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4 results for “in-context learning”
Bootstrap-Conditioned Action Selection with Tabular Foundation Models
Researchers propose BC-ICL, a method using frozen pre-trained tabular foundation models with bootstrap resampling and in-context learning to improve early-round decision-making performance in contextual bandits under sparse, biased, or cold-start data conditions.
Aug 10, 2026
The Importance of Encoder Choice:A Tabular-Image Study
A new arXiv preprint evaluates modern tabular models as encoders in image-tabular multimodal learning, revealing that top-performing in-context learning tabular models cannot be used naively as encoders due to label dependency during embedding — a previously unaddressed architectural constraint.
Jul 10, 2026
Induction Heads Interpolate N-Grams
A new arXiv preprint formally links induction heads in transformer models to classical statistical smoothing techniques—specifically Jelinek-Mercer and Dirichlet-style smoothing—by analyzing their behavior on order-k Markov chains.
Jul 8, 2026
From Approximation to Emergence: A Theory of Deep Learning
A new arXiv monograph proposes a unified theoretical framework for deep learning, positioning emergence—not just approximation—as the central organizing principle of modern AI theory.
Published Jul 3, 2026 · Analyzed Jul 6, 2026