# LLM-Assisted Semantic Alignment and Integration in Collaborative Model-Based Systems Engineering Using SysML v2

arXiv: https://arxiv.org/abs/2508.16181 · HTML: https://mlsanity.com/paper/2508.16181/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 37 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design](https://mlsanity.com/paper/2303.07839/) (2023)
- [Trust at Your Own Peril: A Mixed Methods Exploration of the Ability of Large Language Models to Generate Expert-Like Systems Engineering Artifacts and a Characterization of Failure Modes](https://mlsanity.com/paper/2502.09690/) (2025)

## Built on by (most cited first, 4 of 4)

- [Automated Semantic Fault Localization in SysML v2: A Human-in-the-Loop Framework Using Knowledge-Graph Augmented LLMs](https://mlsanity.com/paper/2606.23395/) (2026)
- [Natural-Language to SysMLv2 Translation via Conformance-Driven Iterative Refinement](https://mlsanity.com/paper/2607.14162/) (2026)
- [LLM-Driven Approach to Modeling Tool Interoperability in Automotive Domain](https://mlsanity.com/paper/2607.14659/) (2026)
- [Cross-Organizational SysML Model Integration: A Survey of Challenges and AI-Supported Tasks](https://mlsanity.com/paper/2609.37000/) (2026)

## Abstract (arXiv)

Cross-organizational collaboration in Model-Based Systems Engineering (MBSE) faces many challenges in achieving semantic alignment across independently developed system models. SysML v2 introduces enhanced structural modularity and formal semantics, offering a stronger foundation for interoperable modeling. Meanwhile, GPT-based Large Language Models (LLMs) provide new capabilities for assisting model understanding and integration. This paper proposes a structured, prompt-driven approach for LLM-assisted semantic alignment of SysML v2 models. The core contribution lies in the iterative development of an alignment approach and interaction prompts, incorporating model extraction, semantic matching, and verification. The approach leverages SysML v2 constructs such as alias, import, and metadata extensions to support traceable, soft alignment integration. It is demonstrated with a GPT-based LLM through an example of a measurement system. Benefits and limitations are discussed.