一句话：提出用强化学习与认知智能体在反馈闭环中自主优化提示，以提升 LLM 程序修复的稳定性与正确性。
基于大语言模型的自动程序修复虽已改变软件工程流程，却普遍存在行为不稳定、泛化能力有限、对提示高度敏感等问题。本文是一篇研究提案，提出将提示设计视为自适应优化问题，融合强化学习、认知智能体与自自适应架构，在持续反馈闭环中自主精炼提示；同时让智能体维护从代码仓库与执行轨迹中抽取的持久信念，使修复决策有据可依。作者假设这一路线能显著提升修复的稳定性与正确性，并让修复过程更可解释、更有效。摘要仅给出研究动机、假设与方向，尚未报告具体实现与实验评估结果。
Recent advances in Large Language Models (LLMs) have significantly reshaped software engineering workflows, particularly in Automated Program Repair (APR). Despite their potential, LLM-based repair techniques often exhibit unstable behavior, limited generalization and strong prompt sensitivity. This proposal investigates a research direction that unifies Reinforcement Learning (RL), cognitive agents and self-adaptive architectures to autonomously optimize prompts for LLM-based patch generation. We hypothesize that treating prompt design as an adaptive optimization problem can significantly improve stability and correctness. By embedding prompt refinement in a continuous feedback loop, and enabling agents to maintain persistent beliefs extracted from repositories and execution traces, the repair process becomes more stable, explainable and effective.