arXiv:2605.08786v3 Announce Type: replace Abstract: Root cause analysis (RCA) in complex systems is challenging due to error propagation across multiple variables, the need for structural causal knowledge, and the computational cost of inference at test time. We introduce PRIM (Prior-fitted Root cause Identification with Meta-learning), a causal meta-learning approach that frames RCA as a Bayesian inference task over a synthetic prior of causal models. By marginalising out structural uncertainty, PRIM implicitly identifies changes in the data-generating mechanism between baseline and anomalous
The increasing complexity of AI systems and the growing necessity for robust troubleshooting in production environments necessitate more sophisticated root cause analysis techniques.
This meta-learned Bayesian approach promises to significantly reduce the computational cost and improve the accuracy of identifying faults in complex AI and other systems, enabling faster anomaly detection and resolution.
Traditional root cause analysis methods, often reliant on explicit causal models or extensive manual debugging, will be augmented or replaced by more efficient, data-driven, and adaptive approaches like PRIM.
- · AI developers
- · Cloud infrastructure providers
- · Large-scale system operators
- · Automation software vendors
- · Manual debugging services
- · Legacy RCA tool vendors
Faster and more reliable identification of operational issues in complex software and hardware systems.
Reduced downtime and operational costs for AI-driven services and mission-critical infrastructure.
Acceleration of autonomous system development and deployment, as self-diagnosis and recovery become more efficient.
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Read at arXiv cs.LG