Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction

Researchers developed a closed-loop control framework using a small language model (Qwen2.5-1.5B) aligned via GRPO and a multi-agent self-correction loop with a digital-twin validator. In thermal-control simulations, the system achieved 91.5% action-alignment accuracy at 3.84s mean inference latency, demonstrating a practical path for reconfigurable autonomous control at the edge.

arXiv:2607.09713v1 Announce Type: new Abstract: A key step toward autonomous industrial operation is the ability to create and reconfigure control policies from natural-language requirement specifications, with minimal or no manual redesign. In this setting, policy generation by AI agents can be a credible path when paired with a plant-aware validator e.g., a digital twin that can check generated candidate actions before execution. However, practical deployment is constrained by inference latency and compute footprint: large cloud-based models are often too slow, opaque, or data-sensitive for edge closed-loop use. This work investigates whether a compact Small Language Model SLM can be retrained for control reasoning and embedded in a validator-guided correction loop. We use a Qwen2.5-1.5B model aligned via Group Relative Policy Optimization GRPO , combined with i an action agent, ii a symbolic/digital-twin-style validation layer, and iii a reprompting agent that iteratively steers outputs toward valid actions. In randomized thermal-control simulations 30 experiments with 500 steps each , the framework achieves 91.5% average action-alignment accuracy 86.3%--100% across cases at 3.84\,s mean inference latency. Under symbolic re-mapping, it maintains a 95% in-range rate, indicating robust physical regulation despite reduced token-level agreement. These results support SLM+validator architectures as a practical path toward reconfigurable autonomous control at the edge.