PestMA：基于大语言模型（LLM）的多智能体系统用于知情害虫管理
PestMA: LLM-based Multi-Agent System for Informed Pest Management
AI总结：
针对现有单智能体害虫管理方法的局限，提出基于LLM的多智能体系统PestMA，通过编辑器、检索器、验证器三个专业智能体协作生成循证建议，验证后决策准确率达92.6%，提升害虫管理的可靠性与有效性。
摘要：
有效的害虫管理因需要准确、特定情境的决策而具有复杂性。大型语言模型（LLMs）的最新进展通过提供复杂、自适应的知识获取和推理能力，为应对这些挑战开辟了新可能。然而，现有基于LLM的害虫管理方法常依赖单智能体范式，这可能限制其整合多样化外部信息、进行系统验证以及处理复杂阈值驱动决策的能力。为克服这些局限，我们引入PestMA——一个基于LLM的多智能体系统（MAS），旨在生成可靠且循证的害虫管理建议。基于编辑范式，PestMA包含三个专业智能体：负责合成害虫管理建议的编辑器、收集相关外部数据的检索器，以及确保正确性的验证器。在真实世界害虫场景中的评估表明，PestMA的害虫管理决策初始准确率为86.8%，验证后提升至92.6%。这些结果强调了基于协作智能体的工作流在优化和验证决策中的价值，突显了基于LLM的多智能体系统在自动化和增强害虫管理流程方面的潜力。
英文摘要：
Effective pest management is complex due to the need for accurate, context-specific decisions. Recent advancements in large language models (LLMs) open new possibilities for addressing these challenges by providing sophisticated, adaptive knowledge acquisition and reasoning. However, existing LLM-based pest management approaches often rely on a single-agent paradigm, which can limit their capacity to incorporate diverse external information, engage in systematic validation, and address complex, threshold-driven decisions. To overcome these limitations, we introduce PestMA, an LLM-based multi-agent system (MAS) designed to generate reliable and evidence-based pest management advice. Building on an editorial paradigm, PestMA features three specialized agents, an Editor for synthesizing pest management recommendations, a Retriever for gathering relevant external data, and a Validator for ensuring correctness. Evaluations on real-world pest scenarios demonstrate that PestMA achieves an initial accuracy of 86.8% for pest management decisions, which increases to 92.6% after validation. These results underscore the value of collaborative agent-based workflows in refining and validating decisions, highlighting the potential of LLM-based multi-agent systems to automate and enhance pest management processes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
