SCOPE: SELF-CONSTRUCTED POSITIVE EVIDENCE FOR IN-CONTEXT TABULAR ANOMALY DETECTION

Tabular anomaly detection is challenging because anomalies are rare, diverse, and typically unlabeled. Existing methods largely model normality, while recent tabular foundation models either rely on anomaly-specific pretraining with synthetic anomalies or use predictive discrepancy directly as the anomaly score. What remains largely unexplored is whether a general-purpose tabular foundation model can construct the supervision needed to use its discriminative in-context capability at inference time. We introduce SCOPE (Self-COnstructed Positive Evidence), which turns a frozen general-purpose tabular foundation model into an anomaly detector without anomaly-specific training. SCOPE first uses conditional feature prediction to locate candidate positive evidence, then uses discriminative in-context prediction to iteratively refine this evidence. Uncertain target rows receive graded intermediate targets rather than being treated as normal, and multiple evidence contexts are sampled from the refined ranking to reduce dependence on a single hard positive set. All context rows are actual observations rather than synthesized anomalies, and the frozen predictor is never updated. Across 1,502 benchmark entries from ADBench, OddBench, and OvRBench, SCOPE ranks first under all five aggregate criteria for both AUROC and AUPRC on every benchmark.