AI 中文总结
针对现有LLM自动化进化优化的局限，提出引入GraphRAG的OptGraph工作流，通过类型化图复用经验等方法，在基准数据集上较现有框架平均精确准确率提升8.9%。
AI 中文摘要
大语言模型（LLMs）已成为自动化进化优化的强大工具，但现有方法在模式复用、错误感知优化及跨任务检索鲁棒性方面仍存在局限。为解决这些问题，我们提出OptGraph，这是首个引入图检索增强生成（GraphRAG）的优化智能体工作流。具体而言，OptGraph首先将可复用经验构建为类型化图，捕捉建模模式、问题形式化、实现细节与错误修正之间的关系；在推理阶段，OptGraph利用图邻域信息丰富检索到的知识，提供结构化上下文以改进建模、验证与迭代优化。此外，OptGraph支持自适应知识更新，可将执行轨迹与验证反馈提炼为可复用的图知识，无需进行LLM参数调优。在基准数据集上的大量实验表明，所提出的OptGraph比现有最先进的基于提示的自动化优化框架平均精确准确率高8.9%，我们的代码已在该httpsURL公开。
英文摘要
Large language models (LLMs) have emerged as a powerful tool for automated evolutionary optimization, but existing methods remain limited in pattern reuse, error-aware refinement, and retrieval robustness across diverse tasks. To address these limitations, we propose OptGraph, the first optimization agentic workflow that introduces graph retrieval-augmented generation (GraphRAG). Specifically, OptGraph first constructs reusable experience as a typed graph, capturing the relationships among modeling patterns, problem formalization, implementation details, and error corrections. In the inference stage, OptGraph leverages graph neighborhood information to enrich retrieved knowledge, providing structured context to improve modeling, verification, and iterative refinement. Moreover, OptGraph supports adaptive knowledge updates, enabling the distillation of execution traces and verification feedback into reusable graph knowledge without ndertaking LLM parameter tuning. Extensive experiments on benchmark datasets show that our proposed OptGraph achieves an average exact accuracy 8.9% higher than the state-of-the-art prompt-based automated optimization frameworks. Our code has been made available at https://github.com/xianchaoxiu/OptGraph.
