Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.11340 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914473091006464 |
|---|---|
| author | Ling, Guoming Huang, Zhongzhan Lin, Yupei Li, Junxin Zhong, Shanshan Wu, Hefeng Lin, Liang |
| author_facet | Ling, Guoming Huang, Zhongzhan Lin, Yupei Li, Junxin Zhong, Shanshan Wu, Hefeng Lin, Liang |
| contents | 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%. Our code and data are available at https://github.com/MilkThink-Lab/Neural-CoT-Search. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_11340 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models Ling, Guoming Huang, Zhongzhan Lin, Yupei Li, Junxin Zhong, Shanshan Wu, Hefeng Lin, Liang Computation and Language 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%. Our code and data are available at https://github.com/MilkThink-Lab/Neural-CoT-Search. |
| title | Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.11340 |