Mind-Brush: Integrating Agentic Cognitive Search and Reasoning into Image Generation
While text-to-image generation has achieved unprecedented fidelity, the vast majority of existing models function fundamentally as static text-to-pixel decoders. Consequently, they often fail to grasp implicit user intentions. Although emerging unified understanding-generation models have improved intent comprehension, they still struggle to accomplish tasks involving complex knowledge reasoning within a single model. Moreover, constrained by static internal priors, these models remain unable to adapt to the evolving dynamics of the real world. To bridge these gaps, we introduce Mind-Brush, a unified agentic framework that transforms generation into a dynamic, knowledge-driven workflow. Simulating a human-like 'think-research-create' paradigm, Mind-Brush actively retrieves multimodal evidence to ground out-of-distribution concepts and employs reasoning tools to resolve implicit visual constraints. To rigorously evaluate these capabilities, we propose Mind-Bench, a comprehensive benchmark comprising 500 distinct samples spanning real-time news, emerging concepts, and domains such as mathematical and Geo-Reasoning. Extensive experiments demonstrate that Mind-Brush significantly enhances the capabilities of unified models, realizing a zero-to-one capability leap for the Qwen-Image baseline on Mind-Bench, while achieving superior results on established benchmarks like WISE and RISE.
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Jun He
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Junyan Ye
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Zilong Huang
claimed_verified
2026-02-03T10:07:44.054Z
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Zilong Huang
SereinH
user
SereinH
69817073ce18b18628095ff8
Dongzhi Jiang
claimed_verified
2026-03-05T09:58:46.383Z
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Dongzhi Jiang
CaraJ
user
CaraJ
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Chenjue Zhang
69817073ce18b18628095ffa
Leqi Zhu
69817073ce18b18628095ffb
Renrui Zhang
69817073ce18b18628095ffc
Xiang Zhang
69817073ce18b18628095ffd
Weijia Li
Mind-Brush: Integrating Agentic Cognitive Search and Reasoning into Image Generation
While text-to-image generation has achieved unprecedented fidelity, the vast majority of existing models function fundamentally as static text-to-pixel decoders. Consequently, they often fail to grasp implicit user intentions. Although emerging unified understanding-generation models have improved intent comprehension, they still struggle to accomplish tasks involving complex knowledge reasoning within a single model. Moreover, constrained by static internal priors, these models remain unable to adapt to the evolving dynamics of the real world. To bridge these gaps, we introduce Mind-Brush, a unified agentic framework that transforms generation into a dynamic, knowledge-driven workflow. Simulating a human-like 'think-research-create' paradigm, Mind-Brush actively retrieves multimodal evidence to ground out-of-distribution concepts and employs reasoning tools to resolve implicit visual constraints. To rigorously evaluate these capabilities, we propose Mind-Bench, a comprehensive benchmark comprising 500 distinct samples spanning real-time news, emerging concepts, and domains such as mathematical and Geo-Reasoning. Extensive experiments demonstrate that Mind-Brush significantly enhances the capabilities of unified models, realizing a zero-to-one capability leap for the Qwen-Image baseline on Mind-Bench, while achieving superior results on established benchmarks like WISE and RISE.
Mind-Brush presents a unified agentic framework for text-to-image generation that dynamically retrieves multimodal evidence and employs reasoning tools to improve understanding of implicit user intentions and complex knowledge reasoning.
text-to-image generation unified understanding-generation models implicit user intentions multimodal evidence reasoning tools dynamic workflow agentic framework knowledge-driven workflow out-of-distribution concepts visual constraints