The field of network anomaly detection and explainability is witnessing significant advancements with the integration of Large Language Models (LLMs) and innovative architectures. Researchers are exploring the potential of LLMs to improve the accuracy and interpretability of anomaly detection systems, enabling proactive network monitoring and security. The use of generative models, attention mechanisms, and sequential learning is also being investigated to enhance the detection of black hole anomalies and other types of network disruptions. Furthermore, the application of LLMs in root cause analysis and incident response is showing promise in reducing the time and effort required to identify and recover from network incidents. Noteworthy papers in this area include: WBHT, a generative attention architecture that achieves significant improvements in black hole anomaly detection. Interpretable Anomaly-Based DDoS Detection in AI-RAN, a framework that leverages LLMs and explainable AI to detect DDoS attacks in 5G networks. Reasoning Language Models for Root Cause Analysis, a lightweight framework that uses LLMs to improve the accuracy and reasoning quality of root cause analysis in 5G wireless networks. Large Language Model-Based Framework for Explainable Cyberattack Detection, a hybrid framework that integrates ML-based attack detection with LLM-generated explanations. OFCnetLLM, a large language model for network monitoring and alertness that enhances anomaly detection and automates root-cause analysis. The Multi-Agent Fault Localization System, an innovative LLM multi-agent system that improves root cause localization accuracy and mitigates hallucinations.