The field of digital twins is rapidly advancing, with a focus on improving the reliability and performance of wireless networks. Current research is exploring the use of digital twins to optimize network quality of service under adverse conditions, such as denial-of-service attacks. Additionally, there is a growing interest in developing trustworthy digital twins that can accurately represent physical networks, enabling more effective training and deployment of AI models. Digital twin technology is also being applied in other domains, including healthcare, where it has the potential to transform patient care through personalized simulations and predictive modeling. Furthermore, researchers are investigating the use of digital twins to enhance security in IoT networks and improve the efficiency of AI models in telecommunications. Noteworthy papers include: A Digital Twin Platform for QoS Optimization Under DoS Attacks for Next Generation Radio Networks, which presents a novel platform for enhancing network quality of service under attack. Toward Trustworthy Digital Twins in Agentic AI-based Wireless Network Optimization: Challenges, Solutions, and Opportunities, which introduces a unified evaluation framework for ensuring trustworthy digital twins. Digital Twin-Driven Secure Access Strategy for SAGIN-Enabled IoT Networks, which proposes a digital twin-driven approach for secure access in IoT networks. Through the telecom lens: Are all training samples important?, which questions the assumption that all training samples contribute equally to AI model performance.