AI Multimodal Reasoning

MLLM show less than 50% accuracy in visually recognizing or systematically counting edges of even simple regular polygons, due to the vision encoder's 'shape-blind' phenomenon that prevents it from distinguishing rare shapes. The models rely only on intuition and memorization (

System 1 Thinking) without performing logical step-by-step reasoning (

System 2 Thinking). However, when applying Visually-Cued CoT prompts that label each shape's edges with numbers/characters and guide step-by-step, GPT-4v's accuracy in counting edges of irregular polygons dramatically improves from 7% to 93%.