用自然语言生成异常检测代码，让非专业人士也能轻松使用。
AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection
- 通过多个智能体协作，将自然语言指令转化为可执行的检测流程。
- 在多个数据集上生成可靠脚本，推荐的模型性能媲美专业方法。
- 适合没有编程经验的研究人员或工业用户快速部署异常检测。
异常检测在欺诈识别、网络监控和科研等领域至关重要。然而，数据模态多样及专用库数量增多，使缺乏专业知识和编程技能的用户难以使用。为此，我们提出AD-AGENT——一个基于大模型的多智能体框架，可将自然语言指令自动转换为完整的异常检测流水线。该框架协调多个专业化智能体，完成意图解析、数据准备、库与模型选择、文档挖掘以及迭代式代码生成与调试。通过共享短期工作区与长期缓存，集成PyOD、PyGOD和TSLib等主流库，实现统一工作流。实验表明，AD-AGENT能生成可靠脚本，并在多个数据集上推荐出性能竞争力强的模型。系统已开源，以支持后续研究与实际应用。
原文摘要 · Abstract (English)
Anomaly detection (AD) is essential in areas such as fraud detection, network monitoring, and scientific research. However, the diversity of data modalities and the increasing number of specialized AD libraries pose challenges for non-expert users who lack in-depth library-specific knowledge and advanced programming skills. To tackle this, we present AD-AGENT, an LLM-driven multi-agent framework that turns natural-language instructions into fully executable AD pipelines. AD-AGENT coordinates specialized agents for intent parsing, data preparation, library and model selection, documentation mining, and iterative code generation and debugging. Using a shared short-term workspace and a long-term cache, the agents integrate popular AD libraries like PyOD, PyGOD, and TSLib into a unified workflow. Experiments demonstrate that AD-AGENT produces reliable scripts and recommends competitive models across libraries. The system is open-sourced to support further research and practical applications in AD.
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