mlsanityData to 7 Oct 2026

TabPFN-3.5: Technical Report

arXiv 2609.17895 ↗First posted 15 Sep 2026cs.LG stat.MLcited by 2

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Abstract (arXiv)

We introduce TabPFN-3.5, our new flagship Tabular Foundation Model. It significantly outperforms its predecessor, TabPFN-3, and all existing baselines across a broad range of tabular problems. TabPFN-3.5 sets a new state of the art on standard tabular prediction in TabArena, and extends it to the data practitioners encounter in practice: non-i.i.d. data with temporal or grouped splits, tables with strings, text and images, high-cardinality categorical features, and wide tables with many features. These gains carry over to our task-specific harnesses: state of the art on relational data and stronger time-series forecasting. For faster inference, our variant TabPFN-3.5-Fast runs up to 3x faster than TabPFN-3 while keeping most of the accuracy gains. In addition, we upgrade TabPFN-3.5-Plus, expanding our multimodal capabilities with advanced text and date handling alongside proprietary inference optimizations. Finally, we release a new version of our Thinking mode, TabPFN-3.5-Thinking, which scales inference-time computation to push the state of the art further. It benefits from our stronger base model and from inference-time improvements that make it up to 12x faster than TabPFN-3-Thinking.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Sep 2026.

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  1. 2026TabPFN-3: Technical Report
  2. 2022TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
  3. 2026TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention
  4. 2026Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure
  5. 2024TabDPT: Scaling Tabular Foundation Models on Real Data
  6. 2026Mind the Gap? A Distributional Comparison of Real and Synthetic Priors for Tabular Foundation Models
  7. 2026TabSieve: Explicit In-Table Evidence Selection for Tabular Prediction
  8. 2026Xiaomi-TabLDM: A Tabular Foundation Model Technical Report