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Context operations to architecture modelling output from large language models and evaluation criteria for their use in systems engineering design

arXiv 2609.10132 ↗First posted 9 Sep 2026eess.SY cs.AI cs.SEcited by 0

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Abstract (arXiv)

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.

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References · 18

  1. 2017Attention Is All You Needcited by 60,361
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  12. 2024Lost in the Middle, and In-Between: Enhancing Language Models' Ability to Reason Over Long Contexts in Multi-Hop QAcited by 13
6 more
  1. 2024Concept-Guided LLM Agents for Human-AI Safety Codesigncited by 6
  2. 2023Trusta: Reasoning about Assurance Cases with Formal Methods and Large Language Modelscited by 3
  3. 2025Order Matters: Rethinking Prompt Construction in In-Context Learningcited by 3
  4. 2025Towards Automated Safety Requirements Derivation Using Agent-based RAGcited by 2
  5. 2025What Works for 'Lost-in-the-Middle' in LLMs? A Study on GM-Extract and Mitigationscited by 2
  6. 2026Positional Failures in Long-Context LLMs: A Blind Spot in Reasoning Benchmarkscited by 1

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