SOURCE-LINKED INTELLIGENCE

Witnesses Explain Anomalies

arXiv · AI, language, vision and robotics · article · Sep 3, 2026 · UTC

Unsupervised anomaly detection scores each point of an unlabelled, contaminated sample in a single pass, and increasingly must also explain why a point is flagged. Yet the dominant detectors give a score with no account of which features drive it, and explanations are bolted on post-hoc with SHAP or LIME, which re-query the detector thousands of times per point and only approximate it. We introduce WAND, an unsupervised tabular anomaly detector that is explainable by design. WAND organises its computation around directions on the unit sphere, scoring each point by how far its projection escape

Read original source ↗ Open in workspace

recordType

paper

region

Global