AI 中文总结
本文以网络控制智能（NCI）为框架，梳理网络控制系统三阶段演进，提出可信自主网络定义及参考架构，明确LLM赋能运营的集成模式与相关研究议程。
AI 中文摘要
自现代通信网络诞生以来，运营自动化的追求从未停止，但网络自动化的演进难以用单一成熟度阶梯描述。历史进程中，网络控制系统在观测、决策支持、常规执行及运营商交互方面的能力不断扩展，却未实现均衡发展，这种不均衡使自动化程度无法作为网络侧执行可信性的可靠指标。未解决的问题不仅是系统提供多少自动化，更是在何种条件下可信任其改变网络状态。本文通过网络控制智能（NCI）这一涵盖决策逻辑、适应性、知识、控制委托及接口的五轴框架研究该问题，用NCI将网络控制系统演进分为三个时代：基于规则与脚本的自动化、可编程与数据驱动的控制、大语言模型（LLM）赋能的网络运营。从该框架看，三个时代存在持续的不对称性，且这些进展均无法自动确定何时应信任网络控制改变网络状态。本文将可信自主定义为系统可推断内容、可验证内容与授权执行内容间的受管控对齐，在此基础上开发了将提案生成与受管控执行分离的参考架构，确定了LLM赋能运营的重复集成模式，并制定了在明确保证、安全及治理约束下实现更高网络自主性的研究议程。
英文摘要
Since the inception of modern communication networks, the quest for operations automation has never ceased. Yet the evolution of network automation is difficult to characterize with a single maturity ladder. Throughout this history, network control systems have expanded their capabilities for observation, decision support, routine execution, and operator interaction, but these capabilities have not advanced uniformly. Such uneven progress makes the degree of automation an unreliable proxy for trustworthy network-side actuation. The unresolved question is not simply how much automation a system provides, but under what conditions it can be entrusted to change the network state. This paper examines that question through Network Control Intelligence (NCI), a five-axis framework spanning Decision Logic, Adaptability, Knowledge, Control Delegation, and Interface. We use NCI to organize the evolution of network-control systems into three eras: rule-based and scripted automation, programmable and data-driven control, and Large Language Model (LLM)-enabled network operations. Viewed through this framework, the three eras reveal a persistent asymmetry. None of these gains, however, automatically determines when network control should be trusted to change the network state. We frame trustworthy autonomy as a governed alignment between what a system can infer, what it can verify, and what it is authorized to execute. On that basis, the paper develops a reference architecture that separates proposal generation from governed execution, identifies recurring integration patterns for LLM-enabled operations, and derives a research agenda for higher network autonomy under explicit assurance, safety, and governance constraints.
