构建AgentOS：从令牌级上下文到涌现的系统级智能
Architecting AgentOS: From Token-Level Context to Emergent System-Level Intelligence
AI总结：
本文提出AgentOS框架，通过重构LLM为推理内核，引入深度上下文管理和语义切片机制，旨在构建具备系统级智能的自主认知环境。
摘要：
大语言模型范式正经历从静态推理引擎向动态自主认知系统的根本转变。尽管当前研究主要集中在扩展上下文窗口或优化提示工程，但微尺度令牌处理与宏观尺度系统智能之间的理论桥梁仍然碎片化。本文提出AgentOS，一个整体的概念框架，重新定义LLM为受结构操作系统逻辑约束的
英文摘要：
The paradigm of Large Language Models is undergoing a fundamental transition from static inference engines to dynamic autonomous cognitive systems.While current research primarily focuses on scaling context windows or optimizing prompt engineering the theoretical bridge between micro scale token processing and macro scale systemic intelligence remains fragmented.This paper proposes AgentOS,a holistic conceptual framework that redefines the LLM as a "Reasoning Kernel" governed by structured operating system logic.Central to this architecture is Deep Context Management which conceptualizes the context window as an Addressable Semantic Space rather than a passive buffer.We systematically deconstruct the transition from discrete sequences to coherent cognitive states introducing mechanisms for Semantic Slicing and Temporal Alignment to mitigate cognitive drift in multi-agent orchestration.By mapping classical OS abstractions such as memory paging interrupt handling and process scheduling onto LLM native constructs, this review provides a rigorous roadmap for architecting resilient scalable and self-evolving cognitive environments.Our analysis asserts that the next frontier of AGI development lies in the architectural efficiency of system-level coordination.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
