Tabular foundation models excel on tiny- to medium-sized IID data but are outperformed by traditional tree-based and deep learning models on non-IID, large, and high-dimensional datasets, based on evaluations across 11 models and 142 datasets in the new BeyondArena benchmark.
arXiv preprint arXiv:2512.11668 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.LG 3years
2026 3representative citing papers
CURE is a context management policy using entropy-gated admission and redundancy-aware eviction that delivers up to 27% relative improvement over classical stream learners on seven streams while remaining robust across TFM backbones.
Disagreement measures from label flipping in IDT ensembles underperform loss-based drift detectors in streaming tabular data due to the limited plasticity of tree models.
citing papers explorer
-
Beyond IID: How General Are Tabular Foundation Models, Really?
Tabular foundation models excel on tiny- to medium-sized IID data but are outperformed by traditional tree-based and deep learning models on non-IID, large, and high-dimensional datasets, based on evaluations across 11 models and 142 datasets in the new BeyondArena benchmark.
-
Bounded Context Management for Tabular Foundation Models on Stream Learning
CURE is a context management policy using entropy-gated admission and redundancy-aware eviction that delivers up to 27% relative improvement over classical stream learners on seven streams while remaining robust across TFM backbones.
-
Pitfalls of Unlabeled Disagreement-Based Drift Detection in Streaming Tree Ensembles
Disagreement measures from label flipping in IDT ensembles underperform loss-based drift detectors in streaming tabular data due to the limited plasticity of tree models.