# Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choice

arXiv: https://arxiv.org/abs/2605.26559 · HTML: https://mlsanity.com/paper/2605.26559/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 26 May 2026.

## Builds on (most cited first, 4 of 4)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models](https://mlsanity.com/paper/2510.21204/) (2025)
- [Choice modelling in the age of machine learning -- discussion paper](https://mlsanity.com/paper/2101.11948/) (2021)
- [A Neural-embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability](https://mlsanity.com/paper/2002.00922/) (2020)

## Built on by (most cited first, 6 of 6)

- [Embedding Foundation Model Predictions in Discrete-Choice Models with Structural Guarantees](https://mlsanity.com/paper/2606.26432/) (2026)
- [Auditing the Audit: Five Failure Modes in Benchmark-Validity Audits](https://mlsanity.com/paper/2607.02586/) (2026)
- [Probe Choice Changes Canary-Memorization Verdicts: Three Post-Hoc Disagreement Case Studies in a Text-Dominant LoRA-Tuned Autoregressive Testbed](https://mlsanity.com/paper/2606.31168/) (2026)
- [From Non-Convex Self-Concordant Regularization to Scalable Quasi-Newton Training of PINNs](https://mlsanity.com/paper/2608.04206/) (2026)
- [Mind the Hook: Source-Level Auditing of Privacy Defenses in Retrieval-Augmented Generation](https://mlsanity.com/paper/2608.09001/) (2026)
- [Temporal Heterogeneous Graph Transformer for Credit Card Fraud Detection](https://mlsanity.com/paper/2609.07100/) (2026)

## Abstract (arXiv)

Tabular foundation models achieve strong accuracy on choice prediction tasks, but their predictions often violate the economic logic those tasks require: raising a price sometimes increases predicted demand, and implied willingness-to-pay estimates are frequently negative or implausible. We propose a two-stage adapter that embeds foundation model predictions within a utility-maximization framework. In the first stage, we estimate a standard choice model whose parameters are constrained to obey economic theory. In the second stage, we freeze those parameters and train a correction term that incorporates the foundation model's predictions as additional information. The result is a model that inherits the foundation model's accuracy gains while guaranteeing monotonic price-demand relationships under policy perturbation and producing analytically computable trade-off measures. On two transportation datasets, the adapter recovers up to 13 percentage points of accuracy over a standard logit model while maintaining perfect economic consistency, something neither the raw foundation models nor conventional distillation achieve.