In-context image generation and editing (ICGE) enables users to specify visual concepts through interleaved image-text prompts, demanding precise understanding and faithful execution of user intent. Although recent unified multimodal models exhibit promising understanding capabilities, these strengths often fail to transfer effectively to image generation. We introduce Re-Align, a unified framework that bridges the gap between understanding and generation through structured reasoning-guided alignment. At its core lies the In-Context Chain-of-Thought (IC-CoT), a structured reasoning paradigm that decouples semantic guidance and reference association, providing clear textual target and mitigating confusion among reference images. Furthermore, Re-Align introduces an effective RL training scheme that leverages a surrogate reward to measure the alignment between structured reasoning text and the generated image, thereby improving the model's overall performance on ICGE tasks. Extensive experiments verify that Re-Align outperforms competitive methods of comparable model scale and resources on both in-context image generation and editing tasks.

翻译：上下文图像生成与编辑（ICGE）允许用户通过交错的图像-文本提示来指定视觉概念，这要求模型对用户意图进行精确理解并忠实执行。尽管近期出现的统一多模态模型展现出有前景的理解能力，但这些优势往往未能有效迁移至图像生成任务。我们提出Re-Align，一个通过结构化推理引导的对齐来弥合理解与生成之间差距的统一框架。其核心是上下文思维链（IC-CoT），这是一种结构化推理范式，能够解耦语义引导与参考关联，提供清晰的文本目标并减轻参考图像间的混淆。此外，Re-Align引入了一种有效的强化学习训练方案，该方案利用代理奖励来衡量结构化推理文本与生成图像之间的对齐程度，从而提升模型在ICGE任务上的整体性能。大量实验验证，Re-Align在上下文图像生成与编辑任务上均优于模型规模和资源相当的主流方法。