TabFM-Auto (AI model): news timeline & CVEs

TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models

TabFM-Auto uses an agent to evolve data pipelines and leads TabArena and MLE-Bench tabular results.

TabFM-Auto pairs the tabular foundation model TabFM with a language-model agent that iteratively improves data cleaning, feature engineering, context selection, and post-processing using dataset metadata and validation feedback. Across all 51 TabArena datasets, five configurations occupy the top five overall ranks, and the best raises TabFM from 1785 to 2013 Elo. Discovered pipelines transfer to other frozen tabular foundation models for gains of +69 to +143 Elo without further search. On the eight tabular competitions in MLE-Bench, TabFM-Auto ranks first among MLE agents.

Hugging Face daily papersSep 28, 20:00 UTC · 3d agoAI research 2 sources

TabFM: A Zero-Shot Foundation Model for Tabular Data

TabFM, a 400M tabular foundation model, delivers calibrated zero-shot predictions that beat tuned AutoML.

TabFM is a 400-million-parameter foundation model that treats supervised tabular prediction as in-context learning and outputs calibrated zero-shot predictions in one forward pass. It is trained only on synthetic tables generated from structural causal models. Across all 51 TabArena datasets, 38 classification and 13 regression, zero-shot TabFM ranks first among default tabular foundation models and beats tuned AutoML pipelines. Frozen-weight extensions TabFM+ and TabFM-Auto further improve both tracks.

Hugging Face daily papersSep 28, 20:00 UTC · 3d agoAI research 2 sources

TabFM-Auto

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TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models

TabFM: A Zero-Shot Foundation Model for Tabular Data