![[2602.13203] Adversarial Network Imagination: Causal LLMs and Digital Twins for Proactive Telecom Mitigation](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.13203] Adversarial Network Imagination: Causal LLMs and Digital Twins for Proactive Telecom Mitigation
Summary
This article presents a novel framework called Adversarial Network Imagination, which leverages Causal Large Language Models and Digital Twins to proactively address telecom network failures, enhancing resilience and operational efficiency.
Why It Matters
Telecommunication networks face increasing challenges from complex failures. This framework shifts from reactive to proactive strategies, potentially reducing downtime and improving service reliability. It highlights the integration of advanced AI technologies in critical infrastructure management, which is essential for future-proofing telecom operations.
Key Takeaways
- Introduces a proactive framework for telecom failure management.
- Integrates Causal LLMs and Digital Twins for scenario simulation.
- Shifts network operations from reactive troubleshooting to anticipatory resilience.
- Utilizes a Knowledge Graph to ground failure scenarios in network dependencies.
- Aims to enhance operational efficiency and reduce service degradation.
Computer Science > Networking and Internet Architecture arXiv:2602.13203 (cs) [Submitted on 9 Jan 2026] Title:Adversarial Network Imagination: Causal LLMs and Digital Twins for Proactive Telecom Mitigation Authors:Vignesh Sriram, Yuqiao Meng, Luoxi Tang, Zhaohan Xi View a PDF of the paper titled Adversarial Network Imagination: Causal LLMs and Digital Twins for Proactive Telecom Mitigation, by Vignesh Sriram and 3 other authors View PDF HTML (experimental) Abstract: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. Subjects: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI) Cite as: arXiv:2602.13203 [cs.NI] (or arXiv...
