We describe our submission to Task 1 of #SMM4H-HeaRD 2026: multilingual binary classification of adverse drug event (ADE) mentions in social media. Our system fine-tunes xlm-roberta-large with LoRA adapters and learned language embeddings, using two-stage training (CADEC translated domain adaptation, then five-fold cross-validation on the official training set). We ensemble the five fold checkpoints by mean logits, apply temperature scaling on the development set, and tune decision thresholds to maximize the official metric. On development, the final ensemble reaches macro-F₁ 0.788 with a global threshold and 0.796 with per-language thresholds; our best official test submission achieves macro-F₁ 0.616 (ID 678990).

With 1.5 billion people speaking over 120 major languages, India exemplifies the challenges of multilingual AI evaluation. Current multilingual VLM benchmarks suffer from unverified auto-translations, narrow task coverage, small sample sizes, and lack of culturally grounded content. We present HinTel-AlignBench, a comprehensive evaluation framework and benchmark for Hindi and Telugu vision-language models with English-aligned samples. Our framework combines semi-automated translation with human verification to generate 4k QA pairs per language across five domains: adapted English datasets (VQAv2, RealWorldQA, CLEVR-Math) and native Indic sets (JEE for STEM, VAANI for cultural grounding). Evaluation of state-of-the-art open and closed-source VLMs reveals consistent performance regression from English to Indic languages, with average drops of 8.3 points for Hindi and 5.5 points for Telugu across four of five tasks. We identify key failure modes and establish reproducible baselines for multilingual multimodal evaluation.