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. The Causal LLM produces structured failure scenarios grounded in network dependencies encoded in the Knowledge Graph. These scenarios are executed within a Digital Twin to measure performance degradation and evaluate mitigation strategies. By iteratively refining scenarios based on simulation feedback, the framework shifts network operations from reactive troubleshooting toward anticipatory resilience analysis.

翻译：电信网络会遭遇光纤中断、流量过载及级联故障等复杂失效情形。现有监测与数字孪生系统多呈被动响应特性，仅在服务降级发生后才能检测故障。我们提出"对抗式网络想象"框架——一种融合因果大语言模型、知识图谱与数字孪生的闭环系统，可主动生成、模拟并评估对抗性网络失效场景。因果大语言模型基于知识图谱编码的网络依赖关系生成结构化故障场景，这些场景在数字孪生环境中执行以量化性能降级程度并验证缓解策略。通过依据仿真反馈迭代优化场景，该框架将网络运维从被动故障排查范式转向预期性韧性分析范式。