这篇论文给出了分析对话的实用框架，在9个数据集上测试过，特别点出元认知调节对协作深度的重要性。
本文提出一个概念框架，用于分析协作问题解决中的对话，尤其关注人类-AI和多智能体协作的动态。该框架通过一个层次化两层编码方案，整合认知与非认知问题解决及元认知调节机制。在跨越多个领域的9个数据集上验证了其有效性和泛化能力，发现元认知调节是深层协作的关键区分器。
Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts
We present a conceptual framework for analyzing dialogue in collaborative problem-solving contexts, with an emphasis on the emerging dynamics of human-AI and multi-agent collaboration. As intelligent systems become active agents capable of autonomous reasoning and strategic cooperation, understanding the dialogic interaction during collaborative problem solving is increasingly important for optimizing and evaluating such partnerships. Our framework addresses key limitations in current analytical approaches through a hierarchical two-layer coding scheme that integrates cognitive and non-cognitive problem solving with metacognitive regulatory mechanisms. We demonstrate its effectiveness and generalizability across nine datasets spanning multiple domains, and provide insights into how humans and agents coordinate their knowledge, skills, and efforts to solve complex problems, showing in particular that metacognitive regulation can be an essential discriminator of deeper collaboration.