将思维链整合到多模态对齐中：一项关于3D视觉-语言学习的研究
Integrating Chain-of-Thought for Multimodal Alignment: A Study on 3D Vision-Language Learning
AI总结：
本文构建带层级CoT标注的3D-CoT Benchmark，通过双层评估比较LRM与LLM，证明结构化思维链可显著提升3D视觉-语言语义对齐与多模态推理。
摘要：
思维链（Chain-of-Thought, CoT）推理已在自然语言任务中被证明有效，但在多模态对齐中的应用仍未得到充分探索。本研究通过将结构化推理嵌入对齐训练，考察其与3D视觉-语言学习的整合。我们提出3D-CoT Benchmark，这是一个带有层级化CoT标注的数据集，涵盖形状识别、功能推理和因果推理。通过受控实验，我们在大型推理模型（LRM）和大型语言模型（LLM）之间比较了CoT结构化文本标注与标准文本标注。评估采用双层框架，同时衡量中间推理与最终推理质量。大量实验表明，CoT显著改善了3D语义落地，且LRM比LLM能更有效地利用CoT。此外，我们强调标注结构会影响性能：显式推理标记有助于LLM，而无标记CoT更契合LRM的推理模式。分析表明，CoT对于增强多模态推理至关重要，并具有超越3D任务的意义。数据集将公开发布于https://huggingface.co/datasets/Battam/3D-CoT。
英文摘要：
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). Our evaluation employs a dual-layer framework assessing both intermediate reasoning and final inference quality. Extensive experiments demonstrate that CoT significantly improves 3D semantic grounding, with LRMs leveraging CoT more effectively than LLMs. Furthermore, we highlight that annotation structure influences performance-explicit reasoning markers aid LLMs, while unmarked CoT better aligns with LRM inference patterns. Our analyses suggest that CoT is crucial for enhancing multimodal reasoning, with implications beyond 3D tasks. The dataset will be publicly available at https://huggingface.co/datasets/Battam/3D-CoT
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
