<![CDATA[Background: Unified Modeling Language (UML) is fundamental to software architecture, yet the automated generation of high-level diagrams remains underexplored. Specifically, Component and Deployment diagrams pose significant challenges due to their high abstraction and complex architectural dependencies, which are difficult to infer from natural language descriptions alone. Objective: This study aimed to develop and validate a novel, end-to-end framework to bridge the gap between natural language feature descriptions and executable UML architectural diagrams. The primary goal was to fully automate the pipeline, from requirement generation to robust, multimodal validation of the final visual outputs. Methods: A quantitative study was conducted using a three-stage automated pipeline. First, LLaMA 3.2-1B-Instruct generated diverse feature descriptions. Second, DeepSeek-R1-Distill-Qwen-32B performed advanced reasoning to synthesize executable PlantUML code for Component and Deployment diagrams. Finally, a novel multimodal validation framework was introduced, employing an ensemble of three vision-language models—Qwen2.5-VL-3B, LLaMA-3.2-11B-Vision, and Aya-Vision-8B—to quantitatively assess the fidelity of the generated diagrams against their source descriptions. Results: Our framework demonstrated high fidelity in accurately capturing both system modularity (Component diagrams) and runtime allocation (Deployment diagrams). The reasoning-driven synthesis by DeepSeek-R1 significantly outperformed baseline models in generating architecturally correct diagrams. The multimodal evaluation pipeline effectively reduced scoring bias by integrating diverse validation perspectives. A key outcome is the creation of a systematically generated benchmark dataset of architectural diagrams. Conclusion: This study successfully establishes the viability of an AI-driven pipeline for automated UML architecture generation and validation. It provides three key contributions: the first fully automated pipeline for this task, a novel multimodal validation method, and a public benchmark dataset. This work lays a foundation for practical, AI-powered software architecture modeling. Future work should extend this framework to encompass behavioral UML diagrams.]]>
