We describe our submission to the BEA 2026 Shared Task on Multilingual Lexical Complexity Prediction. The system fine-tunes XLM-RoBERTa Large separately for Spanish, German, and Chinese, feeding each instance as a flat concatenation of the source word, its sentential context, an English clue, and the English target word. Training uses z-score label normalization and two independent runs thatdiffer in learning rate, scheduler, and random seed; a weighted ensemble of their predictions (0.6/0.4) consistently reduces variance on the validation set. On the official test set the system scores RMSE = 1.170 and Pearson = 0.812.