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This paper studies inverse sampling for Lévy-driven generative models, proposing a structured reverse sampler that decomposes dynamics into diffusion, small jump, and large jump components, with neural networks amortizing jump rates. The method is applied to OFDM-SISO channel estimation under mixed Gaussian and impulsive noise.