# GenAI-Driven Approach to RISC-V Supply Chain Exploration

arXiv: https://arxiv.org/abs/2605.15223 · HTML: https://mlsanity.com/paper/2605.15223/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 31 references that are in the map.
- First posted on arXiv 13 May 2026.

## Builds on (most cited first, 22 of 22)

- [DocLLM: A layout-aware generative language model for multimodal document understanding](https://mlsanity.com/paper/2401.00908/) (2023)
- [Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction](https://mlsanity.com/paper/2404.03868/) (2024)
- [Large Language Models for Supply Chain Optimization](https://mlsanity.com/paper/2307.03875/) (2023)
- [KGGen: Extracting Knowledge Graphs from Plain Text with Language Models](https://mlsanity.com/paper/2502.09956/) (2025)
- [SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graphs](https://mlsanity.com/paper/2410.02811/) (2024)
- [AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora](https://mlsanity.com/paper/2505.23628/) (2025)
- [Aligning Vision to Language: Annotation-Free Multimodal Knowledge Graph Construction for Enhanced LLMs Reasoning](https://mlsanity.com/paper/2503.12972/) (2025)
- [Large Language Models for Supply Chain Decisions](https://mlsanity.com/paper/2507.21502/) (2025)
- [SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions](https://mlsanity.com/paper/2408.05357/) (2024)
- [Enhancing Supply Chain Visibility with Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2408.07705/) (2024)
- [iText2KG: Incremental Knowledge Graphs Construction Using Large Language Models](https://mlsanity.com/paper/2409.03284/) (2024)
- [Automated Knowledge Graph Construction using Large Language Models and Sentence Complexity Modelling](https://mlsanity.com/paper/2509.17289/) (2025)
- [Structured Attention Matters to Multimodal LLMs in Document Understanding](https://mlsanity.com/paper/2506.21600/) (2025)
- [Zero- and Few-Shots Knowledge Graph Triplet Extraction with Large Language Models](https://mlsanity.com/paper/2312.01954/) (2023)
- [GenAI for Automotive Software Development: From Requirements to Wheels](https://mlsanity.com/paper/2507.18223/) (2025)
- [LLM-based Iterative Approach to Metamodeling in Automotive](https://mlsanity.com/paper/2503.05449/) (2025)
- [LKD-KGC: Domain-Specific KG Construction via LLM-driven Knowledge Dependency Parsing](https://mlsanity.com/paper/2505.24163/) (2025)
- [Towards a RISC-V Open Platform for Next-generation Automotive ECUs](https://mlsanity.com/paper/2307.04148/) (2023)
- [Enhancing Supply Chain Visibility with Generative AI: An Exploratory Case Study on Relationship Prediction in Knowledge Graphs](https://mlsanity.com/paper/2412.03390/) (2024)
- [LLM-Empowered Event-Chain Driven Code Generation for ADAS in SDV systems](https://mlsanity.com/paper/2511.21877/) (2025)
- [Automating Supply Chain Disruption Monitoring via an Agentic AI Approach](https://mlsanity.com/paper/2601.09680/) (2026)
- [RISC-V Functional Safety for Autonomous Automotive Systems: An Analytical Framework and Research Roadmap for ML-Assisted Certification](https://mlsanity.com/paper/2604.17391/) (2026)

## Abstract (arXiv)

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.