PAPER / ARXIV:2609.17895
Benjamin Jäger , Nick Erickson , Léo Grinsztajn , Felix Birkel , Klemens Flöge , Oscar Key , Kürşat Kaya , Jonas Kübler , Adèle Frankel , Tobias Schröder , Anurag Garg , Jan Hendrik Metzen , David Salinas , Simon Bing , Kristina Collins , Tuana Çelik , Vahid Balazadeh , Lydia Sidhoum , Tomás 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 Schölkopf , Yann LeCun , Madelon Hulsebos , Sauraj Gambhir , Noah Hollmann , Frank Hutter
RESUMO
We introduce TabPFN-3.5, our new flagship Tabular Foundation Model. It significantly outperforms its predecessor, TabPFN-3, and all existing baselines across a broad range of tabular problems. TabPFN-3.5 sets a new state of the art on standard tabular prediction in TabArena, and extends it to the data practitioners encounter in practice: non-i.i.d. data with temporal or grouped splits, tables with strings, text and images, high-cardinality categorical features, and wide tables with many features. These gains carry over to our task-specific harnesses: state of the art on relational data and stronger time-series forecasting. For faster inference, our variant TabPFN-3.5-Fast runs up to 3x faster than TabPFN-3 while keeping most of the accuracy gains. In addition, we upgrade TabPFN-3.5-Plus, expanding our multimodal capabilities with advanced text and date handling alongside proprietary inference optimizations. Finally, we release a new version of our Thinking mode, TabPFN-3.5-Thinking, which scales inference-time computation to push the state of the art further. It benefits from our stronger base model and from inference-time improvements that make it up to 12x faster than TabPFN-3-Thinking.
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