@inproceedings{hua-etal-2025-eot,
title = "{E}o{T}: Evolution of Thoughts for Complex Reasoning Tasks",
author = "Hua, Qin and
Sun, Jiaqi and
Qian, Shiyou and
Yang, Dingyu and
Cao, Jian and
Xue, Guangtao",
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.1297/",
doi = "10.18653/v1/2025.findings-emnlp.1297",
pages = "23864--23886",
ISBN = "979-8-89176-335-7",
abstract = "Knowledge-based complex reasoning remains a significant challenge for large language models (LLMs) with in-context learning. To tackle this issue, previous studies focus on ensuring behavior fidelity, factuality, or reliability in generated reasoning processes that guide LLMs to produce solutions. However, these studies often neglect the simultaneous optimization on all these three aspects for each thought. The main challenges are the lack of comprehensive assessment mechanisms and the difficulty of efficient thought-level optimization. This paper introduces the Evolution of Thoughts (EoT) framework, which enhances the factuality, fidelity, and reliability of each thought in the reasoning process through a few LLM inferences. We propose a thought assessment method that is sensitive to knowledge and LLM behaviors, using three scorers to evaluate each thought by considering domain context, semantic alignment, and behavior impact. Additionally, we establish a self-reflective evolution mechanism to facilitate each reasoning process generation in a single-forward inference. Extensive experiments demonstrate that, for knowledge-based complex tasks, EoT improves the factuality and fidelity of reasoning processes by approximately 16.5{\%} and 48.8{\%}, respectively, while enhancing LLM reasoning capability by about 6.2{\%}, outperforming advanced approaches."
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<abstract>Knowledge-based complex reasoning remains a significant challenge for large language models (LLMs) with in-context learning. To tackle this issue, previous studies focus on ensuring behavior fidelity, factuality, or reliability in generated reasoning processes that guide LLMs to produce solutions. However, these studies often neglect the simultaneous optimization on all these three aspects for each thought. The main challenges are the lack of comprehensive assessment mechanisms and the difficulty of efficient thought-level optimization. This paper introduces the Evolution of Thoughts (EoT) framework, which enhances the factuality, fidelity, and reliability of each thought in the reasoning process through a few LLM inferences. We propose a thought assessment method that is sensitive to knowledge and LLM behaviors, using three scorers to evaluate each thought by considering domain context, semantic alignment, and behavior impact. Additionally, we establish a self-reflective evolution mechanism to facilitate each reasoning process generation in a single-forward inference. Extensive experiments demonstrate that, for knowledge-based complex tasks, EoT improves the factuality and fidelity of reasoning processes by approximately 16.5% and 48.8%, respectively, while enhancing LLM reasoning capability by about 6.2%, outperforming advanced approaches.</abstract>
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%0 Conference Proceedings
%T EoT: Evolution of Thoughts for Complex Reasoning Tasks
%A Hua, Qin
%A Sun, Jiaqi
%A Qian, Shiyou
%A Yang, Dingyu
%A Cao, Jian
%A Xue, Guangtao
%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 hua-etal-2025-eot
%X Knowledge-based complex reasoning remains a significant challenge for large language models (LLMs) with in-context learning. To tackle this issue, previous studies focus on ensuring behavior fidelity, factuality, or reliability in generated reasoning processes that guide LLMs to produce solutions. However, these studies often neglect the simultaneous optimization on all these three aspects for each thought. The main challenges are the lack of comprehensive assessment mechanisms and the difficulty of efficient thought-level optimization. This paper introduces the Evolution of Thoughts (EoT) framework, which enhances the factuality, fidelity, and reliability of each thought in the reasoning process through a few LLM inferences. We propose a thought assessment method that is sensitive to knowledge and LLM behaviors, using three scorers to evaluate each thought by considering domain context, semantic alignment, and behavior impact. Additionally, we establish a self-reflective evolution mechanism to facilitate each reasoning process generation in a single-forward inference. Extensive experiments demonstrate that, for knowledge-based complex tasks, EoT improves the factuality and fidelity of reasoning processes by approximately 16.5% and 48.8%, respectively, while enhancing LLM reasoning capability by about 6.2%, outperforming advanced approaches.
%R 10.18653/v1/2025.findings-emnlp.1297
%U https://aclanthology.org/2025.findings-emnlp.1297/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1297
%P 23864-23886
Markdown (Informal)
[EoT: Evolution of Thoughts for Complex Reasoning Tasks](https://aclanthology.org/2025.findings-emnlp.1297/) (Hua et al., Findings 2025)
ACL
- Qin Hua, Jiaqi Sun, Shiyou Qian, Dingyu Yang, Jian Cao, and Guangtao Xue. 2025. EoT: Evolution of Thoughts for Complex Reasoning Tasks. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 23864–23886, Suzhou, China. Association for Computational Linguistics.