AI RESEARCH
Semantic Context Matters: Improving Conditioning for Autoregressive Models
arXiv CS.CV
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ArXi:2511.14063v2 Announce Type: replace Recently, autoregressive (AR) models have shown strong potential in image generation, offering better scalability and easier integration with unified multi-modal systems compared to diffusion-based methods. However, extending AR models to general image editing remains challenging due to weak and inefficient conditioning, often leading to poor instruction adherence and visual artifacts. To address this, we propose SCAR, a Semantic-Context-driven method for Autoregressive models. SCAR