AI RESEARCH
Real-Time Explanations for Tabular Foundation Models
arXiv CS.LG
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ArXi:2603.29946v1 Announce Type: new 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