Apple proposes faster RAG retrieval via learned functions

The method trains small neural networks offline to model query-key matching patterns, enabling faster and more accurate retrieval at inference time. It leverages convex analysis principles and input-convex neural networks to capture the geometric structure of matching. Separate networks are trained for support function estimation and optimal key prediction, improving performance on real-world R-AG queries with short user inputs.

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