Mind-Brush: Integrating Agentic Cognitive Search and Reasoning into Image Generation

• URL: http://arxiv.org/abs/2602.01756v1
• Date: Mon, 02 Feb 2026 07:42:13 GMT
• Title: Mind-Brush: Integrating Agentic Cognitive Search and Reasoning into Image Generation
• Authors: Jun He, Junyan Ye, Zilong Huang, Dongzhi Jiang, Chenjue Zhang, Leqi Zhu, Renrui Zhang, Xiang Zhang, Weijia Li,
• Abstract summary: We introduce Mind-Brush, a unified agentic framework that transforms generation into a dynamic, knowledge-driven workflow.<n>Simulating a human-like 'think-research-create' paradigm, Mind-Brush actively retrieves multimodal evidence to ground out-of-distribution concepts.<n>Extensive experiments demonstrate that Mind-Brush significantly enhances the capabilities of unified models.
• Score: 47.97278965762397
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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