Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation, and their integration with Extended Reality (XR) is poised to transform how users interact with immersive environments. This survey provides a comprehensive review of recent developments at the intersection of LLMs and XR, offering a structured organization of research along both technical and application dimensions. We propose a taxonomy of LLM-enhanced XR systems centered on key technical paradigms -- such as interactive agent control, XR development toolkits, and generative scene synthesis -- and discuss how these paradigms enable novel capabilities in XR. In parallel, we examine how LLM-driven techniques support practical XR applications across diverse domains, including immersive education, clinical healthcare, and industrial manufacturing. By connecting these technical paradigms with application frontiers, our survey highlights current trends, delineates design considerations, and identifies open challenges in building LLM-augmented XR systems. This work provides insights that can guide researchers and practitioners in advancing the state of the art in intelligent XR experiences.

翻译：大语言模型（LLMs）在自然语言理解与生成方面展现出卓越能力，其与扩展现实（XR）的融合有望彻底改变用户与沉浸式环境的交互方式。本综述全面回顾了LLMs与XR交叉领域的最新进展，从技术和应用两个维度对研究进行了结构化梳理。我们提出了一种以关键技术范式为中心的LLM增强型XR系统分类体系——包括交互式智能体控制、XR开发工具包以及生成式场景合成等——并探讨了这些范式如何赋能XR实现新颖功能。同时，我们分析了LLM驱动技术如何支持跨领域实际XR应用，涵盖沉浸式教育、临床医疗和工业制造等领域。通过将技术范式与应用前沿相结合，本综述揭示了当前发展趋势，阐明了系统设计考量，并指出了构建LLM增强型XR系统所面临的开放挑战。本工作为研究者和实践者推动智能XR体验的前沿发展提供了指导性见解。