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LLMs enhance semantic alignment in collaborative systems engineering using SysML v2

By PulseAugur Editorial · [1 sources] ·

A new paper proposes a method for using Large Language Models (LLMs), specifically GPT-based models, to improve semantic alignment in collaborative Model-Based Systems Engineering (MBSE). The approach leverages SysML v2's enhanced structural modularity and formal semantics to facilitate interoperable modeling. The core contribution involves an iterative process of developing alignment strategies and interaction prompts, which include model extraction, semantic matching, and verification, ultimately supporting traceable integration. AI

IMPACT This research could streamline collaboration in complex engineering projects by improving model interoperability.

RANK_REASON The cluster contains a research paper detailing a novel approach using LLMs for semantic alignment in MBSE. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs enhance semantic alignment in collaborative systems engineering using SysML v2

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The cluster contains a research paper detailing a novel approach using LLMs for semantic alignment in MBSE. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zirui Li, Stephan Husung, Haoze Wang ·

    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 modul…

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