See2Think Framework Tests Whether Multimodal AI Really Uses Visual Reasoning
A recent study has unveiled See2Think, a comprehensive evaluation framework aimed at assessing whether multimodal large language models truly utilize intermediate visual states—like sketches, annotations, and rendered images—during reasoning, or if they simply mimic visual comprehension. This framework consists of two key elements: See2ThinkBench, which features 1,200 open-ended, visually dependent challenges across 12 task categories, including 2D structured, 3D scene, and real-world reasoning; and Visual Action-of-Thought (VAoT), which captures textual thoughts, visual actions, rendered states, and subsequent reasoning across four controlled inference scenarios. The research examines various proprietary and open-source multimodal models, revealing that visual reasoning is frequently superficial. The findings are available on arXiv with the identifier 2607.26769.
Key facts
- See2Think is a unified evaluation framework for multimodal LLMs.
- See2ThinkBench contains 1,200 open-ended problems across 12 task categories.
- Categories cover 2D structured, 3D scene, and real-world reasoning.
- VAoT records textual thoughts, visual actions, rendered states, and reasoning.
- Four controlled inference settings are used in VAoT.
- Evaluated proprietary and open-source multimodal models.
- Findings indicate visual reasoning is often superficial.
- Paper published on arXiv with ID 2607.26769.
Entities
Institutions
- arXiv