Beyond Rule-Based Context Awareness: Large Language Models as Adaptive Cognitive Layers in Cyber-Physical Systems

Abstract

Cyber-physical systems (CPS) have traditionally relied on rule-based mechanisms and machine learning models for context awareness. However, these approaches often struggle with dynamic adaptation, multimodal data integration, and real-time decision-making in complex environments. With the emergence of large language models (LLMs), we argue that CPS should adopt LLMs as adaptive cognitive layers capable of interpreting, reasoning, and responding to real-world contexts in real time. This position paper explores the paradigm shift introduced by LLMs, discusses their advantages and limitations, and presents a vision for their integration into next-generation CPS.