This paper describes the Gladiators system for Task 1 of the SMM4H 2026 shared task on binary classification of adverse drug event (ADE) mentions in multilingual social media posts. Our system fine-tunes three XLM-RoBERTa large models with different random seeds using focal loss (α=0.75, γ=2.0) and 3× positive oversampling, then averages their predicted probabilities and applies per-language threshold optimization. On the development set, our ensemble achieves a pooled binary F1 of 0.7505. On the official test set—which introduced surprise Farsi comprising 35.5% of samples—our system achieves F1 = 0.6039, above the competition mean (0.5465) and median (0.5798). We evaluated eleven approaches and document key negative results. Post evaluation, a six-model cross-regime ensembleimproved dev F1 to 0.7585.