arXiv:2510.06995v2 Announce Type: replace-cross Abstract: We study the propagation of outliers in cyclic causal graphs with linear structural equations, tracing them back to one or several "root cause" nodes. We show that it is possible to identify a short list of potential root causes provided that the perturbation is sufficiently strong and propagates according to the same structural equations as in the normal mode. This shortlist consists of the true root causes together with those of its parents lying on a cycle with the root cause. Notably, our method does not require prior knowledge of t
The increasing complexity of AI systems and their integration into critical infrastructure necessitates robust methods for identifying the root causes of anomalies and ensuring reliability.
This research provides a foundational step towards building explainable and reliable autonomous AI systems by improving anomaly detection and diagnostics in complex networks.
The ability to accurately trace outliers to their root causes, even in unknown cyclic graphs, moves us closer to AI systems that can self-diagnose and potentially self-correct.
- · AI developers
- · Automation industries
- · AI safety researchers
- · Cybersecurity
- · Systems with opaque anomaly handling
- · Malicious actors relying on undetected system vulnerabilities
Improved debugging and reliability for machine learning models and complex AI systems.
Accelerated development of verifiable and robust autonomous agents with enhanced diagnostic capabilities.
Reduced risk of AI failures in critical applications, fostering greater public and institutional trust in advanced AI deployments.
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Read at arXiv cs.LG