LLM-Assisted Semantic Alignment and Integration in Collaborative Model-Based Systems Engineering Using SysML v2
📰 ArXiv cs.AI
Learn how LLMs enhance semantic alignment in collaborative MBSE using SysML v2, improving model integration and interoperability
Action Steps
- Apply LLMs to analyze SysML v2 models for semantic inconsistencies
- Use GPT-based LLMs to generate formal semantics for model elements
- Integrate LLM-assisted model analysis with existing MBSE tools and workflows
- Configure LLMs to learn from feedback and adapt to project-specific modeling conventions
- Test LLM-assisted model integration on a collaborative MBSE project to evaluate its effectiveness
Who Needs to Know This
Systems engineers and researchers working on collaborative MBSE projects can benefit from this approach to improve model consistency and reduce integration errors
Key Insight
💡 LLMs can significantly improve semantic alignment and model integration in collaborative MBSE by providing automated analysis and feedback mechanisms
Share This
🤖 LLMs enhance semantic alignment in collaborative MBSE using SysML v2! 📈 Improve model integration and interoperability with AI-assisted analysis and feedback loops
Key Takeaways
Learn how LLMs enhance semantic alignment in collaborative MBSE using SysML v2, improving model integration and interoperability
Full Article
Title: LLM-Assisted Semantic Alignment and Integration in Collaborative Model-Based Systems Engineering Using SysML v2
Abstract:
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 pa
Abstract:
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 pa
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