UniReason 1.0: 一个统一的推理框架，用于世界知识对齐的图像生成与编辑
UniReason 1.0: A Unified Reasoning Framework for World Knowledge Aligned Image Generation and Editing
AI总结：
UniReason 1.0通过统一推理框架整合图像生成与编辑，利用世界知识增强文本推理和视觉优化，提升复杂合成任务的性能。
摘要：
统一的多模态模型往往在需要深度推理的复杂合成任务中遇到困难，并且通常将文本到图像生成和图像编辑视为孤立的能力，而不是相互关联的推理步骤。为此，我们提出了UniReason，一个统一的框架，通过两种互补的推理范式来协调这两个任务。我们将在生成中融入增强世界知识的文本推理，以推断隐含知识，并利用编辑能力进行细粒度的编辑式视觉优化，以通过自我反思进一步纠正视觉错误。这种方法在共享架构内统一了生成和编辑，模仿了人类认知过程中的规划后细化过程。我们通过系统构建一个大规模的以推理为中心的数据集（约30万样本）来支持这一框架，该数据集涵盖了五个主要知识领域（例如文化常识、物理等）的文本推理，以及由代理生成的视觉优化语料库。广泛的实验表明，UniReason在需要深度推理的基准测试如WISE、KrisBench和UniREditBench上实现了先进的性能，同时保持了优越的综合合成能力。
英文摘要：
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.
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