Scaling Graph Topology Augmentation with Structured Variations and Spectral Trust in Non-stationary Reinforcement Learning

Graph Topology Augmentation (GTA) improves prioritized planning under reward change. Yet the current compute model hinder scalability, with critical bottlenecks in CPU residence, exact matrix operator representation and obnoxious eigensolve. Therefore, we introduce GTA-plus-PS, which stages changed transition rows as rank-$r$ shocks and publishes consistent belief state through coherent commits. Moreover, a trace-derived spectral trust certificate replaces periodic second-largest-eigenvalue-modulus estimation. We derive exact Woodbury updates for resolvent actions, connect the trace to discounted signal variance, and prove convergence under deterministic or variance-scaled stochastic selection. Concurrently, we map all parallelizable operations onto GPU-resident computation model, coupling with an efficient implementation that enables streamlined inference-learning. We evaluate the new design on an extensive suite of varying environments, representation, host model, and learn update. Our augmented methods gain performance over their standard baseline, in both tabular and visual-control setting.