Chain-of-Thought Reasoning Impairs Spatial Intelligence in Multimodal AI Models
An extensive assessment of seventeen multimodal reasoning models tested against thirteen spatial benchmarks indicates that Chain-of-Thought prompting consistently undermines performance in visual spatial reasoning challenges. Although CoT-based methodologies have revolutionized mathematical and logical problem-solving, they falter in generalized spatial intelligence. A new No-Image++ ablation study revealed that MRMs and CoT-prompted MLMs experience significant shortcut learning, often fabricating visual elements from text even in the absence of images. These results question the effectiveness of text-only CoT for spatial tasks and highlight the necessity for vision-centered reasoning frameworks. Published on arXiv, this research exposes a significant shortcoming in existing multimodal AI strategies for spatial reasoning.
Key facts
- Chain-of-Thought prompting degrades performance in visual spatial reasoning
- Seventeen multimodal reasoning models were evaluated
- Thirteen spatial benchmarks were used in the evaluation
- Models suffer from severe shortcut learning
- Models hallucinate visual details from textual priors
- Text-only CoT is ineffective for spatial tasks
- Vision-centric reasoning paradigms are needed
- Research was published on arXiv
Entities
Institutions
- arXiv