Tabular ICL

ICL: In-Context Learning

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9 papers in the last four weeks, up 125% on the four weeks before. 0.1% of all new papers.

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Toward Privileged Foundation Models:LUPI for Accelerated and Improved Learning
May 8, 2026Xueying Ding, Leman AkogluTabular Foundation Models Learning Using Privileged Information
Carnegie Mellon University
Is One Layer Enough? Understanding Inference Dynamics in Tabular Foundation Models
May 7, 2026Amir Rezaei Balef, Mykhailo Koshil, Katharina EggenspergerTabular Foundation Models Transformer Interpretability
TU Dortmund University, Dortmund, Germany · Lamarr Institute for Machine Learning and Artificial Intelligence, Dortmund, Germany · University of Tübingen, Tübingen, Germany
In-Context Positive-Unlabeled Learning
May 7, 2026Siyan Liu, Yi Chang, Manli Cheng +2Binary Classification Weakly Supervised Learning
Department of Statistics and Actuarial Science, University of Waterloo
Breaking the Quality-Privacy Tradeoff in Tabular Data Generation via In-Context Learning
May 6, 2026Xinyan Han, Yan Lu, Xiaoyu Lin +5Synthetic Data Augmentation Privacy-Preserving ML
1Tsinghua University · 2Southeast University · 3Siemens China
Mitigating Label Shift in Tabular In-Context Learning via Test-Time Posterior Adjustment
May 6, 2026Seunghan LeeDistribution Shift Robustness Tabular Foundation Models
LG AI Research, Seoul, South Korea.
Tabular foundation models for in-context prediction of molecular properties
Apr 17, 2026Karim K. Ben Hicham, Jan G. Rittig, Martin Grohe +1Tabular Foundation Models Molecular Property Prediction
RWTH Aachen University, Process Systems Engineering (AVT.SVT), Aachen, Germany. · Lehrstuhl Informatik 7, RWTH Aachen University, Aachen, Germany. · JARA-CSD, Aachen, Germany. +1
TabSieve: Explicit In-Table Evidence Selection for Tabular Prediction
Feb 12, 2026Yongyao Wang, Ziqi Miao, Lu Yang +4In-Context Example Selection Reinforcement Learning
Gaoling School of Artificial Intelligence, Renmin University of China · Shanghai AI Laboratory
TabICLv2: A better, faster, scalable, and open tabular foundation model
Feb 11, 2026Jingang Qu, David Holzmüller, Gaël Varoquaux +1Synthetic Data Pretraining Tabular Foundation Models
SODA Team, INRIA Saclay, Palaiseau, France · Probabl, France

May 8, 2026, cs.LGJ/K move · Enter open · S save

Toward Privileged Foundation Models:LUPI for Accelerated and Improved Learning

Xueying Ding, Leman Akoglu

Carnegie Mellon University

Tabular Foundation Models Learning Using Privileged Information Tabular ICL LLM Training Data-Efficient Learning

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Training foundation models is computationally intensive and often slow to converge. We introduce PIQL,Privileged Information for Quick and Quality Learning, the first framework to systematically integrate privileged information (PI) to simultaneously accelerate learning and improve generalization in tabular foundation models (TFMs). We construct two complementary forms of PI: (i) aggregate dataset-level statistics that reduce the burden on in-context learning, and (ii) encodings of the underlying data-generating program, providing knowledge beyond observable data. We further design an architecture that effectively transfers the train-time-only PI by learning to reconstruct it from observed context at inference. We provide a theoretical analysis characterizing conditions under which PI reduces the population-level approximation gap and accelerates convergence in finite-data regimes. Empirical evidence shows that PIQL enables TFMs to achieve faster convergence, lower final loss, and better generalization, in effect, reducing data and compute requirements. Our work establishes PI-guided pretraining as a principled and practical paradigm for improving the efficiency and performance of foundation models.

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