[2603.21523] SafePilot: A Framework for Assuring LLM-enabled Cyber-Physical Systems

Computer Science > Robotics arXiv:2603.21523 (cs) [Submitted on 23 Mar 2026] Title:SafePilot: A Framework for Assuring LLM-enabled Cyber-Physical Systems Authors:Weizhe Xu, Mengyu Liu, Fanxin Kong View a PDF of the paper titled SafePilot: A Framework for Assuring LLM-enabled Cyber-Physical Systems, by Weizhe Xu and 2 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs), deep learning architectures with typically over 10 billion parameters, have recently begun to be integrated into various cyber-physical systems (CPS) such as robotics, industrial automation, and autopilot systems. The abstract knowledge and reasoning capabilities of LLMs are employed for tasks like planning and navigation. However, a significant challenge arises from the tendency of LLMs to produce "hallucinations" - outputs that are coherent yet factually incorrect or contextually unsuitable. This characteristic can lead to undesirable or unsafe actions in the CPS. Therefore, our research focuses on assuring the LLM-enabled CPS by enhancing their critical properties. We propose SafePilot, a novel hierarchical neuro-symbolic framework that provides end-to-end assurance for LLM-enabled CPS according to attribute-based and temporal specifications. Given a task and its specification, SafePilot first invokes a hierarchical planner with a discriminator that assesses task complexity. If the task is deemed manageable, it is passed directly to an LLM-based task planner with built-in veri...