CORE: Robust Out-of-Distribution Detection via Confidence and Orthogonal Residual Scoring

ArXi:2603.18290v1 Announce Type: new Out-of-distribution (OOD) detection is essential for deploying deep learning models reliably, yet no single method performs consistently across architectures and datasets -- a scorer that leads on one benchmark often falters on another. We attribute this inconsistency to a shared structural limitation: logit-based methods see only the classifier's confidence signal, while feature-based methods attempt to measure membership in the