Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction
Quick Answer
This study demonstrates that a compact Small Language Model (Qwen2.5-1.5B) can be effectively retrained for control reasoning in autonomous industrial operations, achieving 91.5% action-alignment accuracy with a mean inference latency of 3.84 seconds.
Quick Take
The framework integrates a validator-guided correction loop, supporting robust physical regulation in edge applications despite reduced token-level agreement.
Key Points
- Achieved 91.5% average action-alignment accuracy in thermal-control simulations.
- Mean inference latency of 3.84 seconds, suitable for edge applications.
- Framework includes a validator-guided correction loop with a symbolic validation layer.
- Maintained a 95% in-range rate under symbolic re-mapping.
- Supports reconfigurable autonomous control in industrial settings.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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)
— Originally published at arxiv.org
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