The development of large language models (LLMs) has given rise to four major application paradigms: LLM app stores, LLM agents, self-hosted LLM services, and LLM-powered devices. Each has its advantages but also shares common challenges. LLM app stores lower the barrier to development but lead to platform lock-in; LLM agents provide autonomy but lack a unified communication mechanism; self-hosted LLM services enhance control but increase deployment complexity; and LLM-powered devices improve privacy and real-time performance but are limited by hardware. This paper reviews and analyzes these paradigms, covering architecture design, application ecosystem, research progress, as well as the challenges and open problems they face. Based on this, we outline the next frontier of LLM applications, characterizing them through three interconnected layers: infrastructure, protocol, and application. We describe their responsibilities and roles of each layer and demonstrate how to mitigate existing fragmentation limitations and improve security and scalability. Finally, we discuss key future challenges, identify opportunities such as protocol-driven cross-platform collaboration and device integration, and propose a research roadmap for openness, security, and sustainability.

翻译：大语言模型（LLM）的发展催生了四大应用范式：LLM应用商店、LLM智能体、自托管LLM服务以及LLM驱动设备。每种范式皆具优势，但也面临共同挑战。LLM应用商店降低了开发门槛却易导致平台锁定；LLM智能体具备自主性但缺乏统一通信机制；自托管LLM服务增强了控制力却提升了部署复杂度；LLM驱动设备在隐私与实时性方面表现更优但受硬件条件制约。本文系统回顾并剖析了这些范式，涵盖其架构设计、应用生态、研究进展以及面临的挑战与开放性问题。在此基础上，我们勾勒出LLM应用的未来前沿，通过基础设施层、协议层与应用层三个相互关联的层次对其进行刻画，阐述各层的职责与作用，并论证如何缓解现有碎片化局限、提升安全性与可扩展性。最后，我们探讨了未来关键挑战，指出协议驱动的跨平台协作与设备集成等机遇，并针对开放性、安全性与可持续性提出了研究路线图。