Faster Retrieval for Better, More Efficient RAG

Apple researchers present amortized maximum inner product search (MIPS), training small neural networks offline on a fixed key database to learn the support function of the key set, which is convex and whose gradient returns the best-matching key. SupportNet learns per-cluster support functions to route queries more accurately than centroid-based routing, while KeyNet rewrites queries before a standard IVF index; on Natural Questions, SupportNet reached 95% routing recall about 1.9× faster than centroid routing, and KeyNet cut the time to 99% recall by up to ~8×. The work was presented at ICML 2026.