Beyond Accuracy: Toward Trustworthy Tabular Foundation Models in Industrial Applications

Tabular foundation models (TabFMs) show immense promise on academic benchmarks, but their transition to real-world industrial applications requires more than just predictive accuracy. In these high-stakes environments, properties like reliability, robustness, and ultimately, the trust of engineers, are paramount. We argue that to leverage these advanced models successfully, the evaluation focus must broaden from isolated accuracy metrics to a more holistic, application-centric view of model behavior. To illustrate what such an evaluation could entail, we present a targeted study of TabFMs on two industrial engine datasets. We showcase how performance is affected by key factors such as the number of estimators and the training data fraction. Furthermore, on controlled synthetic functions, we analyze the models’ uncertainty quantification in the presence of noise and their behavior under distribution shift, probing their interpolation versus extrapolation capabilities. Our investigations demand for model cards for TabFMs, TabFM-enabled data and model analysis routines, and holistic benchmarking for reliable and trustworthy application of TabFMs on industrial prediction tasks.