This paper presents our system for Task 1 of the Daleel 2026 shared task, which assigns multiple discourse-function labels to Arabic paragraphs from editorials and debate transcripts. We fine-tune a monolingual Arabic encoder (MARBERTv2) with a label-weighted focal loss, multilabel-stratified k-fold crossvalidation, out-of-fold per-label threshold calibration, k-fold ensembling, and a genreconditioning prefix, in a two-stage protocol that first trains on the training split alone and then retrains on training+development. Internal cross-validation macro-F1 rises from 0.667 to 0.680 across the two stages. On the official test set, the system attains micro-F1 = 0.766 and macroF1 = 0.560, with markedly stronger micro-F1 on debate paragraphs (0.825) than editorials (0.633), and a smaller but consistent macro-F1 gap (0.554 vs. 0.456). Assumption is classified most reliably (F1 = 0.88–0.94) and Common Ground least reliably (F1 ≤ 0.12), mirroring the low inter-annotator agreement reported for the same category in the source English scheme. We find a ∼0.12 gap between internal cross-validation and official test macro-F1, concentrated in low-support, genre-sensitive categories. Our code is publicly available at https://github.com/EidAhmed1/daleel2026_task1_project.