AI RESEARCH
High Performance, Low Reliability: Uncertainty Benchmarking for Tabular Foundation Models
arXiv CS.LG
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ArXi:2605.28554v1 Announce Type: new Recent Tabular Foundation Models (TFMs) have nstrated state-of-the-art predictive performance, often surpassing Gradient-Boosted Decision Trees (GBDTs). However, the trustworthiness of these models, particularly their uncertainty quantification, has been largely overlooked. We investigate this gap through an extensive study comparing TFMs, GBDTs, and classical baselines on the 112 datasets of the TALENT benchmark.