TaskBridge: Bridging Unsupervised Tabular Anomaly Detection and In-Context Learning via Virtual Tasks

Unsupervised tabular anomaly detection (TAD) aims to identify anomalous rows in tabular data using normal training samples and plays an important role in many real-world applications. While conventional approaches rely on dataset-specific model fitting and configuration search, recent prior-data fitted network (PFN)-based tabular foundation models (TFMs) enable zero-shot anomaly detection on unseen datasets via in-context learning (ICL). Most TFM-based approaches, however, require anomaly-specific pretraining from scratch, making detection inherently dependent on synthetic TAD-specific priors and costly to update and extend. Some approaches instead repurpose pretrained general-purpose TFMs for TAD to avoid this burden, but rely on computationally expensive formulations with restrictive anomaly inductive biases. In this work, we introduce TaskBridge, a new framework that efficiently repurposes pretrained general-purpose TFMs for unsupervised TAD by constructing virtual supervised tasks that directly recast anomaly detection as supervised in-context inference with TFMs. The resulting virtual tasks induce predictive structures through the TFM's in-context inference, under which normal queries and their virtual target pairs receive high support, whereas anomalies tend to violate the induced structures and receive lower support, providing direct anomaly evidence. Across 790 real-world datasets, TaskBridge consistently outperforms 30 baselines, including existing TFM-based approaches, without anomaly-specific TFM pretraining or dataset-specific model optimization.