GSID applies an adaptive configuration encoder and inconsistency dynamic attention on bipartite graphs to detect protocol configuration anomalies, reporting threefold F1 improvement and 23.2% accuracy gain over baselines.
From large AI models to agentic AI: A tutorial on future intelligent communications
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Chain-of-thought reasoning with plan-based demonstrations and similarity retrieval improves LLM mobile traffic prediction accuracy by up to 15% over standard in-context learning on real 5G data.
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Enhancing Network Resilience via Graph-Based Anomaly Detection in Sovereign Functions
GSID applies an adaptive configuration encoder and inconsistency dynamic attention on bipartite graphs to detect protocol configuration anomalies, reporting threefold F1 improvement and 23.2% accuracy gain over baselines.
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Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction
Chain-of-thought reasoning with plan-based demonstrations and similarity retrieval improves LLM mobile traffic prediction accuracy by up to 15% over standard in-context learning on real 5G data.