# Tabular Prediction

TabFM-Auto pairs the frozen tabular foundation model TabFM with a language-model coding agent that iteratively rewrites four pipeline stages—data cleaning, feature engineering, context selection, and post-processing—guided by dataset metadata and validation feedback; across all 51 TabArena datasets five configurations take the top five overall positions, the best (Codex with Opus 5) raises TabFM from 1785.3 to 2013.0 Elo, the discovered pipelines transfer without further search to TabPFN-3, TabICLv2, and EXAONE-Tabular (+69 to +143 Elo), and TabFM-Auto ranks first overall among MLE agents on the 8 tabular competitions of MLE-Bench.

TabFM-Auto pairs the frozen tabular foundation model TabFM with a language-model coding agent that iteratively rewrites four pipeline stages—data cleaning, feature engineering, context selection, and post-processing—guided by dataset metadata and validation feedback; across all 51 TabArena datasets five configurations take the top five overall positions, the best (Codex with Opus 5) raises TabFM from 1785.3 to 2013.0 Elo, the discovered pipelines transfer without further search to TabPFN-3, TabICLv2, and EXAONE-Tabular (+69 to +143 Elo), and TabFM-Auto ranks first overall among MLE agents on the 8 tabular competitions of MLE-Bench.

TabFM is a 400M-parameter tabular foundation model that formulates supervised tabular prediction as in-context learning, is pretrained entirely on synthetic tables generated from structural causal models, and produces calibrated zero-shot predictions in a single forward pass; across all 51 TabArena datasets (38 classification, 13 regression) zero-shot TabFM ranks first among default tabular foundation models and outperforms tuned AutoML pipelines, while its two extensions, TabFM+ (multi-view feature expansion, ensembling, and post-hoc calibration) and TabFM-Auto (LLM-guided, dataset-specific data processing and feature engineering), improve both tracks over the same frozen weights.

TabFM is a 400M-parameter tabular foundation model that formulates supervised tabular prediction as in-context learning, is pretrained entirely on synthetic tables generated from structural causal models, and produces calibrated zero-shot predictions in a single forward pass; across all 51 TabArena datasets (38 classification, 13 regression) zero-shot TabFM ranks first among default tabular foundation models and outperforms tuned AutoML pipelines, while its two extensions, TabFM+ (multi-view feature expansion, ensembling, and post-hoc calibration) and TabFM-Auto (LLM-guided, dataset-specific data processing and feature engineering), improve both tracks over the same frozen weights.