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

ACL ARR 2026 January Submission6726 Authors

Keywords: causal language models, mitigation, constrained generation, knowledge graphs, multi-hop reasoning, digital twins, telecommunications, adversarial

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.

Paper Type: Long

Research Area: AI/LLM Agents

Research Area Keywords: Explainability, Causal reasoning, Large language models, Structured generation, Model analysis, Simulation-based evaluation, Safety-critical systems

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 6726