Unified multimodal models often struggle with complex synthesis tasks that demand deep reasoning, and typically treat text-to-image generation and image editing as isolated capabilities rather than interconnected reasoning steps. To address this, we propose UniReason, a unified framework that harmonizes these two tasks through two complementary reasoning paradigms. We incorporate world knowledge-enhanced textual reasoning into generation to infer implicit knowledge, and leverage editing capabilities for fine-grained editing-like visual refinement to further correct visual errors via self-reflection. This approach unifies generation and editing within a shared architecture, mirroring the human cognitive process of planning followed by refinement. We support this framework by systematically constructing a large-scale reasoning-centric dataset (~300k samples) covering five major knowledge domains (e.g., cultural commonsense, physics, etc.) for textual reasoning, alongside an agent-generated corpus for visual refinement. Extensive experiments demonstrate that UniReason achieves advanced performance on reasoning-intensive benchmarks such as WISE, KrisBench and UniREditBench, while maintaining superior general synthesis capabilities.

翻译：统一的多模态模型在处理需要深度推理的复杂合成任务时常常面临困难，并且通常将文本到图像生成和图像编辑视为孤立的能力，而非相互关联的推理步骤。为解决这一问题，我们提出了UniReason，一个通过两种互补的推理范式来协调这两项任务的统一框架。我们将世界知识增强的文本推理融入生成过程以推断隐含知识，并利用编辑能力进行类似细粒度编辑的视觉精修，通过自我反思进一步纠正视觉错误。该方法在共享架构内统一了生成与编辑，模拟了人类先规划后精修的认知过程。我们通过系统性地构建一个大规模以推理为中心的数据集（约30万个样本）来支持该框架，该数据集涵盖五个主要知识领域（例如文化常识、物理学等）用于文本推理，同时包含一个智能体生成的语料库用于视觉精修。大量实验表明，UniReason在WISE、KrisBench和UniREditBench等推理密集型基准测试中取得了先进的性能，同时保持了卓越的通用合成能力。