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arxiv: 2502.05564 · v2 · pith:YF5SB6PXnew · submitted 2025-02-08 · 💻 cs.LG · cs.AI

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data

Jingang Qu , David Holzm\"uller , Ga\"el Varoquaux , Marine Le Morvan This is my paper

Pith reviewed 2026-05-20 13:28 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords tabular classificationin-context learningfoundation modelslarge-scale tabular dataattention mechanismssynthetic pretrainingTabPFN
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The pith

TabICL scales in-context learning to tabular datasets with 500K rows via a two-stage attention design.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces TabICL to extend in-context learning to much larger tabular classification problems than prior foundation models could manage. Previous approaches like TabPFNv2 become too slow on tables beyond roughly 10K samples because their combined row and column attentions scale poorly. TabICL instead uses a first stage of column-then-row attention to compress each row into a fixed-size vector, then runs a standard transformer on those vectors for in-context prediction. The model is pretrained once on synthetic tables up to 60K rows and can then process real tables with 500K rows on ordinary hardware. Benchmarks across 200 datasets show accuracy comparable to TabPFNv2 but with up to 10x speedups, and clear gains over both TabPFNv2 and CatBoost on the 53 largest datasets.

Core claim

TabICL is a tabular foundation model for classification, pretrained on synthetic datasets with up to 60K samples and capable of handling 500K samples on affordable resources. This is enabled by a novel two-stage architecture: a column-then-row attention mechanism to build fixed-dimensional embeddings of rows, followed by a transformer for efficient ICL. Across 200 classification datasets from the TALENT benchmark, TabICL is on par with TabPFNv2 while being systematically faster (up to 10 times), and significantly outperforms all other approaches. On 53 datasets with over 10K samples, TabICL surpasses both TabPFNv2 and CatBoost, demonstrating the potential of ICL for large data.

What carries the argument

A two-stage architecture that first applies column-then-row attention to produce fixed-dimensional row embeddings, then feeds those embeddings into a transformer for in-context learning.

If this is right

  • In-context learning becomes feasible for tabular classification tasks involving hundreds of thousands of rows without per-dataset retraining.
  • Inference on large tables can be performed up to ten times faster than with prior ICL models while maintaining accuracy.
  • Synthetic pretraining transfers effectively enough to deliver strong results on real data distributions with more than 10K samples.
  • Gradient-boosted trees can be challenged on large tabular problems by a single forward-pass foundation model.
  • The same two-stage compression idea could support scaling ICL to even bigger tables if further efficiency gains are added.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The architecture might be adapted to regression or multi-label tasks by changing only the final prediction head.
  • Similar factored attention patterns could be explored for other high-cardinality structured data where full self-attention is prohibitive.
  • One could measure how much the choice of synthetic data generator affects downstream performance on specific real domains.

Load-bearing premise

The column-then-row attention mechanism produces fixed-dimensional row embeddings that retain enough information for the subsequent transformer-based in-context learning to succeed on real large tables.

What would settle it

A direct accuracy comparison between TabICL and TabPFNv2 on multiple real-world tables each containing more than 100,000 rows; if TabICL falls below TabPFNv2 or strong gradient-boosted baselines, the claim that the embeddings preserve sufficient information would not hold.

read the original abstract

The long-standing dominance of gradient-boosted decision trees on tabular data is currently challenged by tabular foundation models using In-Context Learning (ICL): setting the training data as context for the test data and predicting in a single forward pass without parameter updates. While TabPFNv2 foundation model excels on tables with up to 10K samples, its alternating column- and row-wise attentions make handling large training sets computationally prohibitive. So, can ICL be effectively scaled and deliver a benefit for larger tables? We introduce TabICL, a tabular foundation model for classification, pretrained on synthetic datasets with up to 60K samples and capable of handling 500K samples on affordable resources. This is enabled by a novel two-stage architecture: a column-then-row attention mechanism to build fixed-dimensional embeddings of rows, followed by a transformer for efficient ICL. Across 200 classification datasets from the TALENT benchmark, TabICL is on par with TabPFNv2 while being systematically faster (up to 10 times), and significantly outperforms all other approaches. On 53 datasets with over 10K samples, TabICL surpasses both TabPFNv2 and CatBoost, demonstrating the potential of ICL for large data. Pretraining code, inference code, and pre-trained models are available at https://github.com/soda-inria/tabicl.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces TabICL, a tabular foundation model for classification that uses in-context learning on large tables. It proposes a two-stage architecture in which a column-then-row attention mechanism first produces fixed-dimensional row embeddings, after which a standard transformer performs ICL over those embeddings. The model is pretrained on synthetic datasets containing up to 60K samples and is claimed to handle inference on tables with 500K samples. On the TALENT benchmark the paper reports that TabICL matches TabPFNv2 across 200 classification datasets while being up to 10 times faster and outperforms both TabPFNv2 and CatBoost on the 53 datasets that exceed 10K samples.

Significance. If the performance claims hold under rigorous controls, the work would demonstrate that ICL-based tabular foundation models can be scaled beyond the 10K-sample regime that limits TabPFNv2, thereby offering a practical alternative to gradient-boosted trees on large tabular data. The public release of pretraining code, inference code, and pretrained weights is a clear strength that supports reproducibility.

major comments (2)
  1. [Architecture section] Architecture description (two-stage design): The central scaling claim—that the column-then-row attention produces fixed-dimensional row embeddings that retain sufficient statistical structure for downstream ICL on tables larger than 10K samples—is not isolated by any ablation. No experiment compares the full model against a variant that omits the column-attention stage, varies the embedding dimension, or substitutes a joint column-row attention baseline on the same large TALENT subsets; without such controls it is impossible to attribute the reported gains on the >10K-sample regime to the proposed compression step rather than to other factors.
  2. [Experiments / TALENT results] Experimental results, TALENT benchmark tables: The abstract and results section state that TabICL is “on par” with TabPFNv2 and “surpasses” it on the 53 large datasets, yet no error bars, number of random seeds, or statistical significance tests are reported for any of the 200 datasets. This omission is load-bearing for the claim that the two-stage model delivers a systematic advantage on large data.
minor comments (2)
  1. [§3] The notation for the column-then-row attention blocks is introduced without an explicit equation defining the output dimensionality of the row embeddings; adding a short equation would improve clarity.
  2. [Figure 2] Figure 2 (or equivalent architecture diagram) would benefit from explicit arrows or labels indicating the transition from column-attended features to the fixed-dimensional row embeddings.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which help clarify the strengths and areas for improvement in our work on TabICL. We respond to each major comment below and indicate the revisions we will incorporate.

read point-by-point responses

  1. Referee: [Architecture section] Architecture description (two-stage design): The central scaling claim—that the column-then-row attention produces fixed-dimensional row embeddings that retain sufficient statistical structure for downstream ICL on tables larger than 10K samples—is not isolated by any ablation. No experiment compares the full model against a variant that omits the column-attention stage, varies the embedding dimension, or substitutes a joint column-row attention baseline on the same large TALENT subsets; without such controls it is impossible to attribute the reported gains on the >10K-sample regime to the proposed compression step rather than to other factors.

    Authors: We agree that additional ablations isolating the column-then-row attention on the large TALENT subsets would provide stronger evidence for its specific contribution to scaling. The manuscript emphasizes end-to-end comparisons against TabPFNv2 (which relies on alternating column-row attention but cannot scale beyond ~10K samples) and other baselines, showing clear gains on datasets >10K. We will add targeted ablations in the revision, including a no-column-attention variant evaluated on representative large subsets, to better attribute the benefits of the two-stage compression. revision: yes

  2. Referee: [Experiments / TALENT results] Experimental results, TALENT benchmark tables: The abstract and results section state that TabICL is “on par” with TabPFNv2 and “surpasses” it on the 53 large datasets, yet no error bars, number of random seeds, or statistical significance tests are reported for any of the 200 datasets. This omission is load-bearing for the claim that the two-stage model delivers a systematic advantage on large data.

    Authors: We acknowledge that reporting variability and statistical tests would increase the rigor of the performance claims. The TALENT results follow the benchmark's standard protocol with comparisons to published TabPFNv2 numbers and deterministic CatBoost runs. In the revised manuscript we will include standard deviations over multiple random seeds for the key large-dataset comparisons (the 53 datasets >10K samples) along with appropriate significance tests to substantiate the statements of parity and outperformance. revision: yes

Circularity Check

0 steps flagged

No circularity; claims rest on external empirical benchmarks

full rationale

The paper presents TabICL as a new architecture for scaling in-context learning to large tabular datasets and supports its claims through direct performance comparisons against TabPFNv2, CatBoost, and other baselines on the independent TALENT benchmark (200 classification datasets, with a 53-dataset subset >10K samples). No equations, fitted parameters, or self-citations are used to derive results that reduce to the model's own inputs by construction. The two-stage column-then-row attention is introduced as an engineering choice whose effectiveness is measured externally rather than assumed via internal redefinition or prior self-work.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim depends on the transferability of synthetic pretraining to real tabular data and on the information-preserving properties of the two-stage attention; these are domain assumptions rather than derived results.

free parameters (1)
  • embedding dimension and attention hyperparameters
    Standard neural-network choices that are tuned during pretraining on synthetic data and affect the row-embedding quality.
axioms (1)
  • domain assumption Synthetic tabular datasets capture the statistical structure needed for generalization to real classification tasks
    The model is pretrained exclusively on synthetic data yet evaluated on real TALENT datasets.

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Forward citations

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