Mind-Brush: 将代理认知搜索与推理整合到图像生成中
Mind-Brush: Integrating Agentic Cognitive Search and Reasoning into Image Generation
AI总结：
Mind-Brush通过整合代理认知搜索与推理，提升图像生成模型对复杂知识推理和动态适应能力，实现性能显著跃升。
摘要：
尽管文本到图像生成已实现了前所未有的保真度，但现有大多数模型本质上仍是以静态文本到像素解码器为基础。因此，它们往往无法理解隐含的用户意图。尽管新兴的统一理解和生成模型在意图理解方面有所改善，但仍难以在单一模型中完成涉及复杂知识推理的任务。此外，受限于静态内部先验，这些模型仍然无法适应现实世界的动态变化。为了弥合这些差距，我们引入了Mind-Brush，一个统一的代理框架，将生成转变为一个动态、知识驱动的工作流程。模拟人类般的'思考-研究-创造'范式，Mind-Brush主动检索多模态证据以确定分布外概念，并利用推理工具解决隐含的视觉约束。为了严格评估这些能力，我们提出了Mind-Bench，一个包含500个不同样本的综合基准，涵盖实时新闻、新兴概念以及数学和Geo-Reasoning等领域。广泛的实验表明，Mind-Brush显著增强了统一模型的能力，在Mind-Bench上实现了Qwen-Image基准的零到一能力飞跃，同时在已建立的基准如WISE和RISE上也取得了更优的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
