AI RESEARCH
SADE: Symptom-Aware Diagnostic Escalation for LLM-Based Network Troubleshooting
arXiv CS.AI
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ArXi:2605.04530v1 Announce Type: cross Large language model (LLM) agents are increasingly applied to network troubleshooting, but root-cause localization on public benchmarks remains well below practical deployment thresholds. We argue this is because existing agents do not encode the disciplined, layer-by-layer methodology that human network engineers use, and instead rely on free-form deliberation that conflates evidence acquisition with hypothesis commitment.