Gen-VCoT: Generative Visual Chain-of-Thought Reasoning via Diffusion-Based RGB Intermediate Representations
arXiv:2606.16783v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) excel at visual reasoning but rely on text-based chain-of-thought (CoT), lacking interpretable visual intermediates. Existing methods use opaque tokens or external tools, missing key properties. We propose Gen-VCoT, a framework using expert vision models to generate RGB images as reasoning intermediates. It has three stages: visual grounding (SAM segmentation), geometric reasoning (Marigold depth maps), and semantic reasoning (Qwen2-VL integration). An adaptive router selects reasoning depth. Evaluations
The evolution of multimodal large language models necessitates more interpretable and robust visual reasoning capabilities as they seek to move beyond text-based CoT.
Improving visual reasoning in MLLMs by generating interpretable visual intermediates could lead to more reliable, explainable, and advanced AI systems for complex tasks.
AI models gain the ability to 'visualize' their thought processes, enabling better debugging, understanding, and potentially more sophisticated interaction with the physical world.
- · AI developers
- · Robotics
- · Computer vision researchers
- · Industries relying on visual inspection
- · Systems relying solely on opaque text-based reasoning
Gen-VCoT provides a novel approach to multimodal reasoning by using diffusion models to create visual intermediate representations.
This enhanced visual reasoning could accelerate the development of more capable AI agents and autonomous systems.
The ability of machines to 'think' visually could bridge the gap between human perception and AI processing, leading to more intuitive human-AI collaboration.
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Read at arXiv cs.AI