PRAXIS combines LLM-driven structured traversal of service dependency graphs and hammock-block program dependence graphs to improve root-cause analysis accuracy by up to 6.3x while cutting token consumption by 5.3x on 30 real-world cloud incidents.
Real-time Event Detection on Social Data Streams , booktitle =
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TingIS uses multi-stage LLM event linking plus routing and filtering to extract high-priority incidents from noisy customer data at 2,000 messages per minute, delivering 3.5-minute P90 latency and 95% discovery in production.
citing papers explorer
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PRAXIS: Integrating Program Analysis with Observability for Root-Cause Analysis
PRAXIS combines LLM-driven structured traversal of service dependency graphs and hammock-block program dependence graphs to improve root-cause analysis accuracy by up to 6.3x while cutting token consumption by 5.3x on 30 real-world cloud incidents.
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TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale
TingIS uses multi-stage LLM event linking plus routing and filtering to extract high-priority incidents from noisy customer data at 2,000 messages per minute, delivering 3.5-minute P90 latency and 95% discovery in production.