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Closing the Context Gap: Activation Alignment for Tabular In-Context Learning

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Briefings

This article presents activation alignment, a method for tabular foundation models to achieve in-context learning using limited training data. By training a lightweight linear transformation on synthetic unlabeled data, the method maps intermediate activations of a data-constrained student toward those of a full-context teacher. Evaluated on 38 datasets from TabArena using TabPFN-3 and TabFM, the aligned student shows statistically significant improvements over the unaligned baseline across all context budgets, recovering nearly half of the teacher's advantage in low-data regimes.

Key points

• Activation alignment uses a lightweight linear transformation trained on synthetic unlabeled data to map intermediate activations of a data-constrained student toward those of a full-context teacher.
• The method achieves statistically significant improvements over the unaligned baseline across 38 classification datasets from the TabArena benchmark using TabPFN-3 and TabFM.

Context

Tabular foundation models process all training examples in every forward pass, making predictions expensive. Restricting context reduces cost but degrades performance. This method offers a practical, low-overhead approach to achieve inference speed of compact contexts while closing a significant performance gap to the full-context teacher.

Limitations

Evaluation is limited to 38 datasets from the TabArena benchmark using the leading two tabular foundation models. Results are based on training on commodity hardware with no GPU requirements. Performance gains are statistically significant but specific to the tested models and context budgets.

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