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In this paper, we present \\underline{\\textbf{G}}radient-\\underline{\\textbf{I}}nformed \\underline{\\textbf{L}}ogit \\underline{\\textbf{C}}orrection (\\textbf{GILC}), a plug-and-play framework that efficiently estimates guidance signals by repurposing the pretrained denoising network as a variational proxy. To circumvent the gradient instability inherent in high-dimensional discrete spaces, we introduce a Jacobian-free mechanism that directly corrects the clean prediction logits, facilitating stable and effective guidance. Our method accommodates both differentiable and non-differentiable reward functions. 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