Uniform Ensembles under Dependence: Calibration and Protected Replacement

Temporal dependence does not by itself justify replacing uniform bagging. We separate the information available for learning, the intervention that changes an ensemble, and the evidence needed to approve that change. Uniform-bootstrap voting attains the minimax rate on a covariate-refresh family, while balanced spectral routing is exactly midpoint blocking on weighted paths, decreasing temporal kernels, and sufficiently large band graphs. For frozen replacements, independent-label certification has matching fixed-confidence cost $\Theta (\omega /{\Delta }^2)$ in disagreement $\omega$ and improvement $\Delta$. A multiscale martingale certificate instead protects average conditional improvement along a dependent audit history. Across four chronological tasks, full residual stacking has the lowest mean later error on three, while graph projection improves on full stacking on none. Rolling-origin production evaluation records 23 approved replacements, including 2 forward-error reversals. A gradual within-audit drift experiment explains how a positive historical average can coexist with a harmful current predictor. Restarting at a predeclared recent window removes that outdated evidence but loses useful power under stable conditions. The resulting guarantee concerns the chosen audit window; future transfer still requires separate evidence or restrictions on drift.