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This paper proposes Second-Order Drifting Models, which augment drifting generative models with artificial velocity variables to achieve accelerated second-order dynamics in Fourier space, mitigating spectral stiffness while preserving one-step inference. The method is evaluated on synthetic distribution matching, sequential data generation, and robotic control, showing improved convergence and competitive performance.