arXiv:2606.00582v1 Announce Type: new Abstract: Network faults propagate layer by layer along topology and protocol dependencies, yet operations systems typically observe only symptomatic alerts at the tail end of propagation chains, where distinct root-cause faults may produce highly similar end-point symptoms. Existing approaches, whether rule-based, machine learning (ML)-based, or large language model (LLM)-based, fundamentally map the alert set to a diagnosis in a single pass and are structurally incapable of resolving this end-point ambiguity. This paper proposes PropLLM, which is the fir
This paper addresses a known limitation in current network fault diagnosis systems, which struggle with the ambiguity of end-point symptoms despite the increasing complexity of network topologies and dependencies.
Improving network fault diagnosis using AI, particularly LLMs, can significantly enhance operational efficiency, reduce downtime, and bolster the resilience of critical digital infrastructure.
Traditional, single-pass alert mapping methods will be augmented or replaced by more sophisticated, propagation-aware LLM approaches capable of disambiguating root causes in complex network systems.
- · IT Operations
- · Network Infrastructure Providers
- · AI/ML-Driven Observability Platforms
- · Manual Network Troubleshooting Teams
- · Legacy Rule-Based Diagnosis Systems
PropLLM improves the accuracy and speed of network fault identification.
Reduced network downtime leads to higher service availability and improved business continuity across all sectors reliant on digital infrastructure.
The success of propagation-aware AI for network diagnosis could inspire similar 'causal-aware' AI applications in other complex systems, such as supply chains or industrial control systems.
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Read at arXiv cs.AI