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Tabular foundation models show improved generalization with less data, reducing need for feature engineering

By PulseAugur Editorial · [9 sources] ·

Recent research indicates that tabular foundation models (TFMs) can achieve generalization capabilities with significantly less data than previously thought, with some models showing strong transfer learning from a single pre-training table. Furthermore, the necessity of manual feature engineering for TFMs is diminishing, especially for the latest generations of models, as they become more adept at learning representations directly from raw data. However, providing additional task-relevant context remains a key factor in improving TFM performance. AI

IMPACT These advancements suggest a future where tabular data analysis requires less manual effort and can leverage more generalized models, potentially accelerating development and improving accuracy across various applications.

RANK_REASON The cluster contains multiple academic papers and a product release detailing advancements in tabular foundation models, including new research findings and benchmark performance.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 9 sources. How we write summaries →

Tabular foundation models show improved generalization with less data, reducing need for feature engineering

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The cluster contains multiple academic papers and a product release detailing advancements in tabular foundation models, including new research findings and benchmark performance.
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COVERAGE [9]

  1. arXiv cs.LG TIER_1 English(EN) · Jingang Qu, David Holzm\"uller, Ga\"el Varoquaux, Marine Le Morvan ·

    TabICLv2: A better, faster, scalable, and open tabular foundation model

    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, demonstrating the value of in-context learning for tabular data. We introduce TabICLv2, …

  2. arXiv cs.LG TIER_1 English(EN) · Lu Han, Jin Wang, Yuchen Li, Haoran Gu, Shulei Liu, Ziyang Shi, Wenao Lu, Handing Wang ·

    Benchmarking Tabular Foundation Models as Surrogates in Expensive Evolutionary Optimization

    arXiv:2609.18130v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) are effective methods for solving expensive optimization problems (EOPs), where surrogate models replace most expensive evaluations and critically influence the final optimization…

  3. arXiv cs.AI TIER_1 English(EN) · Junwei Ma, Nour Shaheen, Alex Labach, Amine Mhedhbi, Frank Hutter, Anthony L. Caterini, Valentin Thomas ·

    Generalization Can Emerge in Tabular Foundation Models From a Single Table

    arXiv:2511.09665v1 Announce Type: cross Abstract: Deep tabular modelling increasingly relies on in-context learning where, during inference, a model receives a set of $(x,y)$ pairs as context and predicts labels for new inputs without weight updates. We challenge the prevailing v…

  4. arXiv cs.LG TIER_1 English(EN) · Yifan WU, Pinjun Dong, Jiran Tao, Binyan Jiang ·

    Do Tabular Foundation Models Still Need Feature Engineering?

    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 datasets and applied via in-context learning. Their rise raises a natural questi…

  5. MarkTechPost TIER_1 English(EN) · Asif Razzaq ·

    Prior Labs Releases TabPFN-3.5: A Tabular Foundation Model That Beats the Winning Otto Kaggle Solution With Default Settings

    <p>Prior Labs released TabPFN-3.5, a tabular foundation model pretrained only on synthetic data that beats Otto's winning solution.</p> <p>The post <a href="https://www.marktechpost.com/2026/09/15/prior-labs-releases-tabpfn-3-5-a-tabular-foundation-model-that-beats-the-winning-ot…

  6. MarkTechPost TIER_1 English(EN) · Michal Sutter ·

    Nums AI Releases Causilo: A Tabular Foundation Model That Tops TabArena Among Single Models

    <p>Nums AI has released Causilo, a pretrained tabular foundation model for classification and regression with a scikit-learn interface. It posts the top TabArena Elo among single models, ahead of Google's TabFM and LG's EXAONE Tabular. The code is Apache-2.0, while weights are li…

  7. Mastodon — sigmoid.social TIER_1 English(EN) · [email protected] ·

    Prior Labs has released TabPFN-3.5, a tabular foundation model that beats the winning Otto Kaggle solution with default settings. The model was pretrained only

    Prior Labs has released TabPFN-3.5, a tabular foundation model that beats the winning Otto Kaggle solution with default settings. The model was pretrained only on synthetic data and scores 0.375 on the private leaderboard. https://www. marktechpost.com/2026/09/15/pr ior-labs-rele…

  8. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    Nums AI has released Causilo, a pretrained tabular foundation model for classification and regression. The model tops TabArena rankings among single models for

    Nums AI has released Causilo, a pretrained tabular foundation model for classification and regression. The model tops TabArena rankings among single models for both classification and regression tasks, using an in-context learning approach. https://www. marktechpost.com/2026/09/1…

  9. r/MachineLearning TIER_1 English(EN) · /u/tuanacelik ·

    TabPFN-3.5 is released as the next SOTA tabular foundation model [N]

    <!-- SC_OFF --><div class="md"><p>Prior Labs released their latest tabular foundation model, TabPFN-3.5 today.</p> <p>The model is top of both TabArena and BeyondArena and SOTA for 1M rows and up to 20k features</p> <p>It comes with:</p> <p>- TabPFN-3.5-Fast (in alpha): This one …

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