Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction
3.40T1 sourcearXiv cs.MA
Source record
Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2607.09713.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryA research paper investigating whether a compact Small Language Model (Qwen2.5-1.5B aligned via GRPO) can serve as a control policy within a multi-agent loop featuring an action agent, a symbolic/digital-twin validator, and a reprompting agent. Thermal-control simulations across 30 experiments (500 steps each) show 91.5% average action-alignment accuracy at 3.84s mean inference latency and 95% in-range rate under symbolic re-mapping.
Why it mattersThe SLM-plus-validator plus reprompting-correction loop is a concrete agentic pattern for edge deployment under latency and compute constraints, worth examining as a reusable template beyond industrial control.
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