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2026 · Conference paper · Published

Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models

Guoming Ling, Zhongzhan Huang, Yupei Lin, Junxin Li, Shanshan Zhong, Hefeng Wu, Liang Lin
Findings of the Association for Computational Linguistics: ACL 2026
Figure 1 — Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models
Figure 1: Motivation and Overview of our NCoTS. (a) Planning Bottleneck in Traditional CoT. (b) Importance of Path Planning. Sparse guiding tokens from a strong teacher significantly boost performance, confirming that path planning is the key bottleneck. (c) The NCoTS framework. Our method reformulates reasoning as a search process, employing a dual-factor heuristic to ac- tively discover paths that are both accurate and concise.
Overview

Reformulates reasoning as a dynamic search over thinking strategies. Candidate reasoning operators are evaluated with a dual-factor heuristic that balances correctness and computational cost.