让大模型推理过程可解释，看清每步逻辑的演变与关联。
Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics
- 用谱分析和聚类将推理步骤抽象为语义状态
- 构建马尔可夫链描述状态转移，揭示整体推理结构
- 适合研究模型推理机制或提升可信度的开发者
链式思维（CoT）提示已使大语言模型具备多步推理能力，但其可解释性仍有限，以往工作多聚焦于局部标记级归因，对推理步骤的高层语义角色及其转换关系研究不足。本文提出一种状态感知的转换框架，将CoT轨迹抽象为结构化的潜在动态。具体而言，通过词元嵌入的谱分析将每个推理步骤表示为语义连贯的潜在状态，并以马尔可夫链建模其演化过程，获得可解释的推理结构视图。该抽象支持多种分析，包括语义角色识别、时间模式可视化及一致性评估。
原文摘要 · Abstract (English)
Recent advances in chain-of-thought (CoT) prompting have enabled large language models (LLMs) to perform multi-step reasoning. However, the explainability of such reasoning remains limited, with prior work primarily focusing on local token-level attribution, such that the high-level semantic roles of reasoning steps and their transitions remain underexplored. In this paper, we introduce a state-aware transition framework that abstracts CoT trajectories into structured latent dynamics. Specifically, to capture the evolving semantics of CoT reasoning, each reasoning step is represented via spectral analysis of token-level embeddings and clustered into semantically coherent latent states. To characterize the global structure of reasoning, we model their progression as a Markov chain, yielding a structured and interpretable view of the reasoning process. This abstraction supports a range of analyses, including semantic role identification, temporal pattern visualization, and consistency evaluation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。