SimuGen: Multi-modal Agentic Framework for Constructing Block Diagram-Based Simulation Models
Current large language models (LLMs) lack sufficient Simulink-domain pretraining data, rendering them unreliable for generating complete, executable Simulink simulation models directly from natural-language requirements. To address this, we propose the first multimodal agent framework specifically designed for Simulink modeling. Our approach integrates graph-structured visual understanding of Simulink diagrams, a domain-specific knowledge base, and a modular role-based collaboration mechanism—featuring specialized agents such as an investigator and a debug locator—to enable interpretable and reproducible end-to-end model generation. Crucially, the framework jointly models the visual representation and symbolic logic of Simulink models, supporting automated generation, debugging, and formal verification of simulation models from textual specifications. Evaluated on representative control and signal processing tasks, our method achieves significant improvements in code generation accuracy and structural completeness, demonstrating both technical efficacy and engineering practicality.