arXiv AI July 17, 2026 By Nenad Petrovic, Jiajie Zhang, Vahid Zolfaghari, Alois Knoll

LLM-Driven Approach to Modeling Tool Interoperability in Automotive Domain

arXiv:2607. 14659v1 Announce Type: cross Abstract: Interoperability between heterogeneous modeling tools remains a significant challenge in Model-Driven Engineering (MDE), particularly in the automotive domain where multiple modeling languages, as well as defacto standard proprietary and open-source tools coexist.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

llms

arXiv AI

Jul 7

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.

By Zirui Li, Stephan Husung, Haoze Wang

llms safety

arXiv AI

Jun 15

I'm Sorry Driver, I'm Afraid I Can't Do That: Appraising the Safety of LLMs within Automotive Contexts

arXiv:2606. 14327v1 Announce Type: cross Abstract: This paper appraises recent frameworks within AI development to integrate LLMs into control tasks in automotive contexts from the perspective of safety assurance.

By Shaun Feakins, Ibrahim Habli, Kim Littler, Robert Palin

llms safety

arXiv AI

Jul 21

OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs

arXiv:2607. 17963v1 Announce Type: new Abstract: Ontology extension refers to the process of enriching an existing ontology in response to emerging requirements, making it more complete.

By Anna Sofia Lippolis, Mohammad Javad Saeedizade, Stefan Schmid, Simon Blattner, Robin Keskis\"arkk\"a, Aldo Gangemi, Eva Blomqvist, Andrea Giovanni Nuzzolese

llms rag

arXiv AI

Jun 2

Rethinking Scientific Modeling: Toward Physically Consistent and Simulation-Executable Programmatic Generation

arXiv:2602. 07083v2 Announce Type: replace-cross Abstract: Structural modeling is a fundamental component of computational engineering science, in which even minor physical inconsistencies or specification violations may invalidate downstream simulations.

By Yongqing Jiang, Jianze Wang, Zhiqi Shen, Zhenghong Lin, Jiayuan Wang, Yijian Yang, Kaoshan Dai, Haoran Luo

llms fine-tuning benchmarks safety

arXiv AI

Jun 2

Bridging Requirements and Architecture: Multi-Agent Orchestration with External Knowledge and Hierarchical Memory

arXiv:2606. 01385v1 Announce Type: cross Abstract: Software architecture design is a critical yet inherently complex and knowledge-intensive phase that requires balancing competing quality attributes and adapting to evolving requirements.

By Ruiyin Li, Yiran Zhang, Xiyu Zhou, Yangxiao Cai, Peng Liang, Weisong Sun, Jifeng Xuan, Zhi Jin, Yang Liu

llms rag agents

arXiv AI

Jul 8

Auto-DSM Under the Lens: A Black-Box Evaluation Framework for LLM-Based DSM Generation

arXiv:2607. 05985v1 Announce Type: new Abstract: This paper presents a black-box evaluation framework to systematically assess the ability of Large Language Models (LLMs) to generate Design Structure Matrices (DSMs) from structured technical documentation.

By Niels Potters, Theo Hofman

llms benchmarks safety

Related stories

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

I'm Sorry Driver, I'm Afraid I Can't Do That: Appraising the Safety of LLMs within Automotive Contexts

OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs

Rethinking Scientific Modeling: Toward Physically Consistent and Simulation-Executable Programmatic Generation

Bridging Requirements and Architecture: Multi-Agent Orchestration with External Knowledge and Hierarchical Memory

Auto-DSM Under the Lens: A Black-Box Evaluation Framework for LLM-Based DSM Generation