PropLLM: Propagation-Aware Scene Reconstruction for Network Fault Diagnosis
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 first to integrate the hop-by-hop scene reconstruction paradigm with the generative reasoning capabilities of LLMs. Starting from end-point alerts, ...
| Authors | Zongzong Wu, Ming Zhao, Fengxiao Tang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 May 2026 |
| Last updated at source | 30 May 2026 |
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