arXiv:2604.23270v2 Announce Type: replace Abstract: 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. To address this gap, we propose CAP-CoT, a Cycle Adversarial Prompt optimization framework designed to improve both CoT reas
The rapid advancement and deployment of generative AI models necessitate continuous improvements in their reasoning capabilities to expand real-world applications.
Improving the stability and effectiveness of Chain-of-Thought prompting directly impacts the reliability and trustworthiness of LLMs in complex reasoning tasks, which is crucial for their integration into critical workflows.
This research introduces an iterative, adversarial optimization framework for CoT, moving beyond single-pass improvements to enhance consistency and accuracy in LLM reasoning.
- · AI developers
- · Enterprises adopting LLMs for complex tasks
- · AI research institutions
- · LLM applications requiring high reasoning stability without advanced prompting
- · Methods focused solely on single-pass CoT optimization
LLMs exhibit more reliable and less 'unstable' reasoning over multi-step problems.
Increased enterprise and critical sector adoption of LLMs due to enhanced trustworthiness and reduced error rates.
Acceleration in the development of fully autonomous AI agents capable of handling highly complex, multi-step tasks independently.
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Read at arXiv cs.AI