Natural-Language to SysMLv2 Translation via Conformance-Driven Iterative Refinement

2026-07-14

AI Summary

This work addresses the challenge of reliably translating natural language into industrial-grade, deployable SysMLv2 models. The authors propose an iterative generate-check-repair framework that, for the first time, integrates a production-level SysMLv2 conformance checker directly into the generation process as a control mechanism rather than a post-processing step. By combining large language model (LLM) generation with deterministic diagnostic feedback and targeted repair strategies—and terminating only when zero errors remain—the method achieves perfect compliance. Evaluated across 604 test cases derived from 151 prompts and four distinct LLMs, the approach elevates single-pass generation compliance from 51.16% to 100%, enabling robust, direct translation of natural language specifications into engineering-ready SysMLv2 models.

Abstract

Model-Based Systems Engineering (MBSE) relies on formal system models as primary technical artifacts for representing requirements, structure, and behavior across the system lifecycle. With the standardization of SysMLv2 as a textual language, interest is increasing in translating natural-language descriptions directly into executable models. For practical deployment, generated models must be accepted by industrial modeling environments, not merely satisfy grammar constraints. We present a conformance-checker-driven framework for reliable natural-language-to-SysMLv2 translation that enforces production-level acceptance as the termination condition. The system embeds a SysMLv2 conformance checker within a generate-check-repair loop. Each model is evaluated using the checker, and deterministic diagnostics are incorporated into revisions until zero conformance errors are achieved. Using the production checker as the oracle ensures the framework targets deployability rather than grammar plausibility. We evaluate the approach on the full SysMBench prompt set of 151 prompts across four large language model backends, yielding 604 prompt-model cases. Single-shot generation achieves 51.16% production-conformance acceptance, while our approach achieves 100.00% conformance. By elevating production conformance from a post-processing check to a control mechanism within generation, the framework converts probabilistic outputs into production-accepted SysMLv2 artifacts suitable for loading, visualization, and engineering use.

Problem

• Natural-Language to SysMLv2 Translation
• Model-Based Systems Engineering
• Conformance Checking
• Production-Level Acceptance
• Executable Models

Innovation

• conformance-driven
• iterative refinement
• natural-language-to-SysMLv2
• model-based systems engineering
• executable modeling