We describe the Algebras FluencyScore submissions to the WMT26 Automated Translation Quality Evaluation shared task (Tasks 1-2). FluencyScore is a reference-free structured LLM judge: five fluency sub-dimensions, a fixed-weight aggregate, and a diagnostic error record (issue type, severity, problem phrase). This system paper adapts that judge rather than replacing it. For Task 1 the diagnostic record is specialized into a multi-error character-span annotator with deterministic phrase-to-offset alignment, merge / whole-segment post-processing, and a conservative add-only overlay. For Task 2 the calibrated scalar is mapped onto the cESA scale with a WMT25-fitted affine adapter and moment matching (aira_mo). Submitted systems use Gemini 3.5 Flash and cover all 21 official pairs plus challenge sets. Official human-cESA rankings were not available at camera-ready time; we report no ranks and no Codabench verification scores.

FLORES is a benchmark dataset designed for evaluating machine translation systems, partic- ularly for low-resource languages. This paper, conducted as a part of Open Language Data Ini- tiative (OLDI) shared task, presents our contri- bution to expanding the FLORES dataset with high-quality translations from Russian to Tu- van, an endangered Turkic language. Our ap- proach combined the linguistic expertise of na- tive speakers to ensure both accuracy and cul- tural relevance in the translations. This project represents a significant step forward in support- ing Tuvan as a low-resource language in the realm of natural language processing (NLP) and machine translation (MT).