Releases·Model Release
NVIDIA releases Kumo Tabular, an open tabular foundation model ranking first on TabArena, BeyondArena, TALENT, and ScoringBench
NVIDIA released Kumo Tabular, an open foundation model for tabular classification and regression that predicts labels in a single forward pass with no training, tuning, or feature engineering. Weights and code are on Hugging Face and GitHub under the OpenMDW-1.1 license, and it ranks first on TabArena, BeyondArena, TALENT, and ScoringBench. It challenges the two-decade dominance of gradient-boosted trees in enterprise tabular ML.
1 engagement2 sourcesTue, Sep 29, 2026, 03:30 PM
AI Overview
- NVIDIA releases Kumo Tabular, an open tabular foundation model on Hugging Face predicting labels in a single forward pass with no training or feature engineering.
- The 28M-215M parameter Transformer, pretrained only on 35-137 million artificial SCM-generated tables, uses column, row, and in-context attention.
- Kumo Tabular tops TabArena with ELO 1950, running 17x faster than LimiX-2, and ranks first on BeyondArena (ELO 1418), TALENT, and ScoringBench.
- The model handles numerical and categorical columns only, supports up to 10 classes per forward pass, and is licensed OpenMDW-1.1 for commercial use.
Related
Developments·Paper
TabFM: 400M-Parameter Zero-Shot Foundation Model Tops TabArena Across 51 Datasets
Two arXiv papers (2026-09-29) present TabFM, a 400M-parameter tabular foundation model doing zero-shot tabular prediction as in-context learning, trained entirely on synthetic tables from structural causal models. Zero-shot TabFM ranks first among default tabular foundation models across all 51 TabArena datasets and beats tuned AutoML pipelines. A companion system, TabFM-Auto, pairs TabFM with an LLM agent that evolves the data pipeline, sweeping the top five TabArena positions and ranking first among MLE agents on MLE-Bench.
Two arXiv papers (2026-09-29) present TabFM, a 400M-parameter tabular foundation model doing zero-shot tabular prediction as in-context learning, trained entirely on synthetic tables from structural causal models. Zero-shot TabFM ranks first among default tabular foundation models across all 51 TabArena datasets and beats tuned AutoML pipelines. A companion system, TabFM-Auto, pairs TabFM with an LLM agent that evolves the data pipeline, sweeping the top five TabArena positions and ranking first among MLE agents on MLE-Bench.
0 engagement2 sourcesarxivTue, Sep 29, 2026, 04:30 PM
Releases·Model Release
LG AI Research releases EXAONE Tabular 1.0, a tabular foundation model for in-context learning
LG AI Research released EXAONE Tabular 1.0, a compact tabular foundation model family for classification and regression via in-context learning, eliminating the need for dataset-specific gradient updates. The model is pretrained exclusively on a synthetic structural-causal-model (SCM) prior and introduces an architecture-centered redesign of tabular in-context learning.
LG AI Research released EXAONE Tabular 1.0, a compact tabular foundation model family for classification and regression via in-context learning, eliminating the need for dataset-specific gradient updates. The model is pretrained exclusively on a synthetic structural-causal-model (SCM) prior and introduces an architecture-centered redesign of tabular in-context learning.
0 engagement1 sourcearxivWed, Aug 26, 2026, 01:17 PM
Releases·Model Release
Prior Labs releases TabPFN-3.5, SOTA tabular foundation model topping TabArena and BeyondArena
Prior Labs released TabPFN-3.5, a tabular foundation model that handles up to 1M rows and 20,000 features and tops the TabArena and BeyondArena benchmarks. It ships in three variants — Fast, Thinking, and Plus — trading speed against accuracy. The company is hosting a hackathon encouraging agents to use TabPFN-3.5 as a specialized prediction tool.
Prior Labs released TabPFN-3.5, a tabular foundation model that handles up to 1M rows and 20,000 features and tops the TabArena and BeyondArena benchmarks. It ships in three variants — Fast, Thinking, and Plus — trading speed against accuracy. The company is hosting a hackathon encouraging agents to use TabPFN-3.5 as a specialized prediction tool.
03019 engagement3 sourcesreddit, hackernewsTue, Sep 15, 2026, 04:18 PM
Developments·Paper
Mitra-v2 technical report: 77M-parameter synthetic-data tabular foundation model matches 1.6B TabFM
A technical report introduces Mitra-v2, a tabular foundation model claiming state-of-the-art performance on real-world classification and regression tasks including credit-risk scoring, clinical prediction, and house-price estimation. It is trained entirely on synthetic data using a small 2D Transformer backbone with longer contexts and larger feature spaces than Mitra-v1.
A technical report introduces Mitra-v2, a tabular foundation model claiming state-of-the-art performance on real-world classification and regression tasks including credit-risk scoring, clinical prediction, and house-price estimation. It is trained entirely on synthetic data using a small 2D Transformer backbone with longer contexts and larger feature spaces than Mitra-v1.
0 engagement1 sourcearxivThu, Sep 3, 2026, 10:55 PM
News·Company News
NVIDIA CEO Jensen Huang creates X account, first post defends open AI models
NVIDIA CEO Jensen Huang created his official X (formerly Twitter) account on July 24, 2026, and his first post expressed support for open AI models. The account was created just an hour before a Reddit post highlighted it, signaling a notable public stance from the head of a major AI hardware company.
NVIDIA CEO Jensen Huang created his official X (formerly Twitter) account on July 24, 2026, and his first post expressed support for open AI models. The account was created just an hour before a Reddit post highlighted it, signaling a notable public stance from the head of a major AI hardware company.
2001.1k engagement3 sourcesreddit, hackernewsFri, Jul 24, 2026, 03:00 PM