arXiv Machine Learning August 19, 2026 By Nour Shaheen, Junwei Ma, Alex Labach, Frank Hutter, Valentin Thomas, Anthony L. Caterini

Understanding the Surprising Generalization Properties of Tabular Foundation Models

Read the original on arXiv Machine Learning →

arXiv:2608. 17957v1 Announce Type: new Abstract: Tabular Foundation Models (TFMs) increasingly rely on in-context learning, where a model receives labelled examples at inference time and predicts labels for new inputs without updating its weights.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

llms

arXiv Machine Learning

Aug 4

TabDPT-Turbo: Efficient In-Context Learning for Tabular Prediction

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

llms efficiency

arXiv Machine Learning

Jun 26

LLMTabBench: Evaluating LLMs on Binary Tabular Classification From Zero to Few Shots

arXiv:2605. 24417v2 Announce Type: replace Abstract: Supervised classification on tabular data remains a central machine learning task, but its dependence on large labeled datasets limits its applicability in data-scarce settings.

By Daria Grushina, Kseniia Kuvshinova, Alina Kostromina, Aziz Temirkhanov, Mile Mitrovic, Dmitry Simakov

llms benchmarks

arXiv Machine Learning

Jul 16

Algorithmic Recourse of In-Context Learning for Tabular Data

arXiv:2605. 31272v2 Announce Type: replace Abstract: As predictive models are increasingly deployed in high-stakes settings such as credit approval, there is a growing need for post-hoc methods that provide recourse to affected individuals.

By Wenshuo Dong, Jiaming Zhang, Shaopeng Fu, Hongbin Lin, Di Wang, Lijie Hu

llms multimodal

arXiv AI

Jun 30

Beyond IID: How General Are Tabular Foundation Models, Really?

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

benchmarks

arXiv AI

Jun 4

OpenRFM: Dissecting Relational In-Context Learning

arXiv:2606. 04320v1 Announce Type: cross Abstract: Relational Foundation Models (RFMs) promise a single pre-trained predictor that, given any relational database, returns predictions in one forward pass via relational in-context learning (ICL).

By Zhikai Chen, Junyu Yin, Jialiang Gu, Siheng Xiong, Xiaoze Liu, Ruowang Zhang, Keren Zhou, Kai Guo

llms

arXiv Machine Learning

Jun 8

TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention

arXiv:2606. 07345v1 Announce Type: new Abstract: Tabular foundation models, exemplified by TabPFN, perform prediction via in-context learning, inferring test labels directly from labeled training examples.

By Si-Yang Liu, Han-Jia Ye

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Related stories

TabDPT-Turbo: Efficient In-Context Learning for Tabular Prediction

LLMTabBench: Evaluating LLMs on Binary Tabular Classification From Zero to Few Shots

Algorithmic Recourse of In-Context Learning for Tabular Data

Beyond IID: How General Are Tabular Foundation Models, Really?

OpenRFM: Dissecting Relational In-Context Learning

TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention