Ensembling Tabular Foundation Models: A Diversity Ceiling and a Calibration Trap

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 textbook fix, 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. The best ensemble, two-level cascade stacking, buys +0.18% accuracy over the strongest single TFM at 253× the compute . A Friedman–Nemenyi analysis places three ensembles and the best base TFM in a single equivalence group; three other ensembles are significantly worse than the best base. Stacking with a logistic-regression meta-learner is the most striking case: competitive accuracy and ROC-AUC, the worst log-loss rank among the ensembles. The meta-learner improves accuracy by sharpening class boundaries, which destroys calibration. We recommend greedy selection as the practical default.