Extended reality (XR) technologies, including virtual reality (VR) and augmented reality (AR), are transforming how people interact with the world by merging virtual content with the physical world and creating immersive, interactive experiences. To reach their full potential, next-generation XR systems demand a high degree of context awareness, that is, a detailed understanding of both user behaviors and surrounding environmental conditions. With enhanced context awareness, XR systems can deliver virtual content that is personalized, timely, and highly relevant, adapting to user interactions and responding to changes in the surrounding environments. To achieve this goal, this project builds a new class of retrieval-augmented generation (RAG)-empowered XR systems that bring together the power of large language models (LLMs) and localized, context-rich knowledge databases to make XR systems more intelligent and adaptive. The project builds and maintains an accurate, up-to-date, and diverse knowledge database, which integrates diverse sources of contextual information, such as 3D object data, egocentric images, text inputs, and user-specific data like head pose, eye gaze, and user preferences. The project also makes context-aware XR more resource-efficient and low-latency, by strategically leveraging the collaboration of XR devices with nearby edge servers to process complex and dynamic contextual inputs. This project will lay the foundation for context-aware XR applications ac