# EXAONE Tabular 1.0 : Technical Report

arXiv: https://arxiv.org/abs/2608.25774 · HTML: https://mlsanity.com/paper/2608.25774/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 32 references that are in the map.
- First posted on arXiv 26 Aug 2026.

## Builds on (most cited first, 21 of 21)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second](https://mlsanity.com/paper/2207.01848/) (2022)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Muon is Scalable for LLM Training](https://mlsanity.com/paper/2502.16982/) (2025)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Transformers Can Do Bayesian Inference](https://mlsanity.com/paper/2112.10510/) (2021)
- [TabICL: A Tabular Foundation Model for In-Context Learning on Large Data](https://mlsanity.com/paper/2502.05564/) (2025)
- [TabArena: A Living Benchmark for Machine Learning on Tabular Data](https://mlsanity.com/paper/2506.16791/) (2025)
- [Self-attention Does Not Need O(n2) Memory](https://mlsanity.com/paper/2112.05682/) (2021)
- [TabICLv2: A better, faster, scalable, and open tabular foundation model](https://mlsanity.com/paper/2602.11139/) (2026)
- [TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models](https://mlsanity.com/paper/2511.08667/) (2025)
- [Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data](https://mlsanity.com/paper/2407.04491/) (2024)
- [Understanding Warmup-Stable-Decay Learning Rates: A River Valley Loss Landscape Perspective](https://mlsanity.com/paper/2410.05192/) (2024)
- [LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence](https://mlsanity.com/paper/2509.03505/) (2025)
- [TALENT: A Tabular Analytics and Learning Toolbox](https://mlsanity.com/paper/2407.04057/) (2024)
- [Scalable-Softmax Is Superior for Attention](https://mlsanity.com/paper/2501.19399/) (2025)
- [Sequential Deep Learning for Credit Risk Monitoring with Tabular Financial Data](https://mlsanity.com/paper/2012.15330/) (2020)
- [TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention](https://mlsanity.com/paper/2606.07345/) (2026)
- [ScoringBench: A Benchmark for Evaluating Tabular Foundation Models with Proper Scoring Rules](https://mlsanity.com/paper/2603.29928/) (2026)
- [Retrieval-aligned Tabular Foundation Models Enable Robust Clinical Risk Prediction in Electronic Health Records Under Real-world Constraints](https://mlsanity.com/paper/2604.01841/) (2026)

## Built on by (most cited first, 10 of 10)

- [LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence](https://mlsanity.com/paper/2609.17488/) (2026)
- [Mitra-v2 Technical Report](https://mlsanity.com/paper/2609.04540/) (2026)
- [Causilo Technical Report](https://mlsanity.com/paper/2609.22866/) (2026)
- [What Do Tabular Foundation Models Compute In Context? In-Situ Representation Refinement through Attention-Gated Updates](https://mlsanity.com/paper/2609.27679/) (2026)
- [Support-Compiled Feature Folding: More Evidence at Lower Memory Across Tabular Foundation Models](https://mlsanity.com/paper/2609.28208/) (2026)
- [LoopICL: Looping a single transformer block to solve tabular tasks](https://mlsanity.com/paper/2609.36108/) (2026)
- [Parameter-Efficient Distributionally Robust Adaptation of Tabular Foundation Models under Subpopulation Shift](https://mlsanity.com/paper/2610.01143/) (2026)
- [How Well Do Prior-Data Fitted Networks Amortize Bayesian Inference?](https://mlsanity.com/paper/2610.13764/) (2026)
- [TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings](https://mlsanity.com/paper/2610.17065/) (2026)
- [OrionMoE-v1.0: A Mixture-of-Experts Foundation Model for Tabular In-Context Learning](https://mlsanity.com/paper/2610.20418/) (2026)

## Abstract (arXiv)

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 R2, 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.