EXAONE Tabular 1.0 : Technical Report

• URL: http://arxiv.org/abs/2608.25774v1
• Date: Wed, 26 Aug 2026 13:17:20 GMT
• Title: EXAONE Tabular 1.0 : Technical Report
• Abstract summary: EXAONE Tabular is a compact model family for classification and regression.<n>It produces predictions without dataset-specific gradient updates.<n>It combines strong predictive performance with high efficiency.
• Score: 12.091751977253866
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: EXAONE Tabular is a compact tabular foundation model family for classification and regression via in-context learning, producing predictions without dataset-specific gradient updates. Pretrained exclusively on a synthetic structural-causal-model (SCM) prior, its central contribution is an architecture-centered redesign of tabular in-context learning. Rather than compressing features into a fixed row embedding before a separate row-level learner, EXAONE Tabular interleaves feature-axis attention within each item with support-conditioned item-axis attention within each feature at every Transformer layer, mediated by item-summary and feature-summary tokens. Across four public benchmarks, EXAONE Tabular combines strong predictive performance with high efficiency. On TabArena, its 20.81M-parameter classification model ranks first overall, surpassing tuned ensembles and 4-hour AutoML pipelines, while regression reaches the performance regime of the 1.64B-parameter TabFM at roughly 1/11 the inference cost. On BCCO and TALENT, EXAONE Tabular ranks second in classification and first in regression. On ScoringBench, it achieves the best mean rank for both point-estimation and predictive-distribution quality, leading the $R^2$, RMSE, and CRPS evaluations. Together, these results establish EXAONE Tabular as a state-of-the-art compact tabular foundation model family, combining strong predictive performance across classification, point regression, and probabilistic regression with an efficient model design.

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