Flow Mismatching: Unsupervised Anomaly Detection via Velocity Discrepancies in Flow Matching Models

ArXi:2605.23070v1 Announce Type: new We propose Flow Mismatching, an unsupervised anomaly detection method that deliberately avoids reconstruction-based paradigms. Instead, we treat flow matching as geometric dynamics and leverage a key insight: anomalies occur at places where the learned normal flow disagrees with the geometric path toward a test image. Given a flow matching model trained only on normal images, we probe its learned velocity field along affine paths from Gaussian noise to a target image.