the method enables filtering, trajectory editing, and adaptive stepping. The confidence score correlates with the divergence magnitude of the learned velocity field.

Summary generated by Claude — human-verified