Extreme-Order Context Ensembling for Zero-Shot Tabular Outlier Detection

Zero-shot tabular foundation models flag outliers using context sampled from the same unlabeled data that they are scoring. This inadvertently contaminates the context with target outliers that need flagging, consequently degrading detection performance. We introduce PEAK (Parallel Extreme-order Aggregation over $K$ contexts), a training-free context ensembling technique that addresses context contamination by treating the context as a random draw rather than a fixed input. PEAK samples $K$ contexts, converts each sampled context's outlier scores to percentile ranks, and averages the top $k=\lceil \sqrt{K}\rceil$ ranks for each instance. This parameter-free aggregation exploits an empirical asymmetry where contexts containing instances similar to an outlier suppress its rank, thus informative contexts remain in top ranks. The count $k$ is derived from the budget rather than tuned, and a finite-mixture analysis bounds the probability that a suppressed draw survives. Experiments on multiple large scale outlier detection benchmarks, namely ADBench, MacrOData, and real world dataset BitcoinHeist, demonstrate that PEAK consistently recovers performance of base models lost to contamination.