一句话：提出融合提交历史模式挖掘、动态反馈迭代与历史修复经验检索的LLM代码修复框架，使补丁生成更接近人类迭代式推理。
大语言模型在自动化代码修复上展现出潜力，但往往缺乏人类开发者那种细致、迭代式的推理，也难以有效利用历史上下文。本文提出一个增强LLM修复能力的框架，引入类人机制：从提交历史中挖掘反复出现的修复模式并抽取抽象修复模式；采用动态反馈回路，利用测试与静态分析的自动反馈，并可选地结合人工指导来迭代精化补丁；同时构建向量化的历史修复经验库，支持上下文感知的推理。该框架旨在以更接近人类、更具迭代性且扎根于上下文的问题解决策略来引导LLM，从而提升自动补丁生成的准确性与效率。摘要未报告具体实验评估结果，属于框架性提案。
Large Language Models (LLMs) show promise for automating code repair but often lack the nuanced, iterative reasoning and effective use of historical context employed by human developers. We propose a framework to enhance LLM-based repair by incorporating human-inspired mechanisms: mining commit histories for recurring patterns, employing dynamic feedback loops, and facilitating reasoning over historical repair experiences. By extracting abstract fix patterns, iteratively refining patches using automated feedback (from tests and static analysis) alongside optional human guidance, and leveraging a vectorized repository of past experiences for context-aware reasoning, our approach seeks to improve automated patch generation. This framework aims to increase the accuracy and efficiency of repairs by guiding LLMs with more human-like, iterative, and context-grounded problem-solving strategies.