这篇论文解决了VAR模型的安全对齐难题，发现只需在生成的第一阶段做干预就能擦除指定概念，比扩散模型方法更高效。代码已开源，可快速应用。
该论文针对视觉自回归(VAR)模型提出语义奇异性公理，认为目标语义概念在Scale-0阶段被锁定。通过增量语义显著性分析(ISSA)验证该公理，并首次提出尺度感知的概念擦除框架(SACE)。SACE仅在第一尺度进行干预，结合熵正则化擦除目标和恢复性损失，防止高熵采样退化并保持良性先验完整性。实验表明该方法在多个领域实现精准概念擦除，训练开销极小。
SACE: Concept Erasure at the Semantic Singularity in Visual Autoregressive Models
The rapid progress of visual autoregressive (VAR) models has unlocked a transformative frontier for high-fidelity text-to-image synthesis, while heightening concerns over the safety alignment of generated content. Naive application of existing erasure techniques to VAR models causes catastrophic semantic collapse and visual artifacts, since they are predominantly designed for the homogeneous denoising steps of diffusion models. To address this foundational challenge, we first propose the Semantic Singularity Axiom, which posits that any target semantic concept embedded within a prompt is definitively locked at Scale-0. Then rigorously validate this axiom through our proposed Incremental Semantic Saliency Analysis (ISSA),which also enable the community to transparently inspect the coarse-to-fine semantic injection process. Guided by this insight, we introduce the first scale-aware concept erasure framework (SACE) for VAR models. By strictly confining interventions to the first scale, our approach couples an Entropy-Regularized Erasure Objective to prevent high-entropy sampling degeneration, alongside a restorative preservation loss to safely anchor the integrity of entangled benign priors. Extensive experiments demonstrate that our method achieves surgical concept erasure performance across various domains with minimal training overhead, timely and elegently resolute the critical safety vulnerabilities inherent in emerging VAR architectures. Code is available at: https://github.com/limerenceysy/SACE}{https://github.com/limerenceysy/SACE.