Integrating Chain-of-Thought for Multimodal Alignment: A Study on 3D Vision-Language Learning

Understand

Chain-of-Thought (CoT) reasoning has proven effective in natural language tasks but remains underexplored in multimodal alignment.

• This study investigates its integration into 3D vision-language learning by embedding structured reasoning into alignment training.
• We introduce the 3D-CoT Benchmark, a dataset with hierarchical CoT annotations covering shape recognition, functional inference, and causal reasoning.
• Through controlled experiments, we compare CoT-structured and standard textual annotations across large reasoning models (LRMs) and large language models (LLMs).