German researchers introduce ReImaGin for image-based chain-of-thought reasoning

The system was tested across three VLMs (Gemini-3.1-Pro, GPT-5, and Qwen-3.5-27B) and six visual reasoning tasks, outperforming both unaided reasoning and fixed-tool baselines like Visual Sketchpad. For example, GPT-5’s puzzle accuracy improved from 29.5% (no tools) to 44.9% with ReImaGin. The approach relies on an iterative loop where the model experiments with prompts to discover effective visual strategies, optimizing instructions rather than retraining models. Open-weight image generators like FLUX.2 [dev] and Qwen-Image-Edit-2511 were also tested, though they showed mixed results on complex tasks.

The authors argue that image generation can serve as a general ‘visual imagination’ mechanism, reducing reliance on task-specific tools. The paper suggests this method could evolve as VLMs gain broader self-tooling capabilities.