The field of digital twins and autonomous systems is rapidly advancing, with a focus on improving efficiency, accuracy, and scalability. Recent developments have seen the integration of digital twin technology with AI-powered scenario generation, enabling the systematic generation of realistic and diverse scenarios for testing and validation. This has significant implications for the safety and reliability of autonomous vehicles and other complex systems. Additionally, advancements in language-controlled editing and predictive path refinement are enabling more intuitive and flexible manipulation of digital environments. Notable papers in this area include SIMSplat, which provides detailed object-level editing and predictive path refinement, and RAP, which achieves state-of-the-art closed-loop robustness and long-tail generalization in end-to-end planning. ERUPT is also noteworthy, as it provides an open toolkit for interfacing with robot motion planners in extended reality, allowing for more immersive and interactive planning experiences.