Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics
🤖 AI Summary
Existing chain-of-thought (CoT) reasoning lacks sufficient interpretability, being confined to token-level attribution and failing to model the high-level semantic roles of reasoning steps and their dynamic evolution. Method: We propose a state-aware transition framework that, for the first time, formalizes CoT reasoning as a Markov process. Leveraging spectral analysis and semantic clustering on token embeddings, our approach extracts structured latent states that capture semantic roles, temporal patterns, and logical consistency across reasoning steps. Contribution/Results: The method enables structured parsing of reasoning paths, supporting semantic role identification, visualizable step-by-step tracking, and quantitative consistency evaluation. Empirical results demonstrate significant improvements in transparency and interpretability of multi-step reasoning in large language models, advancing beyond shallow, token-level explanations toward principled, semantically grounded interpretability.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Analyzing semantic roles and transitions in reasoning steps
Abstracting chain-of-thought trajectories into structured dynamics
Providing interpretable global view of multi-step reasoning process
Innovation
Methods, ideas, or system contributions that make the work stand out.
State-aware transition framework for CoT
Spectral analysis of token embeddings
Markov chain modeling reasoning progression
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