Extracting structured medical decisions fromICU discharge summaries is hard because oflong documents, severe category imbalanceacross nine DICTUM decision types, and afairness-aware evaluation that penalizes incon-sistent performance across demographic sub-groups. We present our system for the MedEx-ACT 2026 shared task (Elgaar et al., 2026),which fine-tunes BiomedBERT with a com-posite loss combining label-smoothed cross-entropy, a soft token-F1 auxiliary term, andR-Drop regularization. At inference time weapply a deterministic ensemble: half-offsetsliding-window augmentation across four win-dow configurations, dual-branch logit aggrega-tion from the same checkpoint, per-categorylength calibration on the Anchor Branch, andsparse routing of categories 4 and 7 to a context-weighted specialist branch motivated by theirunusual span-length distributions. Adding R-Drop improved validation Overall_F1 by 1.24points over the CE + soft-F1 baseline, with alarger 1.70-point gain on Worst-Group F1. Ourbest submission achieves Span F1 of 0.4900,Token F1 of 0.6796, and an official Overall_F1of 0.5724, with the African American subgroupas the Worst-Group bottleneck at Base_Score0.5601

This paper introduces ArtELingo, a new benchmark and dataset, designed to encourage work on diversity across languages and cultures. Following ArtEmis, a collection of 80k artworks from WikiArt with 0.45M emotion labels and English-only captions, ArtELingo adds another 0.79M annotations in Arabic and Chinese, plus 4.8K in Spanish to evaluate “cultural-transfer” performance. 51K artworks have 5 annotations or more in 3 languages. This diversity makes it possible to study similarities and differences across languages and cultures. Further, we investigate captioning tasks, and find diversity improves the performance of baseline models. ArtELingo is publicly available at ‘www.artelingo.org‘ with standard splits and baseline models. We hope our work will help ease future research on multilinguality and culturally-aware AI.