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

arXiv: https://arxiv.org/abs/2609.10132 · HTML: https://mlsanity.com/paper/2609.10132/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 9 Sep 2026.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [Extending Context Window of Large Language Models via Positional Interpolation](https://mlsanity.com/paper/2306.15595/) (2023)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding](https://mlsanity.com/paper/2403.04797/) (2024)
- [Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning](https://mlsanity.com/paper/2604.12374/) (2026)
- [Engineering Safety Requirements for Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2403.16289/) (2024)
- [Safety Analysis in the Era of Large Language Models: A Case Study of STPA using ChatGPT](https://mlsanity.com/paper/2304.01246/) (2023)
- [Lost in the Middle, and In-Between: Enhancing Language Models' Ability to Reason Over Long Contexts in Multi-Hop QA](https://mlsanity.com/paper/2412.10079/) (2024)
- [Concept-Guided LLM Agents for Human-AI Safety Codesign](https://mlsanity.com/paper/2404.15317/) (2024)
- [Trusta: Reasoning about Assurance Cases with Formal Methods and Large Language Models](https://mlsanity.com/paper/2309.12941/) (2023)
- [Order Matters: Rethinking Prompt Construction in In-Context Learning](https://mlsanity.com/paper/2511.09700/) (2025)
- [Towards Automated Safety Requirements Derivation Using Agent-based RAG](https://mlsanity.com/paper/2504.11243/) (2025)
- [What Works for 'Lost-in-the-Middle' in LLMs? A Study on GM-Extract and Mitigations](https://mlsanity.com/paper/2511.13900/) (2025)
- [Positional Failures in Long-Context LLMs: A Blind Spot in Reasoning Benchmarks](https://mlsanity.com/paper/2605.23170/) (2026)

## 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.