ReTabAD: A Benchmark for Restoring Semantic Context in Tabular Anomaly Detection

ArXi:2510.02060v2 Announce Type: replace In tabular anomaly detection (AD), textual semantics often carry critical signals, as the definition of an anomaly is closely tied to domain-specific context. However, existing benchmarks provide only raw data points without semantic context, overlooking rich textual metadata such as feature descriptions and domain knowledge that experts rely on in practice. This limitation restricts research flexibility and prevents models from fully leveraging domain knowledge for detection.