Kernel Affine Hull Machines for Compute-Efficient Query-Side Semantic Encoding

ArXi:2605.02950v1 Announce Type: new Transformer-based semantic retrieval is highly effective, yet in many deployments the dominant cost lies in online query encoding rather than corpus indexing. We study the fixed-teacher query-adaptation problem and ask whether repeated neural inference can be replaced by a lightweight, analytically explicit estimator without degrading decision-relevant retrieval quality.