CAP-CoT: Cycle Adversarial Prompt Improves LLM Reasoning Stability
The recently introduced CAP-CoT (Cycle Adversarial Prompt for Chain-of-Thought) framework seeks to improve the precision and reliability of reasoning in large language models (LLMs). Traditional Chain-of-Thought prompting, which encourages detailed, sequential solutions, frequently produces variable outcomes on complex, multi-step tasks. CAP-CoT features a cyclical process: a forward solver generates potential reasoning chains, an adversarial challenger devises believable yet erroneous chains through targeted mistakes, and a feedback agent evaluates both chains to deliver structured, step-aligned feedback. This feedback effectively closes the optimization loop in both directions, enhancing the performance of the deployed solver. This method addresses limitations in earlier research that concentrated on single-pass reasoning without iterative adjustments. The paper can be found on arXiv with ID 2604.23270.
Key facts
- CAP-CoT is a Cycle Adversarial Prompt optimization framework.
- It improves Chain-of-Thought reasoning accuracy and stability.
- The framework uses a forward solver, adversarial challenger, and feedback agent.
- The adversarial challenger constructs deliberately flawed reasoning chains.
- Feedback is step-aligned and structured.
- The optimization loop is bidirectional.
- Prior work focused on single-pass forward reasoning.
- Paper available at arXiv:2604.23270.
Entities
Institutions
- arXiv