用图增强大模型提升微服务故障诊断，给出可操作修复建议。
GALA: Can Graph-Augmented Large Language Model Agentic Workflows Elevate Root Cause Analysis?
- 结合因果推断与大模型迭代推理，多模态分析日志、指标和链路数据。
- 在开源基准上准确率提升42.22%，显著优于现有方法。
- 输出结果更符合因果逻辑且便于工程师理解，适合运维场景使用。
微服务系统中的根因分析（RCA）极具挑战性，需值班工程师快速诊断跨异构遥测数据（如指标、日志、链路）的故障。传统方法通常仅关注单一模态或仅排序可疑服务，难以提供可操作的诊断结论和修复指引。本文提出GALA，一种新颖的多模态框架，融合统计因果推断与大模型驱动的迭代推理，以增强根因分析能力。在开源基准上的评估显示，GALA相较于最先进方法最高提升42.22%的准确率。我们提出的新型人工引导式大模型评估分数表明，GALA生成的诊断输出在因果合理性与可操作性上显著优于现有方法。通过全面实验与案例研究，证明GALA能弥合自动化故障诊断与实际事件处理之间的鸿沟，既精准识别根因，又提供人类可理解的修复建议。
原文摘要 · Abstract (English)
Root cause analysis (RCA) in microservice systems is challenging, requiring on-call engineers to rapidly diagnose failures across heterogeneous telemetry such as metrics, logs, and traces. Traditional RCA methods often focus on single modalities or merely rank suspect services, falling short of providing actionable diagnostic insights with remediation guidance. This paper introduces GALA, a novel multi-modal framework that combines statistical causal inference with LLM-driven iterative reasoning for enhanced RCA. Evaluated on an open-source benchmark, GALA achieves substantial improvements over state-of-the-art methods of up to 42.22% accuracy. Our novel human-guided LLM evaluation score shows GALA generates significantly more causally sound and actionable diagnostic outputs than existing methods. Through comprehensive experiments and a case study, we show that GALA bridges the gap between automated failure diagnosis and practical incident resolution by providing both accurate root cause identification and human-interpretable remediation guidance.
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