@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics
%A Yu, Sheldon
%A Xiong, Yuxin
%A Wu, Junda
%A Li, Xintong
%A Yu, Tong
%A Chen, Xiang
%A Sinha, Ritwik
%A Shang, Jingbo
%A McAuley, Julian
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F yu-etal-2025-explainable
%X 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.
%U https://aclanthology.org/2025.findings-emnlp.904/
%P 16660-16667
Markdown (Informal)
[Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics](https://aclanthology.org/2025.findings-emnlp.904/) (Yu et al., Findings 2025)
ACL
- Sheldon Yu, Yuxin Xiong, Junda Wu, Xintong Li, Tong Yu, Xiang Chen, Ritwik Sinha, Jingbo Shang, and Julian McAuley. 2025. Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 16660–16667, Suzhou, China. Association for Computational Linguistics.