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

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arXiv:2606. 11473v1 Announce Type: new 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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Authors: Jamie Heredge, Mattia J. Villani, Pranav Deshpande, Akshay Seshadri, Niraj Kumar
Released: 2026-06-12 (27d ago)
Topic: Related to current project
Relation to my work: Read next because CRUMB: Efficient Prior Fitted Network Inference via Distributionally Matched Context Batching overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, class, distributional, eval, rate, stage, test, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.11473

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