mlsanityData to 7 Oct 2026

Retrieval Augmented (Knowledge Graph), and Large Language Model-Driven Design Structure Matrix (DSM) Generation of Cyber-Physical Systems

arXiv 2602.16715 ↗First posted 30 Jan 2026cs.AI cs.CL cs.SYcited by 0

On the map: Retrieval-augmented generation → RAG for code

Abstract (arXiv)

We explore the potential of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Graph-based RAG (GraphRAG) for generating Design Structure Matrices (DSMs). We test these methods on two distinct use cases--a power screwdriver and a CubeSat with known architectural references--evaluating their performance on two key tasks: determining relationships between predefined components, and the more complex challenge of identifying components and their subsequent relationships. We measure the performance by assessing each element of the DSM and overall architecture. Despite design and computational challenges, we identify opportunities for automated DSM generation, with all code publicly available for reproducibility and further feedback from the domain experts.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG for code, one of 15 directions in Retrieval-augmented generation. Builds on 25 of its 103 references that are in the map. First posted on arXiv 30 Jan 2026.

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25 of 103 references.

References · 25

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13 more
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