# TabPFN-3.5: Technical Report

arXiv: https://arxiv.org/abs/2609.17895 · HTML: https://mlsanity.com/paper/2609.17895/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2026.

## Built on by (most cited first, 3 of 3)

- [TICDA: Tabular In-Context Data Attribution](https://mlsanity.com/paper/2610.07996/) (2026)
- [Estimating Uncoded Crash Factors with Tabular Foundation and System One Models: Kumo Tabular and Jev](https://mlsanity.com/paper/2610.10321/) (2026)
- [Test-Time Compute for Tabular Foundation Models: Mechanisms, Gains, and Limits](https://mlsanity.com/paper/2610.12005/) (2026)

## 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.