This paper describes the system submitted for the MWE 2026 Shared Task (AdMIRe 2.0 Subtask A). The submission focused on a text-centric approach, reframing the idiom-image alignment task as a sentence-pair classification problem using mBERT (Multilingual BERT). The submitted system relied on full fine-tuning using only the English training data, achieving a Top-1 Accuracy of approximately 0.30 on the blind test set. Following the evaluation phase, significant limitations were identified in the cross-lingual generalization of the base model. In a post-evaluation study, the backbone was upgraded to XLM-RoBERTa-Large-XNLI, incorporating Low-Rank Adaptation (LoRA) and utilizing the full multilingual dataset with hard negative mining. These improvements boosted the accuracy to 0.41, demonstrating the necessity of NLI-specific pre-training and parameter-efficient tuning for MWE-aware multimodal tasks.