Embedding Foundation Model Predictions in Discrete-Choice Models with Structural Guarantees
University of California, Berkeley（加州大学伯克利分校）；Duke University（杜克大学）；Northeastern University（东北大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Southern Methodist University（南卫理公会大学）
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. We propose a two-stage adapter that takes a foundation model's predicted choice probabilities as a precomputed feature and embeds them inside a multinomial logit's utility. In Stage 1, we fit the multinomial logit's structural coefficients by maximum likelihood with sign constraints; in Stage 2, we freeze those coefficients and fit a small neural correction operating on the foundation model's predictions. We prove that this composition exactly preserves the multinomial logit's marginal rate of substitution, so analytically computable value-of-time becomes a mathematical guarantee rather than an empirical accident. Across three datasets and two foundation models, the adapter gains 6.4 percentage points (pp) of test accuracy on average over the multinomial logit and up to 12.8 pp, maintains 100% cost monotonicity, and produces values of time within the published transportation-economics range on the transportation datasets. Performance degrades gracefully under foundation-model context restriction, retaining at least 6 pp of accuracy gain even at 10% of the original foundation-model context.
Understanding the Implicit Biases of Design Choices for Time Series Foundation Models
Center for Applied Mathematics（应用数学中心）；Cornell University（康奈尔大学）；Amazon Web Services（亚马逊网络服务）；Amazon Selling Partner Services（亚马逊销售合作伙伴服务）；Amazon Supply Chain Optimization Technologies（亚马逊供应链优化技术）
Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation Choices
Division AI4Health, Carl von Ossietzky Universität Oldenburg（奥尔登堡卡尔·冯·奥西茨基大学AI4Health部门）；University Institute for Diagnostic and Interventional Radiology, Klinikum Oldenburg AöR（奥尔登堡医院大学诊断与介入放射学研究所）
Social Choice Foundations for Simulation-Augmented Generation
University of Oxford（牛津大学）；PrincInt（PrincInt（注：无通用标准译名，保留原名））；FAIR at Meta（Meta人工智能研究院）；Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）；Northwestern University（西北大学）；Harvard University（哈佛大学）
Center for Data Science, New York University（纽约大学数据科学中心）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Department of Population Health, NYU Grossman School of Medicine（纽约大学格罗斯曼医学院人口健康系）
