# NEGATE: Constrained Semantic Guidance for Linguistic Negation in Text-to-Video Diffusion Source: [NEGATE: Constrained Semantic Guidance for Linguistic Negation in Text-to-Video Diffusion]() Collection index: [llms.txt]() Data updated: 2026-03-10T05:00:12.119Z Authors: Taewon Kang, Ming C. Lin Published: 2026-03-06T18:21:49.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Text-to-Video Generation on Human Evaluation 50 participants, 400 ratings (test)](): Text-to-Video Generation; Human Evaluation 50 participants, 400 ratings (test); Mean Score: 4.84. - [Negation-aware Video Generation Benchmark](): Negation-aware Video Generation; Negation-aware Video Generation Benchmark; CLIPScore: 0.2924. - [Negation-aware Video Generation on Negation-aware Evaluation Suite (AOC, LEN, INA, MNC, SFN, NMI, DNS, SND) (full evaluation set)](): Negation-aware Video Generation; Negation-aware Evaluation Suite (AOC, LEN, INA, MNC, SFN, NMI, DNS, SND) (full evaluation set); CLIPScore: 0.2924. ## Original sources and implementation [Original paper]() [PDF]()