想知道表格模型换了数据还灵不灵？这篇论文测了9个主流模型，结果全都不乐观，性能差距最小也有0.003。
论文评估了9种表格基础模型（TabPFNv2、TabICL、Mitra等）在分布偏移下的表现。使用了HELOC、Voting和Childhood Lead三个真实数据集，涵盖标签、社会经济和地理偏移类型。结果显示所有模型性能均系统性下降，偏移差距在0.003到0.060之间，且高性能模型需要大幅计算资源。
Empirical Evaluation of Out-Of-Distribution Performance of Tabular Foundation Models
Tabular Foundation Models (TFMs) have emerged as novel approaches for tabular predictive tasks, demonstrating competitive predictive performance to ensemble tree-based models. Most TFMs are trained and evaluated on independent and identically distributed data, but this assumption changes in real-world scenarios due to distribution shifts, which compromise the robustness of models. Limited research has been conducted of TFMs under distribution shifts. We present an empirical evaluation of Out-Of-Distribution (OOD) performance of nine TFMs, spanning diverse pre-training strategies and architectures: TabPFNv2, TabPFNv2.5, TabPFNv2.6, TabPFNv3, TabICL, TabICLv2, Mitra, LimiX and TabFM. Three real-world datasets from the TableShift study were considered (HELOC, Voting, Childhood Lead), covering label, socioeconomic, and geographic shift types. Our results show that all evaluated TFMs degrade systematically under distribution shift regardless of pre-training strategy, with shift gaps ranging from 0.003 to 0.060 depending on shift type. The relationship between in-distribution and OOD predictive performance documented for classical tabular models extends into TFMs. We also identified a scalability gap, as high-performing models demand significant memory and computational resources beyond what standard deployment infrastructure can support. This study extends existing benchmarks for OOD in tabular data, providing evidence to support their adoption in high-stakes domains characterized by structural distribution shifts.