Benchmarks do more than report scores: they provide a shared basis for evaluating methods, comparing systems, and measuring progress. As language technologies are now widely integrated into everyday life, efforts should also be made to include minority languages in the resources that underpin language-technology development. If languages are absent from relevant benchmarks, they remain harder to evaluate, compare, and incorporate into future language technologies. Expanding benchmark coverage is therefore essential for ensuring fair evaluation, increasing visibility, and enabling continued development of language technologies for these communities. To support MT development for Ladin, we contribute a Ladin extension of BOUQuET to the WMT 2026 OLDI shared task, covering the Val Badia and Gherdëina varieties. We present a systematic reference-creation workflow combining human translation, expert revision, cross-variant alignment, and blind ranking to produce high-quality references. The dataset is released under the CC BY-SA 4.0 license.

Recent advances in neural machine translation (NMT) have opened new possibilities for developing translation systems also for smaller, so-called low-resource, languages. The rise of large language models (LLMs) has further revolutionized machine translation by enabling more flexible and context-aware generation. However, many challenges remain for low-resource languages, and the availability of high-quality, validated test data is essential to support meaningful development, evaluation, and comparison of translation systems. In this work, we present an extension of the FLORES+ dataset for two Ladin variants, Val Badia and Gherdëina, as a submission to the Open Language Data Initiative Shared Task 2025. To complement existing resources, we additionally release two parallel datasets for Gherdëina–Val Badia and Gherdëina–Italian. We validate these datasets by evaluating state-of-the-art LLMs and NMT systems on this test data, both with and without leveraging the newly released parallel data for fine-tuning and prompting. The results highlight the considerable potential for improving translation quality in Ladin, while also underscoring the need for further research and resource development, for which this contribution provides a basis.