标题： 重新对齐：结构化推理引导的上下文图像生成与编辑对齐 显示英文标题

标题： Re-Align: Structured Reasoning-guided Alignment for In-Context Image Generation and Editing

摘要： 通过上下文的图像生成和编辑（ICGE），用户可以通过交错的图像-文本提示来指定视觉概念，这需要精确理解并忠实执行用户的意图。 尽管最近的统一多模态模型表现出有希望的理解能力，但这些优势通常难以有效地转移到图像生成中。 我们引入了Re-Align，这是一个统一的框架，通过结构化推理引导的对齐来弥合理解和生成之间的差距。 其核心是上下文链式思维（IC-CoT），这是一种结构化推理范式，将语义指导和参考关联解耦，提供清晰的文本目标并减少参考图像之间的混淆。 此外，Re-Align引入了一种有效的强化学习训练方案，利用替代奖励来衡量结构化推理文本与生成图像之间的对齐程度，从而提高模型在ICGE任务中的整体性能。 大量实验验证了Re-Align在上下文图像生成和编辑任务中，优于具有可比模型规模和资源的竞争方法。 显示英文摘要

摘要： 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.