This paper describes the University of Florida gators submission to the WMT26 Low-Resource Indic Language Translation shared task. We adapt the retrieval-augmented many-shot translation pipeline from our AmericasNLP 2026 system to translate between English and eleven North-Eastern Indian languages in both directions. At inference time, BM25 retrieves the most similar parallel examples from a language-specific training bank, and Gemini 2.5 Flash translates the input conditioned on these examples. No model fine-tuning is involved. Training banks combine official WMT26 data with publicly available corpora such as Samanantar and prior WMT shared task releases. A grid search over retrieval count r and development exemplar count d across all 22 language-direction pairs selects the best configuration for each submission.

This paper presents the University of Florida Gators submission to the AmericasNLP 2026 shared task on cultural image captioning for Indigenous languages. The system uses a two-stage pipeline: first generating Spanish captions from images with a vision-language model, then translating them into target languages using retrieval-augmented many-shot prompting with Gemini 2.5 Flash. The paper reports strong improvements over the shared task baseline across multiple languages, analyzes the role of retrieval, synthetic exemplars, and morphology-aware prompting, and discusses limitations related to dev-set exemplars, cascade errors, and chrF++ based evaluation.