@inproceedings{ling-etal-2026-neural,
title = "Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models",
author = "Ling, Guoming and
Huang, Zhongzhan and
Lin, Yupei and
Li, Junxin and
Zhong, Shanshan and
Wu, Hefeng and
Lin, Liang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1149/",
pages = "22900--22933",
ISBN = "979-8-89176-395-1",
abstract = "Chain-of-Thought reasoning has significantly enhanced the problem-solving capabilities of Large Language Models. Unfortunately, current models generate reasoning steps sequentially without foresight, often becoming trapped in suboptimal reasoning paths with redundant steps. In contrast, we introduce Neural Chain-of-Thought Search (NCoTS), a framework that reformulates reasoning as a dynamic search for the optimal thinking strategy. By quantitatively characterizing the solution space, we reveal the existence of sparse superior reasoning paths that are simultaneously more accurate and concise than standard outputs. Our method actively navigates towards these paths by evaluating candidate reasoning operators using a dual-factor heuristic that optimizes for both correctness and computational cost. Consequently, NCoTS achieves a Pareto improvement across diverse reasoning benchmarks, boosting accuracy by over 3.5{\%} while reducing generation length by over 22{\%}. We will make our code and data publicly available."
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<abstract>Chain-of-Thought reasoning has significantly enhanced the problem-solving capabilities of Large Language Models. Unfortunately, current models generate reasoning steps sequentially without foresight, often becoming trapped in suboptimal reasoning paths with redundant steps. In contrast, we introduce Neural Chain-of-Thought Search (NCoTS), a framework that reformulates reasoning as a dynamic search for the optimal thinking strategy. By quantitatively characterizing the solution space, we reveal the existence of sparse superior reasoning paths that are simultaneously more accurate and concise than standard outputs. Our method actively navigates towards these paths by evaluating candidate reasoning operators using a dual-factor heuristic that optimizes for both correctness and computational cost. Consequently, NCoTS achieves a Pareto improvement across diverse reasoning benchmarks, boosting accuracy by over 3.5% while reducing generation length by over 22%. We will make our code and data publicly available.</abstract>
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%0 Conference Proceedings
%T Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models
%A Ling, Guoming
%A Huang, Zhongzhan
%A Lin, Yupei
%A Li, Junxin
%A Zhong, Shanshan
%A Wu, Hefeng
%A Lin, Liang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F ling-etal-2026-neural
%X Chain-of-Thought reasoning has significantly enhanced the problem-solving capabilities of Large Language Models. Unfortunately, current models generate reasoning steps sequentially without foresight, often becoming trapped in suboptimal reasoning paths with redundant steps. In contrast, we introduce Neural Chain-of-Thought Search (NCoTS), a framework that reformulates reasoning as a dynamic search for the optimal thinking strategy. By quantitatively characterizing the solution space, we reveal the existence of sparse superior reasoning paths that are simultaneously more accurate and concise than standard outputs. Our method actively navigates towards these paths by evaluating candidate reasoning operators using a dual-factor heuristic that optimizes for both correctness and computational cost. Consequently, NCoTS achieves a Pareto improvement across diverse reasoning benchmarks, boosting accuracy by over 3.5% while reducing generation length by over 22%. We will make our code and data publicly available.
%U https://aclanthology.org/2026.findings-acl.1149/
%P 22900-22933
Markdown (Informal)
[Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models](https://aclanthology.org/2026.findings-acl.1149/) (Ling et al., Findings 2026)
ACL
- Guoming Ling, Zhongzhan Huang, Yupei Lin, Junxin Li, Shanshan Zhong, Hefeng Wu, and Liang Lin. 2026. Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 22900–22933, San Diego, California, United States. Association for Computational Linguistics.