The alluring promise of reconfigurable intelligent surfaces (RIS) to reshape wireless environments into empowered communication participants faces obstacles in navigating intricate configuration spaces to optimize signal transformations [1]. Efficiently deducing appropriate reflecting coefficient settings through beam training or unwieldy channel models remains computationally taxing. Prior works apply deep learning to estimate favorable configurations from sampled channel conditions but overlook latent commonalities between sequential channel observations. This exploration eschews their independence assumptions, unlocking a potent new optimization paradigm from within temporal channel response consistencies. Our approach harnesses interrelationships connecting a sequence of sampled channel configurations to current channel states through a multilayer deep neural network. By encoding past channel observations alongside present conditions into the network input, continuity-aware output configurations emerge to bolster performance predictability without exacerbating training overheads. Assessments demonstrate pronounced signal improvement over techniques resting upon solitary state optimization in environments with dynamic obstructions. Results affirm the technique’s ingenuity in extracting useful correlations from channel history amidst seemingly erratic fluctuations deducing robust RIS configuration guidance by reflecting upon echoes of channels past.