Predictive Compression for State–Access Allocation

A prediction head must choose how much local state to retain and how often to read a shared representation. State preserves predictable future answers between reads; new task information can require an earlier read. For periodic reads of fixed-width packets and outputs affine in the held state between reads, an analysis separates these bottlenecks, and controlled synthetic interventions exhibit both. We train one multi-step predictor, compress its future task answers into candidate held states, calibrate their executed risks, and deploy the cheapest passing configuration. Across 12 new synthetic worlds, compilation gives a 20.29× cold-preparation speedup over training a complete receiver grid. A matched follow-up on the same worlds yields a 12.11× paired geometric speedup over cost-ordered early-stop receiver search, at 1.038 times the baseline’s selected normalized state-and-read cost. With 65,536 calibration episodes per task, all 432 allocation requests pass independent risk tests at a target mean squared error of 0.04. A sufficiently trained shared grid can select slightly cheaper budgets on these synthetic tasks, while a simple sample-and-hold baseline is stronger on the tested frozen video features.