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[2602.02437] UniReason 1.0: A Unified Reasoning Framework for World Knowledge Aligned Image Generation and Editing
Summary
UniReason 1.0 presents a unified framework for image generation and editing, integrating textual reasoning and visual refinement to enhance performance in complex synthesis tasks.
Why It Matters
This framework addresses the limitations of existing multimodal models by combining text-to-image generation and image editing into a cohesive process. By leveraging world knowledge and enhancing reasoning capabilities, UniReason aims to improve the quality and accuracy of generated images, which is crucial for applications in AI-driven content creation and visual storytelling.
Key Takeaways
- UniReason integrates text-to-image generation and image editing into a single framework.
- The model uses world knowledge to enhance textual reasoning and visual refinement.
- Extensive experiments show superior performance on reasoning-intensive benchmarks.
- A large-scale dataset supports the framework, covering multiple knowledge domains.
- The approach mirrors human cognitive processes, improving synthesis capabilities.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.02437 (cs) [Submitted on 2 Feb 2026 (v1), last revised 20 Feb 2026 (this version, v4)] Title:UniReason 1.0: A Unified Reasoning Framework for World Knowledge Aligned Image Generation and Editing Authors:Dianyi Wang, Chaofan Ma, Feng Han, Size Wu, Wei Song, Yibin Wang, Zhixiong Zhang, Tianhang Wang, Siyuan Wang, Zhongyu Wei, Jiaqi Wang View a PDF of the paper titled UniReason 1.0: A Unified Reasoning Framework for World Knowledge Aligned Image Generation and Editing, by Dianyi Wang and 10 other authors View PDF Abstract: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 dom...