PROBE turns runtime telemetry from failed software engineering agent runs into evidence-grounded diagnoses and actionable recovery guidance, achieving 65.37% diagnosis accuracy and 21.79% recovery rate on 257 cases.
Pace: Prompting and augmentation for calibrated confidence estimation with gpt-4 in cloud incident root cause anal- ysis
3 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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LLM fine-tuning, RAG, and hybrid methods can build a useful knowledge base from support tickets that serves as a good starting point for root cause analysis in networks.
TSGuard builds domain knowledge bases offline from historical incidents and applies online multi-agent structured reasoning to diagnose AI workload failures, delivering 19.8% higher accuracy and 63.4% lower verification time than baselines on Azure production data.
citing papers explorer
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Debugging the Debuggers: Failure-Anchored Structured Recovery for Software Engineering Agents
PROBE turns runtime telemetry from failed software engineering agent runs into evidence-grounded diagnoses and actionable recovery guidance, achieving 65.37% diagnosis accuracy and 21.79% recovery rate on 257 cases.
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LLM-Augmented Knowledge Base Construction For Root Cause Analysis
LLM fine-tuning, RAG, and hybrid methods can build a useful knowledge base from support tickets that serves as a good starting point for root cause analysis in networks.
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TSGuard: Automated User-Centric Incident Diagnosis for AI Workloads in the Cloud
TSGuard builds domain knowledge bases offline from historical incidents and applies online multi-agent structured reasoning to diagnose AI workload failures, delivering 19.8% higher accuracy and 63.4% lower verification time than baselines on Azure production data.