Negation is a fundamental linguistic operator, yet it remains inadequately modeled in diffusion-based generative systems. In this work, we present a formal treatment of linguistic negation in diffusion-based generative models by modeling it as a structured feasibility constraint on semantic guidance within diffusion dynamics. Rather than introducing heuristics or retraining model parameters, we reinterpret classifier-free guidance as defining a semantic update direction and enforce negation by projecting the update onto a convex constraint set derived from linguistic structure. This novel formulation provides a unified framework for handling diverse negation phenomena, including object absence, graded non-inversion semantics, multi-negation composition, and scope-sensitive disambiguation. Our approach is training-free, compatible with pretrained diffusion backbones, and naturally extends from image generation to temporally evolving video trajectories. In addition, we introduce a structured negation-centric benchmark suite that isolates distinct linguistic failure modes in generative systems, to further research in this area. Experiments demonstrate that our method achieves robust negation compliance while preserving visual fidelity and structural coherence, establishing the first unified formulation of linguistic negation in diffusion-based generative models beyond representation-level evaluation.

翻译：否定是一种基本的语言算子，然而在基于扩散的生成系统中仍未得到充分建模。在本工作中，我们通过将语言否定建模为扩散动力学中语义引导的结构化可行性约束，提出了基于扩散的生成模型中语言否定的形式化处理方法。我们并非引入启发式方法或重新训练模型参数，而是将无分类器引导重新解释为定义语义更新方向，并通过将更新投影到从语言结构导出的凸约束集上来强制执行否定。这一新颖的表述为处理多样化的否定现象提供了一个统一框架，包括对象缺失、分级非反转语义、多重否定组合以及范围敏感的消歧。我们的方法无需训练，与预训练的扩散主干网络兼容，并能自然地从图像生成扩展到时间演化的视频轨迹。此外，我们引入了一个结构化的以否定为中心的基准测试套件，用于隔离生成系统中不同的语言失效模式，以推动该领域的进一步研究。实验表明，我们的方法在保持视觉保真度和结构连贯性的同时，实现了稳健的否定遵从性，从而建立了超越表示层面评估的、基于扩散的生成模型中语言否定的首个统一表述。