Apple's Learned Support Functions Speed Up RAG Retrieval

Apple researchers presented "Amortizing Maximum Inner Product Search with Learned Support Functions" at ICML 2026, training small neural networks offline to route queries toward their best-matching keys faster. SupportNet learns the support function via input convex neural networks (ICNNs) to improve cluster routing, while KeyNet directly outputs the best-matching key vector, kept geometrically grounded by a consistency loss from Euler's theorem. Both are trained on the actual query distribution, trading an upfront amortization cost for faster, more accurate retrieval at inference time.