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Introduces Perron–Frobenius Operator Matching (PFOM), a generative framework that unifies flow, diffusion, and jump models via integral PF operator matching, proving KL divergence yields a practical loss equivalent to Koopman path matching, and develops Nesterov-accelerated training and sampling for improved efficiency.