Adversarial Network Imagination: Causal LLMs and Digital Twins for Proactive Telecom Mitigation
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913157642977280 |
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| author | Sriram, Vignesh Meng, Yuqiao Tang, Luoxi Xi, Zhaohan |
| author_facet | Sriram, Vignesh Meng, Yuqiao Tang, Luoxi Xi, Zhaohan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_13203 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adversarial Network Imagination: Causal LLMs and Digital Twins for Proactive Telecom Mitigation Sriram, Vignesh Meng, Yuqiao Tang, Luoxi Xi, Zhaohan Networking and Internet Architecture Artificial Intelligence 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. |
| title | Adversarial Network Imagination: Causal LLMs and Digital Twins for Proactive Telecom Mitigation |
| topic | Networking and Internet Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2602.13203 |