梳理大模型应用四大范式，提出三层未来架构。
LLM Applications: Current Paradigms and the Next Frontier
- 分析四类应用模式的优劣与共性挑战。
- 提出基础设施-协议-应用三层融合新框架。
- 适合关注大模型生态演进的研究者与开发者。
大型语言模型（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服务和赋能设备的LLM。每种范式各有优势，但也面临共同挑战：应用商店降低开发门槛却导致平台锁定；智能体实现自主性但缺乏统一通信机制；自托管服务提升控制力但增加部署复杂度；赋能设备改善隐私与实时性但受限于硬件。本文系统回顾并分析这些范式在架构设计、应用生态、研究进展及所遇挑战方面的现状。基于此，我们提出大模型应用的下一前沿，以基础设施、协议与应用三层次互连为特征，阐明各层职责与作用，并展示如何缓解现有碎片化问题，增强安全性与可扩展性。最后，讨论关键未来挑战，识别协议驱动跨平台协作与设备集成等机遇，并提出面向开放性、安全性和可持续性的研究路线图。
原文摘要 · Abstract (English)
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.
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