Qwen-Image-Agent: Bridging the Context Gap in Real-World Image Generation
- Published
- Source
- arXiv
- Paper number
- 503
- Field
- Computer Vision
- arXiv ID
- 2606.26907
Key points
- It defines the root cause of real-world T2I failures as the Context Gap and proposes an agent paradigm to address it.
- It structures Context-Aware Planning into three stages, information-level, content-level, and generation-level, for identifying missing context, collecting it, and allocating it.
- Context Grounding collects context from four channels: reasoning, web and image search, memory, and feedback.
- It builds IA-Bench with four capabilities, Plan, Reason, Search, and Memory, 17 tasks, 730 test instances, and 1,801 checklist items.
- The intelligence of the MLLM backbone is decisive for the whole system, and swapping the backbone causes large drops on most metrics.
- It finds that overusing image search can hurt generation quality, which shows that the search boundary must match the generator's ability.
Paper links
External research summaries. These are not HDATF publications or measured product results.