arXiv:2606.27154v1 Announce Type: new Abstract: Root cause analysis (RCA) poses a holistic test of LLM agentic capabilities, such as long-context understanding, multi-step reasoning, and tool use. However, existing datasets suffer from a fundamental gap: they label only the root cause, not the propagation path connecting it to the observed symptom, which largely simplifies the task to naive pattern matching. To support rigorous evaluation, we introduce PAVE, a step-wise labeling protocol that leverages known interventions from fault injection to reconstruct causal propagation paths. The mechan
The rapid advancement of LLMs necessitates more sophisticated evaluation methods to unlock their full potential in complex tasks like root cause analysis.
Improved root cause analysis capabilities in AI agents can significantly enhance enterprise resilience, operational efficiency, and system reliability across various sectors.
The introduction of PAVE establishes a new, more rigorous standard for evaluating agentic LLM capabilities, moving beyond simplistic outcome labeling to detailed causal path reconstruction.
- · AI Agent developers
- · Cloud infrastructure providers
- · Enterprise IT departments
- · Complex system operators
- · Companies relying on naive pattern matching solutions
- · Legacy monitoring systems
More robust and reliable AI agents will emerge for critical operational functions.
Enterprises will integrate advanced AI-driven root cause analysis, leading to fewer outages and faster problem resolution.
The development of highly autonomous, self-healing systems will accelerate, transforming operational paradigms across industries.
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Read at arXiv cs.AI