Optical surface deviations in ophthalmic optics arise from fast tool servo errors, the tool footprint, freeform surface residuals, and the freeform surface curvature responses across processing stages. In this paper, a PAM-Net physics–AI dual-driven compensation control method is presented for addressing the issues in dynamically accurate positioning of a diamond cutting tool via the fast tool servo using existing methods, e.g., struggling to characterize micrometer-scale CNC following-up errors, spatial surface-form perturbations, and S/C optical quality simultaneously. PAM-Net maps Z-axis position, velocity, acceleration, jerk, and A/B-axis phases to surface-form residuals and S/C deviations through tool-lens projection and curvature-mediated optical-response operators, while jointly estimating uncertainty and safety risk for constrained NC compensation. The framework also preserves an interpretable mediation chain from servo dynamics to final optical quality. On holdout-35, removing acceleration/jerk increased RMSE from 0.512 to 5.395 μm, indicating strong predictive dependence on high-order servo dynamics. Closed-loop validation increased the strict ±0.12 D pass rate from 72.5% to 87.5%, alongside reduced surface-form and curvature residuals. These results indicate that learning-based compensation control for high-precision freeform-optics manufacturing requires joint consideration of prediction accuracy, physical interpretability, executable NC write-back, and manufacturing constraints.