This paper presents an LLM-empowered workflow for RISC-V supply chain analysis, integrating Vision-Language Models (VLMs) and Model-Driven Engineering (MDE) to enable comprehensive, multimodal data-driven insights. The proposed approach addresses the challenges of heterogeneous and unstructured supply chain data by leveraging LLMs for textual understanding and VLMs for extracting information from visual artifacts such as diagrams, tables, and scanned documents. These models collaboratively identify key entities and relationships, which are then organized into a knowledge graph representing supply chain components and their interdependencies. For analytical reasoning, the workflow incorporates MDE techniques and constraint-based modeling to enable formal validation of dependencies, detection of bottlenecks, and assessment of risks. The synergy between LLM- and VLM-based semantic understanding and MDE-based formal analysis supports both exploratory and systematic evaluation of supply chain resilience. A human-in-the-loop mechanism further enables interactive querying and expert validation. The approach is evaluated in RISC-V ecosystem scenarios, demonstrating its effectiveness in generating actionable insights, enhancing transparency, and supporting decision-making in complex semiconductor supply chains.

翻译：本文提出一种基于大语言模型（LLM）的RISC-V供应链分析工作流，通过整合视觉语言模型（VLM）与模型驱动工程（MDE）技术，实现对多模态数据驱动的全面洞察。该方法针对异构与非结构化供应链数据的挑战，利用LLM进行文本语义理解，并借助VLM从图表、表格及扫描文档等视觉素材中提取信息。这些模型协同识别关键实体及其关联关系，进而构建表征供应链组件及其相互依赖关系的知识图谱。在分析推理环节，工作流融合MDE技术与约束建模方法，实现对依赖关系的形式化验证、瓶颈检测及风险评估。基于LLM与VLM的语义理解能力与MDE形式化分析能力的协同机制，支持对供应链韧性的探索性与系统性评估。通过人机协同机制实现交互式查询与专家验证。该方法在RISC-V生态系统场景中完成评估，验证了其在生成可执行洞察、提升透明度及支撑复杂半导体供应链决策方面的有效性。