Evaluating LLMs as Interpretable Controllers for Dynamical Systems
Large Language Models (LLMs) are increasingly used for decision-making and reasoning tasks, yet their potential as controllers for physical systems remains largely unexplored. This work investigates whether LLMs can function as interpretable controllers for a dynamic thermal environment, examining their ability to follow setpoints, interpret natural-language commands, reason about actuator effects, and incorporate prior model-based knowledge. Five LLMs of varying scales are evaluated under multiple scenarios, including settings with penalties on heater or fan usage and cases where the models have access to a physics-based prediction tool. The results show that control performance depends ...
| Authors | Aleksander Østensen, Alberto Mino Calero, Anastasios M. Lekkas et al. |
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| Primary category | cs.AI |
| Published | 14 June 2026 |
| Last updated at source | 14 June 2026 |
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