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.

翻译：大语言模型（LLMs）已彻底改变了基于文本的代码自动化，但它们在面向图形的工程工作流中的潜力仍未得到充分探索。本文介绍SimuAgent，一个专为Simulink设计的基于LLM的建模与仿真智能体。SimuAgent使用简洁的字典式Python表示替代冗长的XML，显著减少了令牌数量，提升了可解释性，并实现了快速的进程内仿真。通过两阶段训练的轻量级规划-执行架构，使智能体同时具备了底层工具技能与高层设计推理能力。为应对长周期任务中的稀疏奖励问题，我们提出了Reflection-GRPO（ReGRPO），该方法通过融入提供丰富中间反馈的自反思轨迹来增强组相对策略优化（GRPO），从而加速收敛并提升鲁棒性。在新发布的包含5300个多领域建模任务的基准测试集SimuBench上的实验表明，经SimuAgent微调的Qwen2.5-7B模型比标准强化学习基线收敛更快，建模精度更高，甚至在同一基准的少样本提示评估中超越了GPT-4o。消融实验证实，两阶段课程学习与抽象-重构数据增强进一步提升了泛化能力。SimuAgent完全在本地使用普通硬件进行训练和运行，为工业模型驱动工程提供了一个保护隐私、经济高效的解决方案。SimuAgent弥合了大语言模型与图形化建模环境之间的鸿沟，为工业环境中的AI辅助工程设计提供了实用解决方案。