Do multimodal models imagine electric sheep?
📰 ArXiv cs.AI
Discover how multimodal models develop mental imagery to solve spatial puzzles, and learn to apply this concept to your own AI projects
Action Steps
- Fine-tune a multimodal model like Qwen3.5 VLM on diverse visual reasoning tasks to develop its mental imagery capabilities
- Use open-loop sequence prediction to supervise the model and improve its performance on tasks like tangram, jigsaw, and sokoban
- Apply the concept of mental imagery to solve spatial puzzles and understand geometry, spatial relationships, and the consequences of actions
- Evaluate the model's performance on 3D mental rotation and rush hour tasks to assess its ability to reason visually
- Compare the results of the fine-tuned model with other state-of-the-art models to identify areas for improvement
Who Needs to Know This
AI researchers and engineers working on multimodal models can benefit from understanding how these models develop mental imagery, and apply this knowledge to improve their models' performance on visual reasoning tasks
Key Insight
💡 Multimodal models can develop mental imagery, enabling them to solve complex visual reasoning tasks and understand spatial relationships
Share This
🤖 Multimodal models can develop mental imagery to solve spatial puzzles! 📝 New research on Qwen3.5 VLM reveals the potential of these models to reason visually and understand geometry and spatial relationships
Key Takeaways
Discover how multimodal models develop mental imagery to solve spatial puzzles, and learn to apply this concept to your own AI projects
Full Article
Title: Do multimodal models imagine electric sheep?
Abstract:
arXiv:2605.09693v1 Announce Type: cross Abstract: Yes. We find that large multimodal models develop mental imagery when solving spatial puzzles, and they do imagine sheep when solving sheep puzzles. We fine-tune a Qwen3.5 VLM to solve twelve diverse visual reasoning tasks -- including tangram, jigsaw, sokoban, 3D mental rotation, and rush hour -- that require understanding geometry, spatial relationships, and the consequences of actions. By supervising the model to predict the open-loop sequence
Abstract:
arXiv:2605.09693v1 Announce Type: cross Abstract: Yes. We find that large multimodal models develop mental imagery when solving spatial puzzles, and they do imagine sheep when solving sheep puzzles. We fine-tune a Qwen3.5 VLM to solve twelve diverse visual reasoning tasks -- including tangram, jigsaw, sokoban, 3D mental rotation, and rush hour -- that require understanding geometry, spatial relationships, and the consequences of actions. By supervising the model to predict the open-loop sequence
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