用多智能体LLM分析洛杉矶山火期间空气质量，生成健康建议。
Instructor-Worker Large Language Model System for Policy Recommendation: a Case Study on Air Quality Analysis of the January 2025 Los Angeles Wildfires
- 设计导师-工人架构的LLM系统，分工完成数据检索与分析。
- 基于空气质量数据提出健康建议，验证了政策推荐能力。
- 适合关注环境治理与AI决策的科研人员和政策制定者。
2025年1月洛杉矶山火造成超过2500亿美元损失，持续近一个月才被控制。在先前数字孪生建筑工作基础上，我们改进并利用多智能体大语言模型框架及云地图集成，研究山火期间的空气质量。大语言模型的进步使大规模数据分析实现开箱即用。本研究采用由导师智能体与工人智能体组成的多智能体系统：用户指令下达后，导师智能体从云端平台获取数据，并生成任务提示分发给工人智能体；工人智能体完成数据分析并输出摘要，最终汇总回导师智能体进行综合研判，形成最终分析结论。通过评估该系统在洛杉矶山火期间基于空气质量提出的健康建议，验证其数据驱动的政策推荐能力。
原文摘要 · Abstract (English)
The Los Angeles wildfires of January 2025 caused more than 250 billion dollars in damage and lasted for nearly an entire month before containment. Following our previous work, the Digital Twin Building, we modify and leverage the multi-agent large language model framework as well as the cloud-mapping integration to study the air quality during the Los Angeles wildfires. Recent advances in large language models have allowed for out-of-the-box automated large-scale data analysis. We use a multi-agent large language system comprised of an Instructor agent and Worker agents. Upon receiving the users' instructions, the Instructor agent retrieves the data from the cloud platform and produces instruction prompts to the Worker agents. The Worker agents then analyze the data and provide summaries. The summaries are finally input back into the Instructor agent, which then provides the final data analysis. We test this system's capability for data-based policy recommendation by assessing our Instructor-Worker LLM system's health recommendations based on air quality during the Los Angeles wildfires.
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