CoVA-TAD: In-Context Predictive Dispersion for Training-Free Tabular Anomaly Detection

We study whether a frozen tabular regression foundation model can provide a useful anomaly-ranking signal without target-table parameter updates or anomaly-specific training. In the one-class screening regime, CoVA-TAD (**Co**nditional **V**ariance **A**ggregation for **T**abular **A**nomaly **D**etection) prompts a regressor with a clean normal reference cohort, predicts selected columns from the remaining columns, and sums the log dispersion of its quantile outputs. The resulting score is an operational aggregation of conditional predictive dispersions: it is not a calibrated uncertainty estimate or a likelihood. On a fixed 15-table ADBench-derived protocol, CoVA-TAD attains 88.49 macro AUROC and 78.19 macro AUPRC. It exceeds the transcribed OFA-TAD aggregate by 0.66 AUROC and 1.79 AUPRC points, while paired dataset-level tests are inconclusive (Wilcoxon $p=0.524$ and $p=0.151$). Six context seeds give $88.43\pm 0.23$ AUROC and $79.00\pm 0.93$ AUPRC. We release full-prevalence results, distinguish local controls from published comparators, and document sensitivity to the variance stabilizer, projection count, reference contamination, and feature order. These findings position predictive dispersion as a promising, training-free baseline for offline tabular screening, with important calibration and compute limitations.