Mitra‑v2 is a tabular foundation model that achieves state‑of‑the‑art performance on a wide range of real‑world classification and regression tasks, including credit‑risk scoring, clinical prediction, equipment‑failure detection, and house‑price estimation. Trained solely on synthetic data with a larger and more diverse pretraining distribution than its predecessor, it uses a compact 2D Transformer backbone and improved optimization to handle longer contexts and larger feature spaces. On the TabArena and TALENT benchmarks, Mitra‑v2 outperforms leading models such as TabPFN‑3 and TabICLv2, matching the performance of a 1.6B‑parameter TabFM with only 77M parameters, and ranks first on multi‑class classification tasks with more than ten classes.
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.
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.
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.
TabBench-Bio is a living, interactive benchmark that evaluates machine learning models on 43 high‑dimensional biomedical tables, covering multiple domains. Using a shared cross‑validation protocol, the benchmark compares classical estimators, neural networks, and tabular foundation models across 28 feature‑by‑sample operating points, with RealTabPFN v2.5 achieving the highest performance at the reference cell of 10,000 features and 100 training samples. The benchmark provides reproducible results, fold‑level predictions, and invites community contributions to expand its dataset collection.
arXiv:2602.11139v2 Announce Type: replace Abstract: Tabular foundation models, such as TabPFNv2 and TabICL, have recently dethroned gradient-boosted trees at the top of predictive benchmarks, demonst...
arXiv:2605. 18147v2 Announce Type: replace Abstract: Predictive models play a pivotal role in credit risk management, guiding critical decisions through accurate estimation of default probabilities and losses.
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.
arXiv:2607. 26000v1 Announce Type: cross Abstract: Tabular Foundation Models (TFMs) have emerged as novel approaches for tabular predictive tasks, demonstrating competitive predictive performance to ensemble tree-based models.
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.
arXiv:2608. 06137v1 Announce Type: new Abstract: Tabular data are ubiquitous in real-world applications and are crucial for data-driven prediction and decision-making across science, industry, finance, healthcare, and public services.
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.
arXiv:2606. 12006v1 Announce Type: cross Abstract: Predicting time-to-event outcomes such as mortality is a fundamental task in clinical decision-making, commonly addressed through survival analysis.