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Embedding Foundation Model Predictions in Discrete-Choice Models with Structural Guarantees

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arXiv:2606. 26432v1 Announce Type: new Abstract: Tabular foundation models achieve strong accuracy on choice prediction tasks, but their predictions often violate the economic logic those tasks require: raising a price can increase predicted demand, implied willingness-to-pay estimates are frequently negative or implausible, and unavailable alternatives receive nonzero probability.

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Authors: Yingshuo Wang, Xian Sun, Yanhang Li, Zhichao Fan, Zexin Zhuang
Released: 2026-06-26 (13d ago)
Topic: Related to current project
Relation to my work: Read next because Embedding Foundation Model Predictions in Discrete-Choice Models with Structural Guarantees 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: strong, text, rect, under, correct, rate, full, stage. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.26432

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