Xiaomi-TabLDM: A Tabular Foundation Model Technical Report

• URL: http://arxiv.org/abs/2609.03880v2
• Date: Fri, 04 Sep 2026 03:23:21 GMT
• Title: Xiaomi-TabLDM: A Tabular Foundation Model Technical Report
• Abstract summary: Xiaomi-TabLDM is a model for classification and regression via in-context learning.<n>It ranks 1st on OpenML-CTR23 and 2nd on regression across TALENT, TabArena, and BCCO.<n>It combines strong predictive performance with substantially lower computational cost.
• Score: 30.675451775869274
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Xiaomi-TabLDM, a tabular large data foundation model for classification and regression via in-context learning, which delivers superior prediction accuracy without requiring task-specific fine-tuning. Pretrained exclusively on synthetic data generated from structural causal models (SCMs), our model enables more flexible context utilization and more efficient capacity scaling. i) A new performance standard. Strong regression performance across benchmarks: Xiaomi-TabLDM ranks 1st on OpenML-CTR23 and 2nd on regression across TALENT, TabArena, and BCCO, demonstrating consistently strong regression performance across four complementary benchmark suites. Favorable performance--efficiency trade-off: Xiaomi-TabLDM combines strong predictive performance with substantially lower computational cost. For example, on TabArena regression, it achieves the second-highest Elo while using 82% less training time and 68% less prediction time than the top-ranked TabFM. ii) Large-scale synthetic pretraining. Xiaomi-TabLDM expands the coverage and diversity of synthetic tabular data used for pretraining. We also adopt a three-stage training strategy together with dual-stream feature grouping, lightweight Attention Residual, and sparse Mixture-of-Experts, enabling Xiaomi-TabLDM to learn richer feature interactions and expert specialization across diverse tabular tasks. iii) Test-time scaling. Xiaomi-TabLDM further extends tabular prediction through test-time compute scaling, where allocating additional computation at inference time consistently improves predictive performance over the base model.

Related papers

• EXAONE Tabular 1.0 : Technical Report [12.091751977253866]
  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.
  arXiv  Detail & Related papers  (2026-08-26T13:17:20Z)
• LLaDA MoE v2: Scaling Mixture-of-Experts Diffusion Language Models [129.64558694024126]
  Diffusion language models (dLLMs) offer an alternative to autoregressive (AR) language modeling.<n>We characterize how optimization hyper parameters, compute allocation, and architecture scale for MoE dLLMs.<n>We train LLaDA MoE v2, a 30B-A3B dLLM, from scratch on 23.5T tokens.
  arXiv  Detail & Related papers  (2026-08-04T10:53:02Z)
• TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention [47.480923979431886]
  Tabular foundation models perform prediction via in-context learning, inferring test labels directly from labeled training examples.<n>Recent models often improve accuracy with increasingly complex architectures, incurring higher inference cost and limiting practical deployment.<n>We show that a lightweight row-wise attention-only backbone can remain highly competitive with two simple enhancements.
  arXiv  Detail & Related papers  (2026-06-05T14:55:30Z)
• KumoRFM-2: Scaling Foundation Models for Relational Learning [48.88283881350858]
  KumoRFM-2 is the next iteration of a pre-trained foundation model for relational data.<n>It supports in-context learning as well as fine-tuning and is applicable to a wide range of predictive tasks.
  arXiv  Detail & Related papers  (2026-04-14T11:24:11Z)
• TabICLv2: A better, faster, scalable, and open tabular foundation model [18.594859017648346]
  We introduce TabICLv2, a new state-of-the-art foundation model for regression and classification built on three pillars.<n>Tabiclv2 generalizes effectively to million-scale datasets under 50GB GPU memory while being markedly faster than RealTabPFN-2.5.
  arXiv  Detail & Related papers  (2026-02-11T18:51:02Z)
• Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models [85.64873567417396]
  We introduce Mitra, a TFM trained on a curated mixture of synthetic priors selected for their diversity, distinctiveness, and performance on real-world data.<n>Mitra consistently outperforms state-of-the-art TFMs, such as TabPFNv2 and TabICL, across both classification and regression benchmarks.
  arXiv  Detail & Related papers  (2025-10-24T07:15:06Z)
• SPaRFT: Self-Paced Reinforcement Fine-Tuning for Large Language Models [51.74498855100541]
  Large language models (LLMs) have shown strong reasoning capabilities when fine-tuned with reinforcement learning (RL)<n>We propose textbfSPaRFT, a self-paced learning framework that enables efficient learning based on the capability of the model being trained.
  arXiv  Detail & Related papers  (2025-08-07T03:50:48Z)
• A Closer Look at TabPFN v2: Understanding Its Strengths and Extending Its Capabilities [51.08999772842298]
  Tabular Prior-data Fitted Network v2 (TabPFN v2) achieves unprecedented in-context learning performance across diverse downstream datasets.<n>We show that TabPFN v2 can infer attribute relationships even when provided with randomized attribute token inputs.<n>We demonstrate that TabPFN v2's limitations can be addressed through a test-time divide-and-context strategy.
  arXiv  Detail & Related papers  (2025-02-24T17:38:42Z)
• TabDPT: Scaling Tabular Foundation Models on Real Data [20.00390825519329]
  We propose an approach to combine ICL-based retrieval with self supervised learning to train foundation models.<n>We show that incorporating real data during the pre-training phase can lead to significantly faster training and better generalization to unseen data.<n>Our resulting model, TabDPT, achieves top performance on both regression (CTR23) and classification (CC18) benchmarks.
  arXiv  Detail & Related papers  (2024-10-23T18:00:00Z)
• UniPredict: Large Language Models are Universal Tabular Classifiers [33.811778526930745]
  This paper exploits the idea of building universal tabular data predictors based on generative modeling, namely UniPredict. We train a single LLM on an aggregation of 169 datasets with diverse targets and compare its performance against baselines that are trained on each dataset separately. We observe this versatile UniPredict model demonstrates an advantage over other models, ranging from 5.4% to 13.4%, when compared with the best tree-boosting baseline and the best neural network baseline.
  arXiv  Detail & Related papers  (2023-10-05T02:37:09Z)
• Generative Table Pre-training Empowers Models for Tabular Prediction [71.76829961276032]
  We propose TapTap, the first attempt that leverages table pre-training to empower models for tabular prediction. TapTap can generate high-quality synthetic tables to support various applications, including privacy protection, low resource regime, missing value imputation, and imbalanced classification. It can be easily combined with various backbone models, including LightGBM, Multilayer Perceptron (MLP) and Transformer.
  arXiv  Detail & Related papers  (2023-05-16T06:37:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.