标题： SimuAgent：一个基于大语言模型的Simulink建模助手，通过强化学习进行增强 显示英文标题

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

摘要： 大型语言模型（LLMs）已经革新了基于文本的代码自动化，但它们在图导向的工程工作流中的潜力仍鲜有探索。 我们引入了SimuAgent，一个为Simulink量身定制的基于LLM的建模和仿真代理。 SimuAgent用简洁的字典式Python表示代替冗长的XML，显著减少标记数量，提高可解释性，并实现快速的进程内仿真。 一种轻量级的计划-执行架构，经过两个阶段的训练，使代理具备低级别的工具技能和高级别的设计推理能力。 为了解决长视野任务中的稀疏奖励问题，我们提出了Reflection-GRPO（ReGRPO），它通过自我反思轨迹增强组相对策略优化（GRPO），提供丰富的中间反馈，加速收敛并提升鲁棒性。 在我们新发布的基准SimuBench上的实验表明，使用SimuAgent微调的Qwen2.5-7B模型在收敛速度和建模准确性方面优于标准的RL基线，并且在相同基准上使用少样本提示进行评估时甚至超过了GPT-4o。 消融实验确认，两阶段的课程学习和抽象-重建数据增强进一步提高了泛化能力。 SimuAgent在硬件要求适中的本地环境中进行训练和运行，为工业模型驱动工程提供了隐私保护、成本效益高的解决方案。 SimuAgent弥合了LLMs与图形建模环境之间的差距，为工业环境中的AI辅助工程设计提供了一个实用的解决方案。 显示英文摘要

摘要： 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.