Scheduled Runtime Refinement for Neural Networks: Periodic Pruning, Replay, and Distillation as an Operating Regime

Contemporary neural networks are trained and deployed as largely static parameter accumulators: once weights are learned, they persist indefinitely, regardless of their ongoing contribution to performance. While scaling has delivered substantial gains, this accumulation introduces a growing systems-level tax in the form of increased inference latency, memory pressure, interference in continual learning, and diminishing returns on additional parameters. This paper proposes scheduled runtime refinement as an operating regime for neural networks. Rather than treating pruning, replay, and distillation as isolated or one-off optimization steps, we integrate them into a recurring refinement cycle executed during low-load or idle periods. Each cycle estimates parameter importance, downscales or prunes low-contribution mass, reinforces high-value pathways via targeted replay, and optionally distills or caches pruned subnetworks for on-demand recall. We argue that periodic runtime refinement increases performance density—the amount of effective capability per unit of compute, memory, and energy—without requiring additional hardware or retraining from scratch. Drawing on prior work in sparsity, continual learning, and knowledge distillation, we outline the mechanics, scheduling strategies, expected performance effects, and limitations of this approach. The proposed regime is particularly relevant for edge and resource-constrained deployments, but also complements large-scale training by mitigating the systems costs of static accumulation.