Real-Time Explanations for Tabular Foundation Models

Luan Borges Teodoro Reis Sena; Francisco Galuppo Azevedo

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arXiv · 2603.29946

Real-Time Explanations for Tabular Foundation Models

Luan Borges Teodoro Reis Sena·Francisco Galuppo Azevedo

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Abstract

Interpretability is central for scientific machine learning, as understanding \emph{why} models make predictions enables hypothesis generation and validation. While tabular foundation models show strong performance, existing explanation methods like SHAP are computationally expensive, limiting interactive exploration. We introduce ShapPFN, a foundation model that integrates Shapley value regression directly into its architecture, producing both predictions and explanations in a single forward pass. On standard benchmarks, ShapPFN achieves competitive performance while producing high-fidelity explanations ($R^2$=0.96, cosine=0.99) over 1000\times faster than KernelSHAP (0.06s vs 610s). Our code is available at https://github.com/kunumi/ShapPFN

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Luan Borges Teodoro Reis Sena, Francisco Galuppo Azevedo. 2026-03-31. Real-Time Explanations for Tabular Foundation Models. https://arxiv.org/abs/2603.29946

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