Efficient reasoning with small language models: A path forward for agentic AI in Cyber-Physical Systems

Abstract Recent discourse in Artificial Intelligence (AI) emphasizes the growing promise of Small Language Models (SLMs) as a counterpoint to the dominance of Large Language Models (LLMs). While LLMs excel at general-purpose reasoning, their heavy computational demands, high latency, and energy costs often limit their practicality in real-world deployments, particularly in safety-critical environments. This paper explores the emerging role of SLMs in agentic AI, with a focus on Cyber-Physical Systems (CPS). We argue that SLMs, when coupled with augmentation mechanisms such as self-consistency, feedback verification, tool integration, and explicit control scaffolding, can serve as efficient and reliable reasoning backbones for CPS. Our evaluation framework considers four critical metrics: inference efficiency, fine-tuning agility, edge deployment, and parameter utilization. Through targeted analysis and comparative experiments, we showed the accuracy of SLMs, and controllability factors that are central to CPS viability.