面向系统工程设计的 LLM 架构建模输出：上下文操作与评估准则

Electrical Engineering and Systems Science > Systems and Control arXiv:2609.10132 (eess) [Submitted on 9 Sep 2026] Title:Context operations to architecture modelling output from large language models and evaluation criteria for their use in systems engineering design Authors:Vinicius Kaster Marini, Petter Krus View a PDF of the paper titled Context operations to architecture modelling output from large language models and evaluation criteria for their use in systems engineering design, by Vinicius Kaster Marini and Petter Krus View PDF HTML (experimental) Abstract:The development of generative artificial intelligence resources enables opportunities of speeding up systems and engineering design work. This contribution introduces a framework of formal operations for assembling context in LLM-based engineering design. This framework involves the assembly of modular context units, including policy prompts, reference units with persistence, and user questions with prompt vectoring. This approach enables the systematic structuring of interactions with generative models. A formal method for evaluating modelling-as-code LLM outputs is also presented, which enables the evaluation of compliance to intent from LLM answers and thereby asses the support from LLMs for systems architecture modelling. Comments: Subjects: Systems and Control (eess.SY); Artificial Intelligence (cs.AI); Software Engineering (cs.SE) Cite as: arXiv:2609.10132 [eess.SY] (or arXiv:2609.10132v1 [eess.SY] for this version) https://doi.org/10.48550/arXiv.2609.10132 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Vinicius Marini Ph.D. [view email] [v1] Wed, 9 Sep 2026 13:09:01 UTC (5,397 KB) Full-text links: Access Paper: View a PDF of the paper titled Context operations to architecture modelling output from large language models and evaluation criteria for their use in systems engineering design, by Vinicius Kaster Marini and Petter Krus View PDF HTML (experimental) TeX Source view license Current browse context: eess.SY new | recent | 2026-09 Change to browse by: cs cs.AI cs.SE cs.SY eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)