Qwen-Image-Agent：橋接真實世界圖像生成中的上下文差距

摘要

儘管文字轉圖像（T2I）模型已取得顯著進展，但它們在處理現實世界中常存在表述不清、隱含或依賴最新知識的請求時仍有困難。我們將此挑戰定義為「上下文鴻溝」（Context Gap）：使用者上下文與 T2I 模型所需充分生成上下文之間的落差。為彌合此鴻溝，我們提出 Qwen-Image-Agent，一個以上下文為核心的統一代理框架，整合規劃、推理、搜尋、記憶與反饋。Qwen-Image-Agent 將使用者輸入視為部分上下文，並透過「上下文感知規劃」（Context-Aware Planning）與「上下文錨定」（Context Grounding）逐步建構生成上下文。具體而言，上下文感知規劃識別缺失的上下文，並規劃其獲取與使用方式；上下文錨定則透過推理、搜尋、記憶與反饋收集這些上下文。為評估代理式影像生成，我們進一步引入 IA-Bench（Image Agent Bench），該基準涵蓋四項核心影像代理能力：規劃、推理、搜尋與記憶。在 IA-Bench、Mindbench 與 WISE-Verified 上的實驗結果顯示，Qwen-Image-Agent 超越強基線方法，達到最先進的效能。

English

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.