arXiv:2606. 04485v1 Announce Type: new Abstract: Tabular foundation models (TFMs) increasingly rival tree ensembles, but their performance is often compute-inefficient: with standard affine scalar tokenization, each feature injects value variation through an essentially one-dimensional channel, and feature IDs/positional signals cannot increase within-feature value degrees of freedom, yielding weak early-layer value sensitivity and redundant hidden states.
By Yuanrui Wang, Xingxuan Zhang, Han Yu, Mingchao Ming, Gang Ren, Hao Yuan, Li Mao, Yunjia Zhang, Chun Yuan, Peng Cui
Support-Compiled Feature Folding (SCFF) is a training‑free inference framework that addresses the feature‑side scaling dilemma in tabular foundation models by routing support‑ranked features through bounded leaves of the native encoder, checking residual evidence, and merging encoded messages for a single contextual prediction. This approach transforms quadratic pairwise mixing into linear‑in‑width work with a bounded local working set, achieving dataset‑macro accuracy and NLL improvements across six backbones on an 18‑dataset wide‑table slice. SCFF delivers significant GPU‑memory savings (median 2.09×–2.36×) and, when constrained by a peak‑memory ceiling, further boosts accuracy by up to 4.06 points over the widest single‑leaf baseline.
whyItMatters":"SCFF demonstrates that memory‑efficient inference can simultaneously improve accuracy and reduce resource usage in tabular foundation models, offering a practical solution for deploying these models at scale."
By Tian Zhou, Beverly Jin, Xue Wang, Linxiao Yang, Wenwei Wang, Bingqing Peng, Mengni Ye, Jinjie Gu, Liang Sun
arXiv:2606. 02384v1 Announce Type: new Abstract: Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures.
By Andrej Tschalzev, Nick Erickson, Yuyang Wang, Huzefa Rangwala, Stefan L\"udtke, Heiner Stuckenschmidt, Christian Bartelt
arXiv:2607. 27546v1 Announce Type: new Abstract: Tabular Foundation Models, such as TabPFN, have received a large amount of recent attention due to their performance on in-context tabular machine learning tasks, which often exceeds classical baselines.
By Shuting Luo, Monika Mikhail Kanaan, Cameron Gordon, Anna Leontjeva, Simon Lucey
arXiv:2606. 04876v1 Announce Type: new Abstract: Tabular foundation models, such as TabPFN, achieve strong performance on tabular datasets with numerical and categorical data, but do not natively handle high-cardinality text features.
By Mustafa Tajjar, Alexander Pfefferle, Lennart Purucker, Frank Hutter
arXiv:2606. 30258v1 Announce Type: cross Abstract: Tabular foundation models have advanced deep learning for tabular data by delivering strong default performance across many small and medium tasks.
By Boshko Koloski, Xiangjian Jiang, Senja Pollak, Bla\v{z} \v{S}krlj, Mateja Jamnik, Nikola Simidjievski
arXiv:2607. 10077v1 Announce Type: new Abstract: Tabular learning is still dominated by gradient-boosted decision trees (GBDTs), while recent deep learning approaches have become increasingly competitive.
By Jiaqi Luo, Shixin Xu
arXiv:2606. 30410v1 Announce Type: cross Abstract: Foundation models for predictive machine learning on tabular data have recently gained significant traction in academia and industry.
By Lennart Purucker, Andrej Tschalzev, Nick Erickson, Gioia Blayer, David Holzm\"uller, Alan Arazi, Alexander Pfefferle, Mustafa Tajjar, Ga\"el Varoquaux, Frank Hutter
Causilo is a new tabular foundation model that delivers state‑of‑the‑art predictive performance while achieving exceptionally fast inference. On the TabArena benchmark it scores 1785.4 Elo with a median inference time of 0.10 seconds per 1 K test samples, outperforming TabPFN‑3.5‑Fast by 31.6% in speed and reaching the performance–efficiency Pareto frontier. The architecture builds on TabICL’s column‑then‑row design, adding a row‑refinement module that exchanges information among cell representations before a final column stage, and uses cross‑attention with a fixed number of summary tokens to keep attention cost linear in the number of features.
By Minyong Cho, Minho Jeong, Dooho Lee, Jinmo Lee, Jaemin Yoo
arXiv:2608. 12989v1 Announce Type: new Abstract: Pretrained tabular foundation models have demonstrated strong predictive capability; however, their application to large-scale datasets remains constrained by the limited inference context.
By Mahboobe Jadid, Melika Rezaye Garkani, Ali Mousavi
Tydra is a hybrid Transformer‑State Space Model that interleaves attention and SSM layers for tabular in‑context learning. It achieves a 30% reduction in inference time compared to the Transformer‑only TabPFN while preserving most of its predictive performance. On 30 OpenML datasets, Tydra also outperforms a Hydra model that is roughly ten times larger, demonstrating that hybrid architectures can balance accuracy and efficiency for tabular foundation models.
By Mieszko Komisarczyk, Saurabh Mathur, Maurice Kraus, Sriraam Natarajan, Kristian Kersting
arXiv:2609.13202v1 Announce Type: new
Abstract: Feature engineering has long been a cornerstone of tabular machine learning. Tabular foundation models (TFMs) are pretrained on a wide range of tabular...
By Yifan WU, Pinjun Dong, Jiran Tao, Binyan Jiang