Ghost in the Kernel: In-Context Learning with Efficient Transformers via Domain Generalization

arXiv:2607.00479 · cs.LG, stat.ML · Submitted 2026-07-01 · Read on arXiv

Peilin Liu, Ding-Xuan Zhou

cs.LG, stat.ML

Submitted: 2026-07-01

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transformer — 76× in this paper · explained in 2 episodes
• Generalization — 42× in this paper · explained in 13 episodes
• In-Context Learning — 32× in this paper · explained in 4 episodes
• Activation Function — 12× in this paper · explained in 1 episode
• Attention Mechanism — 9× in this paper · explained in 3 episodes
• Distribution Shift — 7× in this paper · explained in 6 episodes
• Hilbert Space — 5× in this paper · explained in 2 episodes
• Error Decomposition — 3× in this paper · explained in 3 episodes
• Decoupling — 2× in this paper · explained in 2 episodes
• Gaussian Error Linear Unit — 2× in this paper
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Hybrid Model — 2× in this paper · explained in 2 episodes
• Hybrid Models — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Reproducing Kernel Hilbert Space (RKHS) — 2× in this paper · explained in 3 episodes
• Zero-Shot Generalization — 2× in this paper · explained in 5 episodes
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Neural Network Approximation — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• Gaussian Error Linear Units (GELUs)
• Optimal Convergence Rates for Neural Operators
• Searching for Activation Functions
• GLU Variants Improve Transformer
• Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods
• Retentive Network: A Successor to Transformer for Large Language Models

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