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Diffusion models can now adapt to entirely new data distributions at inference time without retraining, by learning an in-context score operator from a few examples.
Transformers can be explicitly designed to perform nonlinear regression in-context by leveraging attention as a featurizer, offering a theoretical understanding of how these models learn complex relationships from prompts.