Xiaomi-TabLDM is a tabular foundation model that performs classification and regression via in-context learning without task‑specific fine‑tuning. It is pretrained solely on synthetic data from structural causal models, achieving top‑ranked regression results on multiple benchmarks while reducing training and prediction time compared to leading models. The architecture incorporates a three‑stage training strategy, dual‑stream feature grouping, lightweight attention residuals, and sparse mixture‑of‑experts, and it can further improve accuracy through test‑time compute scaling.
By TabLDM Team, Penghui Wang, Wei Liu, Hong Wang, Chengyue Huang, Yuxi Sun, Zirui Wang, Hongming Huang, Quan Wang, Chunxiao Liu, Erli Meng, Bin Wang
EXAONE Tabular 1.0 is a compact tabular foundation model family that performs classification and regression via in-context learning without dataset-specific gradient updates. It is pretrained exclusively on a synthetic structural‑causal‑model prior and introduces an architecture‑centered redesign that interleaves feature‑axis and item‑axis attention within each Transformer layer, mediated by summary tokens. Across four public benchmarks, its 20.81 M‑parameter classification model ranks first on TabArena, surpassing tuned ensembles and AutoML pipelines, while its regression model matches the performance of a 1.64 B‑parameter model at roughly one‑eleventh the inference cost, and it achieves top rankings on BCCO, TALENT, and ScoringBench.
By Moonjung Eo, Min-Kook Suh, Hye-Seung Cho, Jiwon Kim, Seoyoon Kim, Sangjun Nam, Soonyoung Lee
arXiv:2605. 28418v3 Announce Type: replace Abstract: With the rise of tabular foundation models alongside traditional models still performing well on many tasks, choosing the right model for a tabular dataset remains difficult.
By Markus Herre, Andrej Tschalzev, Sascha Marton, Christian Bartelt
arXiv:2606. 30410v1 Announce Type: cross Abstract: Foundation models for predictive machine learning on tabular data have recently gained significant traction in academia and industry.
By Lennart Purucker, Andrej Tschalzev, Nick Erickson, Gioia Blayer, David Holzm\"uller, Alan Arazi, Alexander Pfefferle, Mustafa Tajjar, Ga\"el Varoquaux, Frank Hutter
arXiv:2606. 15314v1 Announce Type: cross Abstract: Industrial retrofit planning depends on structured operational data rather than free text: planners must estimate whether a newly registered prototype will require a retrofit, which retrofit package it will need, and how long the work will take.
By Aina Vila Pons, Ioannis Tzachristas, Constantinos Antoniou
arXiv:2605. 18383v2 Announce Type: replace Abstract: We present TabH2O, a foundation model for tabular data that performs classification and regression in a single forward pass via in-context learning.
By Pascal Pfeiffer, Dmitry Gordeev, Mathias M\"uller, Laura Fink, Joan Salv\`a Soler, Mark Landry, Branden Murray, Marcos V. Conde, Sri Satish Ambati
arXiv:2606. 02106v1 Announce Type: new Abstract: We present a single classification pipeline that combines an Equiangular Tight Frame (ETF) preprocessing stage with a tabular foundation model for in-context inference, applied identically across modalities once data is mapped to fixed vector representations.
By Julien Lafrance
arXiv:2608. 02412v1 Announce Type: new Abstract: Large language models (LLMs) have become the default tool for a remarkable range of tasks, yet they have had conspicuously little success at one of the most common machine learning workloads: predictive analytics over tabular data.
By Marta Garnelo, Wojciech M. Czarnecki
arXiv:2606. 02384v1 Announce Type: new Abstract: Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures.
By Andrej Tschalzev, Nick Erickson, Yuyang Wang, Huzefa Rangwala, Stefan L\"udtke, Heiner Stuckenschmidt, Christian Bartelt
arXiv:2605. 31272v2 Announce Type: replace Abstract: As predictive models are increasingly deployed in high-stakes settings such as credit approval, there is a growing need for post-hoc methods that provide recourse to affected individuals.
By Wenshuo Dong, Jiaming Zhang, Shaopeng Fu, Hongbin Lin, Di Wang, Lijie Hu
The report introduces TabPFN‑3.5, a new flagship tabular foundation model that outperforms its predecessor TabPFN‑3 and all existing baselines on a wide range of tabular tasks. It achieves state‑of‑the‑art performance on standard tabular prediction in TabArena and extends to practical scenarios such as non‑i.i.d. data, temporal or grouped splits, tables containing strings, text, images, high‑cardinality categorical features, and wide tables. Variants like TabPFN‑3.5‑Fast, TabPFN‑3.5‑Plus, and TabPFN‑3.5‑Thinking offer faster inference, expanded multimodal capabilities, and further speed improvements up to 12× faster than the previous Thinking mode.
By Benjamin J\"ager, Nick Erickson, L\'eo Grinsztajn, Felix Birkel, Klemens Fl\"oge, Oscar Key, K\"ur\c{s}at Kaya, Jonas K\"ubler, Ad\`ele Frankel, Tobias Schr\"oder, Anurag Garg, Jan Hendrik Metzen, David Salinas, Simon Bing, Kristina Collins, Tuana \c{C}elik, Vahid Balazadeh, Lydia Sidhoum, Tom\'as 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 Sch\"olkopf, Yann LeCun, Madelon Hulsebos, Sauraj Gambhir, Noah Hollmann, Frank Hutter
arXiv:2609.13202v1 Announce Type: new
Abstract: Feature engineering has long been a cornerstone of tabular machine learning. Tabular foundation models (TFMs) are pretrained on a wide range of tabular...
By Yifan WU, Pinjun Dong, Jiran Tao, Binyan Jiang