Qwen-Image-Agent：填补现实世界图像生成中的上下文空白
Qwen-Image-Agent: Bridging the Context Gap in Real-World Image Generation
摘要
虽然文本到图像模型已经取得了显著的进展，但它们在处理那些描述不清晰、隐含信息多或需要依赖最新知识的实际应用场景时却面临困难。我们将这种挑战称为“上下文缺口”：即用户上下文与图像生成模型所需的完整上下文之间的不匹配问题。为了弥补这一缺陷，我们提出了Qwen-Image-Agent这一统一的智能体框架，该框架以上下文为中心的方式整合了规划、推理、搜索、记忆和反馈功能。Qwen-Image-Agent将用户输入视为部分上下文，通过上下文感知规划与上下文定位机制逐步构建完整的生成上下文。具体来说，上下文感知规划能够识别缺失的上下文信息，并规划如何获取和利用这些信息；而上下文定位则通过推理、搜索、记忆和反馈机制来收集这些上下文信息。为了评估这种智能体式图像生成能力，我们还引入了Image Agent Bench（IA-Bench）这一基准测试平台，该平台涵盖了规划、推理、搜索和记忆这四项核心能力。在IA-Bench、Mindbench和WISE-Verified上的实验表明，Qwen-Image-Agent的性能优于其他基线模型，达到了最先进的水平。
English Abstract
While text-to-image (T2I) models have achieved remarkable progress, they struggle with real-world requests that are often underspecified, implicit, or dependent on up-to-date knowledge. We identify this challenge as the Context Gap: the mismatch between the user context and the sufficient generation context for T2I models. To bridge this gap, we propose Qwen-Image-Agent, a unified agentic framework that integrates plan, reason, search, memory and feedback in a context-centric manner. Qwen-Image-Agent treats user input as partial context and progressively constructs the generation context through Context-Aware Planning and Context Grounding. Specifically, Context-Aware Planning identifies missing context and plans how it should be acquired and used, while Context Grounding gathers this context from reason, search, memory, and feedback. To evaluate agentic image generation, we further introduce Image Agent Bench (IA-Bench), a benchmark covering four core image agent capabilities: Plan, Reason, Search, and Memory. Experiments on IA-Bench, Mindbench and WISE-Verified show that Qwen-Image-Agent outperforms strong baselines and achieves state-of-the-art performance.