AI RESEARCH
Ensembling Tabular Foundation Models - A Diversity Ceiling And A Calibration Trap
arXiv CS.LG
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ArXi:2605.18696v1 Announce Type: new Tabular foundation models (TFMs) now match or beat tuned gradient-boosted trees on a growing fraction of tabular tasks, but no single TFM wins on every dataset. Ensembling is the go to fix here, and it works less well than expected. Six modern TFMs form a near-redundant pool: their mean pairwise Q-statistic is $0.961$, close enough to $1$ that any convex combination is bounded above. We benchmark six ensemble strategies over six TFMs on 153 OpenML classification tasks.