不重训练也不改结构，用频谱削弱提升视觉自回归模型生成质量

Classifier-free guidance (CFG) has become a widely adopted and practical approach for enhancing generation quality and improving condition alignment. Recent studies have explored guidance mechanisms for unconditional generation, yet these approaches remain fundamentally tied to assumptions specific to diffusion models. In this work, we propose a spectrum-weakening framework for visual autoregressive (AR) models. This method works without the need for re-training, specific conditions, or any architectural modifications. It achieves this by constructing a controllable weak model in the spectral domain. We theoretically show that invertible spectral transformations preserve information, while selectively retaining only a subset of spectrum introduces controlled information reduction. Based on this insight, we perform spectrum selection along the channel dimension of internal representations, which avoids the structural constraints imposed by diffusion models. We further introduce two spectrum renormalization strategies that ensures numerical stability during the weakening process. Extensive experiments were conducted on both discrete and continuous AR models, with text or class conditioning. The results demonstrate that our method enables high-quality unconditional generation while maintaining strong prompt alignment for conditional generation.