GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles
This addresses the challenge of generating complex, interactive puzzle images for applications in gaming or education, though it is incremental as it builds on existing multi-agent and image generation methods.
The paper tackled the problem of generating escape room puzzle images that are visually appealing and logically solid using text-to-image models, and the result was a hierarchical multi-agent framework that improved output quality in terms of solvability, shortcut avoidance, and affordance clarity.
We challenge text-to-image models with generating escape room puzzle images that are visually appealing, logically solid, and intellectually stimulating. While base image models struggle with spatial relationships and affordance reasoning, we propose a hierarchical multi-agent framework that decomposes this task into structured stages: functional design, symbolic scene graph reasoning, layout synthesis, and local image editing. Specialized agents collaborate through iterative feedback to ensure the scene is visually coherent and functionally solvable. Experiments show that agent collaboration improves output quality in terms of solvability, shortcut avoidance, and affordance clarity, while maintaining visual quality.