LLM-Assisted Semantic Alignment and Integration in Collaborative Model-Based Systems Engineering Using SysML v2
arXiv:2508.16181v2 Announce Type: replace-cross Abstract: 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 mo
The increasing complexity of Model-Based Systems Engineering (MBSE) in cross-organizational projects coupled with the maturation of large language models (LLMs) is creating a convergence point for automated semantic alignment.
This development indicates a significant leap in how complex systems are designed and integrated, potentially accelerating innovation cycles and reducing errors across collaborative engineering efforts.
The previous manual and labor-intensive process of aligning diverse system models can now be significantly augmented by AI, reducing human friction and increasing interoperability.
- · Defense contractors
- · Aerospace industry
- · Large engineering firms
- · AI platform providers
- · Consulting firms specializing in manual systems integration
- · Organizations slow to adopt AI tools
Increased efficiency and reduced time-to-market for complex multi-stakeholder projects using MBSE.
Improved robustness and reliability of integrated systems due to more precise semantic alignment.
The potential for AI to autonomously generate or modify system models based on high-level requirements, leading to fully autonomous design agents.
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Read at arXiv cs.AI