Adversarial Network Imagination: Causal LLMs and Digital Twins for Proactive Telecom Mitigation

ArXi:2602.13203v2 Announce Type: replace-cross Telecommunication networks experience complex failures such as fiber cuts, traffic overloads, and cascading outages. Existing monitoring and digital twin systems are largely reactive, detecting failures only after service degradation occurs. We propose Adversarial Network Imagination, a closed-loop framework that integrates a Causal Large Language Model (LLM), a Knowledge Graph, and a Digital Twin to proactively generate, simulate, and evaluate adversarial network failures.