5 papers
GALA: Graph-Augmented LLM Agents for Root Cause Analysis and Incident Response in Microservices
Yifang Tian, Yaming Liu, Zichun Chong +3
Microservice root cause analysis (RCA) requires correlating failures across heterogeneous telemetry within complex service dependency graphs. Existing methods often rely on a singl…
FAME: Failure-Aware Mixture-of-Experts for Message-Level Log Anomaly Detection
Huanchi Wang, Zihang Huang, Yifang Tian +3
Production systems generate millions of log lines daily, yet most anomaly detectors operate at the session or window-level, flagging groups of lines rather than identifying the spe…
SREGym: A Live Benchmark for AI SRE Agents with High-Fidelity Failure Scenarios
Jackson Clark, Yiming Su, Saad Mohammad Rafid Pial +5
AI agents are increasingly used to diagnose and mitigate failures in production systems, known as agentic Site Reliability Engineering (SRE). Current SRE benchmarks are limited to…
GALA: Can Graph-Augmented Large Language Model Agentic Workflows Elevate Root Cause Analysis?
Yifang Tian, Yaming Liu, Zichun Chong +2
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…
RAG-Verus: Repository-Level Program Verification with LLMs using Retrieval Augmented Generation
Sicheng Zhong, Jiading Zhu, Yifang Tian +1
Scaling automated formal verification to real-world projects requires resolving cross-module dependencies and global contexts, which are challenges overlooked by existing function-…