Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction
This paper presents a closed-loop control framework using a small language model (SLM) aligned via Group Relative Policy Optimization (GRPO). The system integrates an action agent, a digital-twin validator, and a reprompting agent to iteratively correct outputs. In thermal control simulations, it achieves 91.5% action-alignment accuracy with 3.84s inference latency, demonstrating viability for edge autonomous control.
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[Submitted on 24 Jun 2026]
Title:Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction
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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.
Comments: Accepted by IEEE CCTA 2026
Subjects:
Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Robotics (cs.RO)
Cite as: arXiv:2607.09713 [cs.AI]
(or arXiv:2607.09713v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.09713
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yuchen Wang [view email] [v1] Wed, 24 Jun 2026 13:49:01 UTC (1,259 KB)
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