Methods for Enhancing the Stability and Robustness of Chain-of-Thought Reasoning in Large Language Models

Abstract

Chain-of-Thought reasoning in large language models enhances the ability to solve complex tasks by generating intermediate steps; however, its stability and robustness face significant challenges. The reasoning process is susceptible to internal randomness, knowledge ambiguity, and error propagation, while existing architectures lack effective mechanisms for process constraint and regulation. This paper systematically analyzes the inherent vulnerabilities of such reasoning and proposes stability optimization methods, including structured iterative refinement, multi-granularity consistency constraints, and dynamic uncertainty regulation. It constructs a robustness enhancement framework encompassing semantic-logical decoupled representation, cross-domain pattern transfer, and meta-reasoning for anomaly recovery. The study forms a comprehensive enhancement methodology ranging from process control to system architecture, thereby providing support for building reliable reasoning systems.