arXiv Machine LearningBy Benjamin J\"ager, Nick Erickson, L\'eo Grinsztajn, Felix Birkel, Klemens Fl\"oge, Oscar Key, K\"ur\c{s}at Kaya, Jonas K\"ubler, Ad\`ele Frankel, Tobias Schr\"oder, Anurag Garg, Jan Hendrik Metzen, David Salinas, Simon Bing, Kristina Collins, Tuana \c{C}elik, Vahid Balazadeh, Lydia Sidhoum, Tom\'as Pereda, Brendan Roof, Andrej Tschalzev, Siyuan Guo, Philipp Singer, Lennart Purucker, Jake Robertson, Marie Salmon, Philipp Jund, Jerry Chen, Diana Kriuchkova, Arthur Cahu, Eliott Kalfon, Adrian Hayler, Georg Grab, Vitor Monteiro, Lilly Wehrhahn, Dominik Safaric, Clara Cornu, Alan Arazi, Rylee Grace, Simone Alessi, Mihir Manium, Bernhard Sch\"olkopf, Yann LeCun, Madelon Hulsebos, Sauraj Gambhir, Noah Hollmann, Frank Hutter
The report introduces TabPFN‑3.5, a new flagship tabular foundation model that outperforms its predecessor TabPFN‑3 and all existing baselines on a wide range of tabular tasks. It achieves state‑of‑the‑art performance on standard tabular prediction in TabArena and extends to practical scenarios such as non‑i.i.d. data, temporal or grouped splits, tables containing strings, text, images, high‑cardinality categorical features, and wide tables. Variants like TabPFN‑3.5‑Fast, TabPFN‑3.5‑Plus, and TabPFN‑3.5‑Thinking offer faster inference, expanded multimodal capabilities, and further speed improvements up to 12× faster than the previous Thinking mode.
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arXiv:2608. 01400v1 Announce Type: new Abstract: Tabular foundation models, driven by in-context learning, have rapidly grown in quality and popularity.
By Rasa Hosseinzadeh, Alex Labach, Zexin Xue, Shuyi Han, Valentin Thomas, Anthony L. Caterini
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
arXiv:2602.11139v2 Announce Type: replace
Abstract: Tabular foundation models, such as TabPFNv2 and TabICL, have recently dethroned gradient-boosted trees at the top of predictive benchmarks, demonst...
By Jingang Qu, David Holzm\"uller, Ga\"el Varoquaux, Marine Le Morvan
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
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:2605. 28418v3 Announce Type: replace Abstract: With the rise of tabular foundation models alongside traditional models still performing well on many tasks, choosing the right model for a tabular dataset remains difficult.
By Markus Herre, Andrej Tschalzev, Sascha Marton, Christian Bartelt