Assessing whether concrete software architectures are consistent with their reference architecture (RA) is essential to ensure that architectural decisions scale coherently across projects, preserving the quality attributes the RA was designed to guarantee. In the absence of dedicated techniques, mapping the components of an architectural model onto a predefined RA can be addressed only with general-purpose model-comparison algorithms. Our experiments show that these algorithms are inaccurate when applied to RAs. To address this gap, we propose Reference Architecture Mapping Discovery (RAMD), an LLM-based workflow that combines model analysis with structured prompting, and refines results through an iterative RA compliance-checking mechanism. We evaluate RAMD on 64 open-source architectural models spanning three RAs from the IoT, self-driving, and smart parking domains. With domain knowledge and GPT-4o, RAMD reaches 85.8% mapping accuracy and outperforms the strongest baseline by 44.6 percentage points, while ensuring structural consistency with the RA.

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