German researchers introduce ReImaGin for image-based chain-of-thought reasoning
A team of researchers from Germany has developed ReImaGin, a vision-language model (VLM) that uses image generation as part of its internal reasoning process. Unlike previous systems that relied on bolted-on image components, ReImaGin integrates image generation as a core mechanism for tasks like depth perception, occlusion counting, and spatial mapping. The model dynamically decides when to…
Key points
- ReImaGin uses image generation as part of its internal chain-of-thought reasoning, dynamically deciding when to create visual aids
- Tested on **Gemini-3.1-Pro**, **GPT-5**, and **Qwen-3.5-27B**, outperforming fixed-tool baselines in tasks like puzzle completion and occlusion counting
- Open-weight image generators like **FLUX.2 [dev]** and **Qwen-Image-Edit-2511** showed mixed performance, with **75%** of ReImaGin’s generated images deemed accurate
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.
AI That Uses Imagery in Chain-of-Thought (CoT) Reasoning
Unite.AI · 28 September 2026
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