@inproceedings{yu-etal-2025-explainable,
    title = "Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics",
    author = "Yu, Sheldon  and
      Xiong, Yuxin  and
      Wu, Junda  and
      Li, Xintong  and
      Yu, Tong  and
      Chen, Xiang  and
      Sinha, Ritwik  and
      Shang, Jingbo  and
      McAuley, Julian",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.904/",
    doi = "10.18653/v1/2025.findings-emnlp.904",
    pages = "16660--16667",
    ISBN = "979-8-89176-335-7",
    abstract = "Recent advances in chain-of-thought (CoT) prompting have demonstrated the ability of large language models (LLMs) to perform multi-step reasoning. While prior work focuses on improving CoT generation quality or attributing token-level importance, we propose a novel framework to structurally analyze the latent dynamics of CoT trajectories for interpretability. Our method segments generated CoT into discrete reasoning steps, abstracts each step into a spectral embedding based on the eigenvalues of token-level Gram matrices, and clusters these embeddings into semantically meaningful latent states. We model the global evolution of reasoning as a first-order Markov chain over latent clusters, yielding interpretable transition structures. Through t-SNE visualizations and Monte Carlo rollouts, we uncover consistent trajectories across tasks and models, supporting the hypothesis that LLM reasoning follows globally coherent yet abstract paths."
}