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Apple Publishes Faster RAG Retrieval Paper
11 min read
Researchers from Apple present a paper at ICML 2026 detailing Faster Retrieval for Better, More Efficient RAG. The team trains small neural networks to route queries toward best matches, trading upfront amortization costs for faster inference. They test models on Natural Questions using 2.6M passages split into 128 clusters, achieving improved recall over standard centroid routing methods.
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