Abstract
Term injection in machine translation is undergoing a paradigm shift in the era of large language models (LLMs). Although recent shared-task results suggest near-saturation for sentence-level term injection from pre-defined glossaries, it remains unclear whether this also holds in more challenging settings. We address this question with a custom test set for legal translation from Italian into South Tyrolean German, a non-dominant and under-resourced language variety. We cover three terminology challenges: simple term injection, localisation of abbreviated forms, and homonym disambiguation. We focus on Reasoning Models (RMs) leveraging Test-Time Scaling, comparing them with different architectures and contributing a human analysis of reasoning traces. We find that reasoning offers little benefit for simple term injection, but yields clear gains for semantically complex cases such as homonym disambiguation. However, human evaluation of reasoning traces shows that these gains do not necessarily reflect robust and factually grounded translation-specific reasoning. We further show that without external terminological resources, even state-of-the-art RMs struggle to retrieve correct terminology for a non-dominant variety, while NMT small models remain competitive when trained on in-domain bilingual corpora. Based on these findings, we propose data collection strategies for inducing translation-specific reasoning, frameworks for adapting to and evaluating terminology across many language varieties, and terminology challenges beyond simple term injection.