SecMate用多智能体实现自适应网络安全故障排查，效果显著优于纯大模型。
SecMate: Multi-Agent Adaptive Cybersecurity Troubleshooting with Tri-Context Personalization
- 融合设备、用户、服务三重上下文，动态调整诊断策略。
- 设备级证据使正确解决率从50%提升至90%以上。
- 适合需要高效、低负担技术支持的场景，如企业IT运维。
大型语言模型与智能体框架的进步催生了复杂支持场景下的虚拟客户助手（VCAs）。本文提出SecMate，一种用于网络安全故障排查的多智能体虚拟客户助手，通过对话与设备级信号整合设备、用户和服务三方面的特异性。设备特异性由轻量级本地诊断工具提供，用户特异性依赖隐式熟练度推断与配置文件感知的排查策略，服务特异性则通过主动、上下文感知的推荐系统实现。在包含144名参与者和711次对话的受控研究中，设备级证据使正确解决率从约50%提升至90%以上，相较于仅使用大模型的基线；逐步指导提升了用户愉悦度并减轻了认知负担。推荐系统达到高相关性（MRR@1=0.75），参与者表现出强烈意愿以远低于人工成本的代价替代人类IT支持。我们公开了完整代码库与丰富标注数据集，以支持自适应虚拟客户助手的可复现研究。
原文摘要 · Abstract (English)
Recent advances in large language models and agentic frameworks have enabled virtual customer assistants (VCAs) for complex support. We present SecMate, a multi-agent VCA for cybersecurity troubleshooting that integrates device, user, and service specificity from conversational and device-level signals. Device specificity is provided by a lightweight local diagnostic utility, while user specificity relies on implicit proficiency inference and profile-aware troubleshooting. Service specificity is achieved through a proactive, context-aware recommender. We evaluate SecMate in a controlled study with 144 participants and 711 conversations. Device-level evidence increased correct resolutions from about 50% to over 90% relative to an LLM-only baseline, while step-by-step guidance improved pleasantness and reduced user burden. The recommender achieved high relevance (MRR@1=0.75), and participants showed strong willingness to substitute human IT support at costs well below human benchmarks. We release the full code base and a richly annotated dataset to support reproducible research on adaptive VCAs.
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