arXiv AI By Jamie Heredge, Mattia J. Villani, Pranav Deshpande, Akshay Seshadri, Niraj Kumar

CRUMB: Efficient Prior Fitted Network Inference via Distributionally Matched Context Batching

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arXiv:2606. 11473v1 Announce Type: cross Abstract: Prior-fitted networks (PFNs) are a promising class of tabular foundation models that perform in-context learning, whereby the entire labelled training set is supplied as context, and predictions for test queries are produced in a single forward pass.

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arXiv Machine Learning
Aug 31

SOMTab: Set-Order Mamba for Efficient Tabular In-Context Learning

SOMTab is a Set-Order Mamba architecture designed for efficient tabular in-context learning. It separates representation construction from query-conditioned retrieval, using Mamba-based state‑space mixing to build compact row and column representations while retaining attention for final prediction. The model, along with a synthetic prior called DCH‑TailMix, achieves performance comparable to Transformer‑based tabular foundation models but with faster inference and lower GPU memory usage.

By Hao Wang, Siyu Zhang, Wei Ma
arXiv Machine Learning
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What Do Tabular Foundation Models Compute In Context? In-Situ Representation Refinement through Attention-Gated Updates

The paper introduces RefineICL, an attention‑gated, feed‑forward‑network‑free framework that refines representations in situ for tabular foundation models. By using support labels to guide episode‑specific updates, the method transfers learned corrections to unlabeled queries without altering model parameters, achieving state‑of‑the‑art performance on AMLB29 and TabArena benchmarks. Experiments and internal interventions demonstrate that intermediate support updates are essential for constructing task‑specific predictors in context.

By Tian Zhou, Beverly Jin, Linxiao Yang, Xue Wang, Wenwei Wang, Bingqing Peng, Mengni Ye, Jinjie Gu, Liang Sun