In this paper, we describe our submission to the WMT26 Multilingual Instruction Shared Task (MIST), Sub-task 1 (context-based question answering). We fine-tune Qwen2.5-3B-Instruct with a LoRA adapter on 3,112 extractive-QA examples drawn from the answerable subset of TyDi QA as released in the wmt26-mist-sample data. We submit outputs for the monolingual Arabic (question and context both in Arabic) instances of the official qa-context test set. Our system reaches a mean token accuracy of 0.9643 and produces accurate extractive spans. On the held-out split, fine-tuning roughly doubles both exact match (23.81 → 53.97) and token-level F1 (37.00 → 68.52) relative to the base model. The adapter corrects over-refusal tendency in addition to improving span precision. The base model emitted the prescribed no-answer string in 60% of passages with answers, versus 0% for the fine-tuned model. We report error analysis identifying recurring failures, and discuss the model’s weaker behaviour on cross-lingual context instances, which make up the large majority of the official test set, as a direct consequence of training exclusively on monolingual data.