# Chunked TabPFN: Exact Training-Free In-Context Learning for Long-Context Tabular Data

arXiv: https://arxiv.org/abs/2509.00326 · HTML: https://mlsanity.com/paper/2509.00326/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 15 references that are in the map.
- First posted on arXiv 30 Aug 2025.

## Builds on (most cited first, 12 of 12)

- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second](https://mlsanity.com/paper/2207.01848/) (2022)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [Ring Attention with Blockwise Transformers for Near-Infinite Context](https://mlsanity.com/paper/2310.01889/) (2023)
- [Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data](https://mlsanity.com/paper/1909.06312/) (2019)
- [TabArena: A Living Benchmark for Machine Learning on Tabular Data](https://mlsanity.com/paper/2506.16791/) (2025)
- [TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling](https://mlsanity.com/paper/2410.24210/) (2024)
- [TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023](https://mlsanity.com/paper/2307.14338/) (2023)
- [Retrieval & Fine-Tuning for In-Context Tabular Models](https://mlsanity.com/paper/2406.05207/) (2024)
- [TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks](https://mlsanity.com/paper/2402.11137/) (2024)
- [Mixture of In-Context Prompters for Tabular PFNs](https://mlsanity.com/paper/2405.16156/) (2024)

## Built on by (most cited first, 8 of 8)

- [From Tables to Signals: Revealing Spectral Adaptivity in TabPFN](https://mlsanity.com/paper/2511.18278/) (2025)
- [CRUMB: Efficient Prior Fitted Network Inference via Distributionally Matched Context Batching](https://mlsanity.com/paper/2606.11473/) (2026)
- [When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach](https://mlsanity.com/paper/2605.19662/) (2026)
- [GEAR: Generative Expansion and Real Anchoring for Two-Stage Distillation of Tabular Foundation Models](https://mlsanity.com/paper/2608.18849/) (2026)
- [Tabular foundation models for non-tabular tasks](https://mlsanity.com/paper/2608.22594/) (2026)
- [LoGIC: Budgeted Context Construction for Node-Level Graph In-Context Learning with Tabular Foundation Models](https://mlsanity.com/paper/2609.05955/) (2026)
- [Compile the Table: Query-Calibrated Operator Compression for Tabular In-Context Learning](https://mlsanity.com/paper/2610.11784/) (2026)
- [Test-Time Compute for Tabular Foundation Models: Mechanisms, Gains, and Limits](https://mlsanity.com/paper/2610.12005/) (2026)

## Abstract (arXiv)

TabPFN v2 achieves better results than tree-based models on several tabular benchmarks, which is notable since tree-based models are usually the strongest choice for tabular data. However, it cannot handle more than 10K context tokens because transformers have quadratic computation and memory costs. Unlike existing approaches that rely on context compression, such as selecting representative samples via K-nearest neighbors (KNN), we introduce a tiled-block strategy to compute attention within the TabPFN framework. This design is compatible with standard GPU setups and, to the best of our knowledge, is the first to enable TabPFN to process long contexts without any pre-processing. We demonstrate the effectiveness of our approach on the standard TabArena benchmark, with code available at https://github.com/mrsergazinov/chunk_tabpfn.