XPFN: Learning Zero-Shot Tabular Prediction and Explanation from Prior-Sampled Response Functions

Tabular foundation models (TFMs) make predictions in a single forward pass, but explaining these predictions still relies largely on expensive post-hoc attribution. Extending the prior-data fitted network (PFN) paradigm from prediction to explanation is a promising alternative, as it would let a TFM itself act as a cross-task Shapley estimator. However, the cost and error of generating attribution supervision have kept such models far below the pretraining scale of TFMs. We introduce XPFN, a TFM that produces zero-shot regression predictions together with their Shapley attributions in one forward pass. Our key design is to draw attribution supervision from the prior itself. A structural causal model generates the feature columns and a prior-sampled response function generates the target, so the function that defines each synthetic task can be queried under any feature coalition. This yields exact coalition labels without fitting a surrogate model or re-evaluating the TFM. On 40 real datasets, XPFN's direct attributions agree with the Shapley values of its own predictions (r=0.91), and its predictions are competitive with tuned tree ensembles such as XGBoost and CatBoost. Its explanations are up to 90$\times$ faster than TreeSHAP on XGBoost and over 1,700$\times$ faster than KernelSHAP on TabPFN-v3 and TabICLv2. Controlled ablations further show that our decoupled prior delivers more accurate and cheaper supervision labels than existing methods, which enables efficient pretraining of XPFN on over 10 million synthetic tabular tasks.