SimuAgent: An LLM-Based Simulink Modeling Assistant Enhanced with Reinforcement Learning

ICLR 2026 Conference Submission21498 Authors

Keywords: Large Language Models, Simulink Modeling, Reinforcement Learning, Intelligent Agent, Engineering Automation

TL;DR: SimuAgent is a lightweight LLM agent trained with reflection-guided reinforcement learning that converts Simulink models into compact Python dictionaries, plans and executes modeling tasks, and outperforms GPT-4o on the new SimuBench benchmark.

Abstract: Large language models (LLMs) have revolutionized text-based code automation, but their potential in graph-oriented engineering workflows remains under-explored. We introduce SimuAgent, an LLM-powered modeling and simulation agent tailored for Simulink. SimuAgent replaces verbose XML with a concise, dictionary-style Python representation, dramatically cutting token counts, improving interpretability, and enabling fast, in-process simulation. A lightweight plan–execute architecture, trained in two stages, equips the agent with both low-level tool skills and high-level design reasoning. To tackle sparse rewards in long-horizon tasks, we propose Reflection-GRPO (ReGRPO), which augments Group Relative Policy Optimization (GRPO) with self-reflection traces that supply rich intermediate feedback, accelerating convergence and boosting robustness. Experiments on SimuBench, our newly released benchmark comprising 5300 multi-domain modeling tasks, show that a Qwen2.5-7B model fine-tuned with SimuAgent converges faster and achieves higher modeling accuracy than standard RL baselines, and even surpasses GPT-4o when evaluated with few-shot prompting on the same benchmark. Ablations confirm that the two-stage curriculum and abstract-reconstruct data augmentation further enhance generalization. SimuAgent trains and runs entirely on-premise with modest hardware, delivering a privacy-preserving, cost-effective solution for industrial model-driven engineering. SimuAgent bridges the gap between LLMs and graphical modeling environments, offering a practical solution for AI-assisted engineering design in industrial settings.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 21498