用多模态检索增强大模型诊断云平台故障，效率更高
Diagnosing and Resolving Cloud Platform Instability with Multi-modal RAG LLMs
- 结合AI模式识别与多模态RAG接口，自动定位故障根源
- 实测性能优于现有最优方案，可快速缩小排查范围
- 适合运维工程师和云平台开发者快速诊断系统异常
当前托管在云上的应用和服务是复杂的系统，性能或功能不稳定可能有数十甚至上百个潜在原因。我们的假设是，通过将现代AI工具的模式匹配能力与自然的多模态RAG大模型接口相结合，可以简化问题识别与修复过程。ARCA是一个针对该领域的新型多模态RAG大模型系统。分步评估表明，ARCA的表现优于现有最先进方案。
原文摘要 · Abstract (English)
Today's cloud-hosted applications and services are complex systems, and a performance or functional instability can have dozens or hundreds of potential root causes. Our hypothesis is that by combining the pattern matching capabilities of modern AI tools with a natural multi-modal RAG LLM interface, problem identification and resolution can be simplified. ARCA is a new multi-modal RAG LLM system that targets this domain. Step-wise evaluations show that ARCA outperforms state-of-the-art alternatives.
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