We describe GABU, our WAT2026 English-to-Indic multimodal translation system (CITATION), which translates image grounded English captions into Hindi, Bengali, Malayalam, and Odia. The task follows the WAT English-to-Indic multimodal track setup with text only, image captioning, and multimodal tracks (CITATION). Our system builds on the pretrained, instruction-tuned vision-language model google/gemma-4-E4B-it. Each input combines a cropped visual region with the English caption, rendered through a chat template with a script constrained translation instruction. We explore two approaches which includes a zero shot baseline and a fine tuned approach of the model using QLORA (rank 64, alpha 128, 4-bit NF4, double quantization), trained for 3,000 steps due to computational resource constraints, reflecting our aim to adapt the model’s existing instruction-following ability rather than train it from scratch. We evaluate both systems using BLEU and CHRF3 under greedy decoding and report macro averaged scores across the target languages. The fine-tuned model substantially outperforms the zero shot baseline and achieves a BLEU score of 43.97 and 69.99 CHRF3 on the test split, and 40.37 BLEU and 68.25 CHRF3 on the challenge split. These results correspond to improvements of 24.39 BLEU and 18.72 CHRF3 points on the test split, and 16.70 BLEU and 11.90 CHRF3 points on the challenge split.

We describe our system for the AstroCLIMB shared task (WASP 2026), which reconstructs the citation graph of astronomy papers from scientific figures and captions alone. The task requires identifying the relationship between pairs of figures and captions as same_figure, same_paper, related_papers, or unrelated_papers. Our system finetunes google/gemma-4-E4B-it using QLoRA on 8,200 competition training rows with a shared descriptive prompt and greedy decoding. A zero shot baseline achieves 0.4408 macro F1 on a balanced 1,800 pair validation split held out from the competition training file, and the finetuned system substantially improves this citation-relation classification for the challenging related_papers class in particular. Our submitted system achieves 0.64282 F1 on all 10,000 official test pairs. These results show that multimodal finetuning can improve citation-relation inference from scientific figures and captions when document metadata is unavailable.