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BayesianOptimizer::recommend evaluated Expected Improvement at every 0.1 Hz grid candidate, and each predict() rebuilt the kernel matrix and re-ran Cholesky — ~82 factorizations per call — though K and alpha=K^-1 y depend only on the observations, not the query point. Fit the GP once (GpFit: cached L + alpha, lower-triangle-only K build) and reuse it across the grid. Bit-identical arithmetic: the pinned deterministic witness and all 64 tests are unchanged; pure work elimination. Measured (criterion, paired): gamma_bayesian_recommend 105us -> 19us (-81%); gamma_calibration_sweep 466us -> 135us (-71%). https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH