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Xiaomi unveils TabLDM, a tabular foundation model trained on synthetic data

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Xiaomi-TabLDM, a new tabular foundation model designed for classification and regression tasks. This model achieves high prediction accuracy through in-context learning without the need for task-specific fine-tuning. It was pretrained on synthetic data generated from structural causal models, enabling efficient capacity scaling and flexible context utilization. Xiaomi-TabLDM has demonstrated strong performance across various benchmarks, ranking first on OpenML-CTR23 and showing a favorable trade-off between performance and computational cost. AI

IMPACT This model's focus on synthetic data and efficient scaling could accelerate the development and deployment of foundation models for tabular data.

RANK_REASON The cluster describes a technical report detailing a new tabular foundation model, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Xiaomi unveils TabLDM, a tabular foundation model trained on synthetic data

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The cluster describes a technical report detailing a new tabular foundation model, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · TabLDM Team, Penghui Wang, Wei Liu, Hong Wang, Chengyue Huang, Yuxi Sun, Zirui Wang, Hongming Huang, Quan Wang, Chunxiao Liu, Erli Meng, Bin Wang ·

    Xiaomi-TabLDM: A Tabular Foundation Model Technical Report

    arXiv:2609.03880v1 Announce Type: new 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 exclusi…

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