用图结构RAG和多智能体协作，自动构建传感器信号处理模型。
Leveraging LLM Agents for Automated Optimization Modeling for SASP Problems: A Graph-RAG based Approach
- 设计多智能体结构模拟人工建模流程
- 图RAG精准匹配查询与领域知识，提升建模效果
- 在10个经典问题上超越现有自动化建模方法
自动化优化建模（AOM）随着大语言模型（LLMs）的快速发展受到广泛关注。现有方法主要依赖提示工程，采用精心设计的专家响应链或结构化引导，但在传感器阵列信号处理（SASP）领域因缺乏特定领域知识而表现不佳。为解决此问题，我们提出一种基于检索增强生成（RAG）的技术，包含两个核心组件：多智能体（MA）结构和基于图的RAG（Graph-RAG）流程。MA结构针对AOM架构设计，每个智能体依据人类建模流程原则构建；Graph-RAG流程用于将用户查询与特定SASP建模知识匹配，从而提升建模结果。在10个经典信号处理问题上的实验表明，所提方法（命名为MAG-RAG）优于多个AOM基准方法。
原文摘要 · Abstract (English)
Automated optimization modeling (AOM) has evoked considerable interest with the rapid evolution of large language models (LLMs). Existing approaches predominantly rely on prompt engineering, utilizing meticulously designed expert response chains or structured guidance. However, prompt-based techniques have failed to perform well in the sensor array signal processing (SASP) area due the lack of specific domain knowledge. To address this issue, we propose an automated modeling approach based on retrieval-augmented generation (RAG) technique, which consists of two principal components: a multi-agent (MA) structure and a graph-based RAG (Graph-RAG) process. The MA structure is tailored for the architectural AOM process, with each agent being designed based on principles of human modeling procedure. The Graph-RAG process serves to match user query with specific SASP modeling knowledge, thereby enhancing the modeling result. Results on ten classical signal processing problems demonstrate that the proposed approach (termed as MAG-RAG) outperforms several AOM benchmarks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。