MT-Tab: Leveraging Inter-Target Dependencies in Tabular Foundation Models for Multi-Target Prediction

Tabular data are widely used across industrial domains such as manufacturing, finance, and healthcare. Tabular foundation models (TFMs), including TabPFN and TabICL, showcase strong generalization capabilities, outperforming Gradient Boosted Decision Trees (GBDTs) through in-context learning. However, existing TFMs are designed for single-target prediction. When applied to multi-target prediction problems which are common in real-world applications, they suffer from three limitations: (i) inference costs and resource requirements scale linearly with the number of targets, (ii) dependencies among targets are not fully leveraged, and (iii) directly modeling a joint distribution during pretraining is computationally expensive and increases architectural complexity. In particular, (ii) is the central motivation of this paper: single-target TFMs fundamentally exclude from the prediction path the information among multiple targets that is usable for prediction, which we call target dependency. Therefore, we propose MT-Tab, a multi-target Tabular foundation model framework that reformulates pretrained TFMs to tackle multi-target prediction problems. MT-Tab unfolds multi-target labels along the meta/batch dimension via target unfolding, passes them through the pretrained target encoder, and integrates them into the TFM's transformer input through prompt expansion. To preserve pretrained priors, we keep the TFM parameters frozen and employ Low-Rank Adaptation (LoRA) for efficient task adaptation. On the newly constructed MT-TabBench with 10 tasks, MT-Tab achieves the highest Elo rating among 17 methods including 16 baselines. Specifically, MT-Tab yields up to a 7.1% performance gain over the strongest single-target TFM under target imbalance by capturing target dependencies, while achieving up to 11.2$\times$ faster inference in many targets.