UniReason 1.0: A Unified Reasoning Framework for World Knowledge Aligned Image Generation and Editing
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 a dual reasoning paradigm. We formulate generation as world knowledge-enhanced planning to inject implicit constraints, and leverage editing capabilities for fine-grained visual refinement to further correct visual errors via self-reflection. This approach unifies generation and editing within a shared representation, 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 planning, alongside an agent-generated corpus for visual self-correction. 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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Dianyi Wang
claimed_verified
2026-02-11T22:17:08.291Z
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Alex Wang(SII)
Alex11556666
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Alex11556666
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Chaofan Ma
claimed_verified
2026-03-27T11:38:49.246Z
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chaofanma
chaofanma
user
chaofanma
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Feng Han
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Size Wu
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Wei Song
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Yibin Wang
claimed_verified
2026-02-03T10:07:21.031Z
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SII-Yibin Wang
CodeGoat24
user
CodeGoat24
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Zhixiong Zhang
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Tianhang Wang
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Siyuan Wang
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Zhongyu Wei
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Jiaqi Wang
claimed_verified
2026-02-17T15:50:08.305Z
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Jiaqi Wang
myownskyW7
user
myownskyW7
UniReason 1.0: A Unified Reasoning Framework for World Knowledge Aligned Image Generation and Editing
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 a dual reasoning paradigm. We formulate generation as world knowledge-enhanced planning to inject implicit constraints, and leverage editing capabilities for fine-grained visual refinement to further correct visual errors via self-reflection. This approach unifies generation and editing within a shared representation, 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 planning, alongside an agent-generated corpus for visual self-correction. Extensive experiments demonstrate that UniReason achieves advanced performance on reasoning-intensive benchmarks such as WISE, KrisBench and UniREditBench, while maintaining superior general synthesis capabilities.
UniReason integrates text-to-image generation and image editing through a dual reasoning paradigm that enhances planning with world knowledge and uses editing for visual refinement, achieving superior performance on reasoning-intensive benchmarks.
text-to-image generation image editing dual reasoning paradigm world knowledge-enhanced planning visual self-correction shared representation reasoning-intensive benchmarks WISE KrisBench UniREditBench