Large Language Models in the Loop: A Stability- and Network-Aware Survey in Networked Control, Cyber-Physical, and Multi-Agent Systems

2026-09-14

AI Summary

本文探讨了如何在不牺牲闭环保证的情况下，将大型语言模型集成到网络控制系统、信息物理系统和多智能体系统中，通过慢速监督与快速内环控制相结合的方法解决稳定性问题。

Abstract

Modern networked control systems (NCSs), cyber-physical systems (CPSs), and complex multi-agent network systems (CNSs) increasingly rely on large language models (LLMs) for high-level decision-making. However, the slow, stochastic nature of LLMs directly conflicts with the strict stability and safety guarantees required by these physical systems. This survey presents a unified analysis of how LLMs can be admitted into the control loop of NCS, CPS, and CNS without compromising closed-loop guarantees. We organize this around a core principle: the LLM operates as a slow supervisor adjusting high-level goals and constraints, while a fast, certified inner loop maintains physical stability. Under this framework, LLM integration maps directly to classical networked control challenges, where inference latency acts as delay, API failures as packet dropouts, tokenization as quantization, and hallucinations as bounded disturbances. We assess current developments across all these three domains, highlighting that rising model capabilities are frequently accompanied by a drop in formal safety assurances. Finally, we propose concrete future research directions, identifying the widespread lack of formal stability proofs as the field's central open problem.

Problem

• Large Language Models
• Networked Control Systems
• Cyber-Physical Systems
• Multi-Agent Systems
• Stability

Innovation

• Large Language Models
• Networked Control Systems
• Stability Guarantees
• High-Level Decision-Making
• Unified Analysis