LLMs show promise as interpretable controllers for dynamic systems

A new research paper explores the use of Large Language Models (LLMs) as interpretable controllers for dynamic systems, specifically a thermal environment. The study evaluated five LLMs of varying scales, finding that higher-complexity models like Qwen-3 14B and GPT-4o demonstrated superior performance in temperature tracking and stable actuator usage. Incorporating a physics-based model further enhanced control by enabling anticipatory decision-making and improving energy efficiency, suggesting that LLMs can serve as effective, explainable controllers when adequately capable and grounded in domain knowledge. AI

IMPACT Demonstrates LLMs' potential for real-world control applications, suggesting future hybrid control strategies.

RANK_REASON The cluster contains a research paper detailing an evaluation of LLMs for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Adil Rasheed Professor
• arXiv
• GPT-4o

• paper
• model release

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