We present Team TIET’s systems for two shared tasks at #SMM4H-HeaRD 2026: Task 5 (detection of patient metadata in SARS-CoV-2 sequencing papers) and Task 1 (multilingual adverse drug event detection across six languages plus an unseen Farsi subset). For Task 5 we explore iterative LLM prompting followed by fine-tuning BiomedBERT-base with weighted cross-entropy loss and probability threshold optimization, achieving F1 = 0.760 on the official test set (above the competition mean of 0.729). For Task 1 we fine-tune XLM-RoBERTa-base with a combined language- and class-balanced sampling strategy and per-language threshold tuning, achieving macro F1 = 0.497 overall (0.608 excluding the unseen Farsi subset). We report empirical findings on BERT+LLM ensemble failure with bimodal probability distributions, the superiority of base over large model variants under limited data, and the importance of language-balanced gradient contribution in multilingual classification.

We adapt the AutoARGUE framework (Walden et al., 2026) for Task A.2 of RAG4Reports 2026, which requires ranking 57 report generation systems across 68 topics using automated evaluation. The RAGTIME-1 corpus poses a fundamental challenge: all nugget annotations use a no-reference-doc sentinel rather than ground-truth document citations, rendering the original citation-relevance gating inoperable. We address this with three adaptations: automatic sentinel detection with forced direct LLM-based nugget matching; a WEAK POSITIVE partial credit mechanism for sentences that correctly answer nuggets but lack attesting citations; and a report-level request alignment check. Our nugget_coverage_weighted metric achieves the highest topic-level Pearson correlation (r=0.599) of any non-coordinator submission, closely approaching the coordinator baseline (r=0.607).