# Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics

## TL;DR

This paper introduces a state-aware framework for analyzing chain-of-thought reasoning in large language models, enhancing interpretability by modeling reasoning steps as a Markov chain of latent semantic states.

## Contribution

It proposes a novel spectral clustering and Markov chain modeling approach to capture and visualize the global dynamics of reasoning processes in LLMs.

## Key findings

- Semantic states identified via spectral analysis
- Global reasoning dynamics visualized as Markov chains
- Enhanced interpretability of reasoning trajectories

## 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.