arXiv Machine Learning June 2, 2026 By Julien Lafrance

When Tabular Foundation Models Transfer Across Modalities: A Systematic Evaluation Across 95 Datasets, 7 Modalities, and Two Regimes

Read the original on arXiv Machine Learning →

arXiv:2606. 02106v1 Announce Type: new Abstract: We present a single classification pipeline that combines an Equiangular Tight Frame (ETF) preprocessing stage with a tabular foundation model for in-context inference, applied identically across modalities once data is mapped to fixed vector representations.

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

fine-tuning

arXiv Machine Learning

Jul 2

Explaining Tabular Foundation Model Differences Through Meta-Features

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

benchmarks

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 Machine Learning

Jul 14

TabPFN beyond Tabular Data: Calibration and Accuracy on Multimodal Embeddings

arXiv:2607. 11007v1 Announce Type: new Abstract: Few-shot multimodal classification commonly attaches a lightweight head, such as $k$-nearest neighbors, logistic regression, or a linear SVM, to a frozen pretrained encoder.

By Jingxiang Zhang, Lujia Zhong, Zijie Zhu, Shuo Huang, Yuang Xu

rag multimodal

arXiv Machine Learning

5d ago

TabH2O: A Unified Foundation Model for Tabular Prediction

arXiv:2605. 18383v2 Announce Type: replace Abstract: We present TabH2O, a foundation model for tabular data that performs classification and regression in a single forward pass via in-context learning.

By Pascal Pfeiffer, Dmitry Gordeev, Mathias M\"uller, Laura Fink, Joan Salv\`a Soler, Mark Landry, Branden Murray, Marcos V. Conde, Sri Satish Ambati

llms benchmarks

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

22h ago

Understanding the Surprising Generalization Properties of Tabular Foundation Models

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.

By Nour Shaheen, Junwei Ma, Alex Labach, Frank Hutter, Valentin Thomas, Anthony L. Caterini

llms

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Explaining Tabular Foundation Model Differences Through Meta-Features

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

TabPFN beyond Tabular Data: Calibration and Accuracy on Multimodal Embeddings

TabH2O: A Unified Foundation Model for Tabular Prediction

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

Understanding the Surprising Generalization Properties of Tabular Foundation Models