CAP-CoT: Cycle Adversarial Prompt for Improving Chain of Thoughts in LLM Reasoning

ArXi:2604.23270v1 Announce Type: new Chain-of-Thought (CoT) prompting has emerged as a simple and effective way to elicit step-by-step solutions from large language models (LLMs). However, CoT reasoning can be unstable across runs on long, multi-step problems, leading to inconsistent answers for unchanged task. Most prior work focuses on improving the forward reasoning chain within a single pass, with less attention to iterative and contrastive correction.