Reachable Adaptation under Delayed Feedback: Budgeted Partial Updates in Nonstationary Streams

A deployed predictor may receive labels only after delay while each maintenance cycle can update only a few model blocks. We ask which part of the current error is reachable from the model already in service. We formulate current risk over an anchored reachable family, separating mismatch from delayed or replayed evidence, localization from frozen directions, adaptive route selection, and restricted estimation. Under stated conditional-law and second-moment conditions, projection geometry characterizes localization; a coherence analysis compares an observable risk-change route with a joint-gain population oracle; and replay-aware bounds expose the roles of staleness, effective sample size, and selected dimension. Drift-Decomposed Adaptation (DDA) instantiates this view by differencing anchored block gradients across two delayed windows and refitting only selected blocks. Controlled studies probe these mechanisms and their boundary regimes. On measured Appliances data, DDA has RMSE 0.535 versus 0.537 for Full with 66.7% fewer block updates. On a measured Fjord5G segment, it reduces RMSE by 10.1% relative to GradMag at the same one-block budget. These offline chronological evaluations illustrate regime-dependent resource–error tradeoffs rather than universal selector dominance.