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TabPFN-3 advances tabular data prediction with speed and scale

By PulseAugur Editorial · [1 sources] ·

A new technical report introduces TabPFN-3, an advanced foundation model for tabular data that significantly enhances performance and speed. This model scales to datasets with up to 1 million training rows and offers substantial reductions in training and inference times compared to its predecessor, TabPFN-2.5. TabPFN-3 demonstrates state-of-the-art results on various benchmarks, including TabArena, and shows improved capabilities in time series, relational, and tabular-text data prediction. AI

IMPACT Sets new SOTA on tabular benchmarks, potentially accelerating adoption in scientific and industrial prediction tasks.

RANK_REASON The cluster contains a technical report detailing a new model release and benchmark results for tabular data. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

TabPFN-3 advances tabular data prediction with speed and scale

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The cluster contains a technical report detailing a new model release and benchmark results for tabular data. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · L\'eo Grinsztajn, Klemens Fl\"oge, Oscar Key, Felix Birkel, Philipp Jund, Brendan Roof, Mihir Manium, Shi Bin Hoo, Magnus B\"uhler, Anurag Garg, Dominik Safaric, Jake Robertson, Benjamin J\"ager, Simone Alessi, Adrian Hayler, Vladyslav Moroshan, Lennart … ·

    TabPFN-3: Technical Report

    arXiv:2605.13986v2 Announce Type: replace-cross Abstract: Tabular data underpins most high-value prediction problems in science and industry, and TabPFN has driven the foundation model revolution for this modality. Designed with feedback from our users, TabPFN-3 builds on this fo…

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