UniReason 1.0: A Unified Reasoning Framework for World Knowledge Aligned Image Generation and Editing
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866918346937597952 |
|---|---|
| author | Wang, Dianyi Ma, Chaofan Han, Feng Wu, Size Song, Wei Wang, Yibin Zhang, Zhixiong Wang, Tianhang Wang, Siyuan Wei, Zhongyu Wang, Jiaqi |
| author_facet | Wang, Dianyi Ma, Chaofan Han, Feng Wu, Size Song, Wei Wang, Yibin Zhang, Zhixiong Wang, Tianhang Wang, Siyuan Wei, Zhongyu Wang, Jiaqi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_02437 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | UniReason 1.0: A Unified Reasoning Framework for World Knowledge Aligned Image Generation and Editing Wang, Dianyi Ma, Chaofan Han, Feng Wu, Size Song, Wei Wang, Yibin Zhang, Zhixiong Wang, Tianhang Wang, Siyuan Wei, Zhongyu Wang, Jiaqi Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | UniReason 1.0: A Unified Reasoning Framework for World Knowledge Aligned Image Generation and Editing |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2602.02437 |