Generalization to larger pretrained prefetching architectures

Determine whether the finding that confidence gating, rather than the prediction model, produces the observed safety and performance behavior generalizes from the 257-parameter online delta-regressor to larger, offline-pretrained architectures such as Pythia, TransFetch, and DART.

Background

The matched-control experiments evaluate a small 257-parameter multilayer perceptron trained online from scratch on each memory trace. The paper explicitly cautions that this setup may not represent larger learned prefetchers trained offline, including Pythia, TransFetch, and DART.

The unresolved issue is whether the paper’s central matched-control conclusion—that the admission gate matters more than the predictor—continues to hold for substantially larger and differently trained learned prefetching architectures. Resolving it would establish the scope of the reported result beyond the specific online delta-regressor used in the experiments.