# LLM-Driven Approach to Modeling Tool Interoperability in Automotive Domain

arXiv: https://arxiv.org/abs/2607.14659 · HTML: https://mlsanity.com/paper/2607.14659/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 16 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [Survey of GenAI for Automotive Software Development: From Requirements to Executable Code](https://mlsanity.com/paper/2507.15025/) (2025)
- [GenAI for Automotive Software Development: From Requirements to Wheels](https://mlsanity.com/paper/2507.18223/) (2025)
- [LLM-based Iterative Approach to Metamodeling in Automotive](https://mlsanity.com/paper/2503.05449/) (2025)
- [LLM-Assisted Semantic Alignment and Integration in Collaborative Model-Based Systems Engineering Using SysML v2](https://mlsanity.com/paper/2508.16181/) (2025)
- [Querying Large Automotive Software Models: Agentic vs. Direct LLM Approaches](https://mlsanity.com/paper/2506.13171/) (2025)
- [Text to model via SysML: Automated generation of dynamical system computational models from unstructured natural language text via enhanced System Modeling Language diagrams](https://mlsanity.com/paper/2507.06803/) (2025)

## Abstract (arXiv)

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. This paper presents an LLM-driven approach for automated model interoperability by considering two relevant aspects: 1) mapping model instances to a target metamodel 2) merging of metamodels. The proposed methodology is demonstrated through transformations involving Ecore and SysML v2 based metamodels and incorporates structural validation of generated model instances against user-defined target models. Automotive case studies illustrate the feasibility of the approach and show that large language models can significantly reduce manual transformation effort while generating structurally valid target models for cross-tool interoperability.