Tabular foundation models like TabPFN and TabICL achieve state-of-the-art performance through in-context learning, yet their architectures remain fundamentally opaque. We introduce KernelICL, a framework to enhance tabular foundation models with quantifiable sample-based interpretability. Building on the insight that in-context learning is akin to kernel regression, we make this mechanism explicit by replacing the final prediction layer with kernel functions (Gaussian, dot-product, kNN) so that every prediction is a transparent weighted average of training labels. We introduce a two-dimensional taxonomy that formally unifies standard kernel methods, modern neighbor-based approaches, and attention mechanisms under a single framework, and quantify inspectability via the perplexity of the weight distribution over training samples. On 55 TALENT benchmark datasets, KernelICL achieves performance on par with existing tabular foundation models, demonstrating that explicit kernel constraints on the final layer enable inspectable predictions without sacrificing performance.
Can Tabular Foundation Models Amortize Statistical Inference?
London School of Economics and Political Science（伦敦政治经济学院）；University of Science and Technology of China（中国科学技术大学）；Shanghai University of Finance and Economics（上海财经大学）；University of Milano-Bicocca（米兰比可卡大学）
Towards Benchmarking Foundation Models for Tabular Data With Text
Department of Computer Science, University of Freiburg, Freiburg, Germany（弗赖堡大学计算机科学系）；Prior Labs, Freiburg, Germany（Prior实验室）；ELLIS Institute, Tubingen, Germany（图宾根ELLIS研究所）
We propose a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet. TabNet uses sequential attention to choose which features to reason from at each decision step, enabling interpretability and more efficient learning as the learning capacity is used for the most salient features. We demonstrate that TabNet outperforms other neural network and decision tree variants on a wide range of non-performance-saturated tabular datasets and yields interpretable feature attributions plus insights into the global model behavior. Finally, for the first time to o…
TabFORGE: A Tabular Foundation Model for Generative Modelling
Department of Computer Science and Technology, University of Cambridge, UK（计算机科学与技术系，剑桥大学，英国）；SAP SE；Télécom Paris, Institut Polytechnique de Paris, France（巴黎telecom，巴黎理工 institute，法国）
Explaining Tabular Foundation Model Differences Through Meta-Features
Clausthal University of Technology, Clausthal-Zellerfeld, Germany（Clausthal技术大学，Clausthal-Zellerfeld，德国）；University of Mannheim, Mannheim, Germany（曼海姆大学，曼海姆，德国）
GeneICL: A Tabular Foundation Model for Bulk Transcriptomics
ETH Zurich（苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Swiss Institute of Bioinformatics（瑞士生物信息学研究所）；Université Paris Cité（巴黎西岱大学）
TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models
Google Research（谷歌研究院）；University of Southern California（南加州大学）；Google DeepMind（谷歌DeepMind）；Harvard University（哈佛大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
Simple and Robust Forecasting of Spatiotemporally Correlated Small Earth Data with A Tabular Foundation Model
School of Engineering and Technology, China University of Geosciences (Beijing)（工程与技术学院，中国地质大学（北京））；School of Geological Engineering and Geomatics, Chang'an University（地质工程与测绘学院，长安大学）
