Stateful Multi-Agent LLMs for Cross-View Interface Alignment in Automotive Model-Based Systems Engineering

• URL: http://arxiv.org/abs/2608.08038v1
• Date: Sat, 08 Aug 2026 09:49:10 GMT
• Title: Stateful Multi-Agent LLMs for Cross-View Interface Alignment in Automotive Model-Based Systems Engineering
• Abstract summary: Large Language Models (LLMs) can accelerate Model-Based Systems Engineering (MBSE) for software-defined vehicles.<n>Their probabilistic nature causes "architectural drift", fabricating interfaces in behavioral views that lack structural foundations.<n>We propose a stateful, multi-agent validation pipeline to enforce deterministic interface alignment.
• Score: 29.585470064353014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While Large Language Models (LLMs) can accelerate Model-Based Systems Engineering (MBSE) for software-defined vehicles, their probabilistic nature causes "architectural drift", fabricating interfaces in behavioral views that lack structural foundations. To enforce deterministic interface alignment, we propose a stateful, multi-agent validation pipeline. The framework utilizes a sequential generation matrix (Class->Activity->Sequence) and Vehicle Signal Specification (VSS)-grounded Retrieval-Augmented Generation (RAG). An independent AI Validator Agent dynamically audits outputs against a strict error taxonomy, triggering state-preserving backtracking loops to resolve incompatibilities. Evaluated on an Advanced Driver Assistance System (ADAS) scenario, standard RAG yielded 0% Entity Traceability. Conversely, our multi-agent workflow eradicated cross-phase hallucinations, achieving 97% Entity Traceability, 87% Signal Conservation, and an 85% F1-score. This proves adversarial auditing enables LLMs to reliably synthesize zero-error MBSE architectures.

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