这个框架不用重训模型，就能在图片和视频生成时自动修语义错，比之前的免训练方法更靠谱。
自回归视觉模型（AVM）基于下一尺度预测进行图像和视频合成，但语义错误易累积。现有免训练方法忽略中间生成状态，导致错误未被诊断。Gazer框架引入多模态大语言模型反馈，在AVM采样循环中通过反思诊断和语义修正两个阶段实时纠正错误。在组合图像和视频基准测试中，Gazer提升了多个AVM的语义对齐和组合准确性，无需额外训练。
Training-Free Semantic Correction for Autoregressive Visual Models
Autoregressive visual models (AVMs) based on next-scale prediction have emerged as a prominent paradigm for image and video synthesis. However, decomposing the generation process into discrete scales with varying granularities in AVM makes semantic errors difficult to identify and correct, thereby undermining the quality of the final output. Prior efforts to enhance AVM can be categorized into training-based and training-free approaches. Although training-based efforts to enhance AVM generation quality come at substantial computational cost, existing training-free methods neglect intermediate generation states, leaving semantic errors undiagnosed and allowing them to accumulate into the final output. In this paper, we focus on training-free paradigms and propose Gazer, a framework that integrates multimodal large language model feedback into the AVM sampling loop for in-generation semantic correction. Concretely, Gazer operates via two cooperating stages: the Reflective Diagnosis stage diagnoses semantic errors from intermediate states, while the Semantic Correction stage rewinds and rectifies the generation trajectory to realign with the target prompt. Experiments on compositional image and video benchmarks demonstrate that Gazer improves semantic alignment and compositional accuracy across multiple AVMs without additional training.