# The Standardization Trap: Certifying Joint Label Processing in Tabular Foundation Models

arXiv: https://arxiv.org/abs/2610.08314 · HTML: https://mlsanity.com/paper/2610.08314/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 6 Oct 2026.

## Builds on (most cited first, 10 of 10)

- [TabArena: A Living Benchmark for Machine Learning on Tabular Data](https://mlsanity.com/paper/2506.16791/) (2025)
- [TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models](https://mlsanity.com/paper/2511.08667/) (2025)
- [Beyond IID: How General Are Tabular Foundation Models, Really?](https://mlsanity.com/paper/2606.30410/) (2026)
- [TabPFN-3: Technical Report](https://mlsanity.com/paper/2605.13986/) (2026)
- [Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms](https://mlsanity.com/paper/2604.04868/) (2026)
- [TabDPT-Turbo: Efficient In-Context Learning for Tabular Prediction](https://mlsanity.com/paper/2608.01400/) (2026)
- [Interpretable Tabular Foundation Models via In-Context Kernel Regression](https://mlsanity.com/paper/2602.02162/) (2026)
- [Where Computation Lives Inside TabPFN: Causal Localisation of Attention Head Function](https://mlsanity.com/paper/2606.12917/) (2026)
- [A Mechanistic Study of Tabular Foundation Models](https://mlsanity.com/paper/2605.21288/) (2026)
- [TabPFN Through The Looking Glass: An interpretability study of TabPFN and its internal representations](https://mlsanity.com/paper/2601.08181/) (2026)

## Abstract (arXiv)

Linear regression and kernel smoothing offer tractable explanations of in-context learning: in both, the features determine the weight assigned to each context label. However, whether this fixed-weight account describes pretrained tabular foundation models (TFMs) remains unclear. Testing this account using derivatives runs into a standardization trap: public TFM packages standardize the labels before the model sees them, yet ordinary derivatives also reflect behavior outside the set of standardized labels, making a model appear nonlinear even when every prediction it makes agrees with a fixed-weight map. We propose two certificates that depend only on predictions at standardized labels and can reject two distinct explanations: fixed-weight prediction and sums of independent nonlinear label transformations. Across the five public TFMs that we evaluate, our certificates show that changing one context label alters how other labels influence the prediction, a behavior we call joint processing. We further find that joint processing emerges with training and that attention scores carry most of the measured interaction. Together, these findings motivate TFM explanations that account for how context labels change the influence of individual examples.