We present our closed-track systems for both subtasks of Daleel 2026, which targets Arabic argumentative discourse unit (ADU) mining over editorials and competitive debates. Task 1 is an imbalanced multi label paragraph classification problem, and Task 2 requires character-level detection and classification of ADU spans. For Task 1 we investigate two parameter-efficient decoder-LLM formulations: joint label generation with Qwen3.5-9B and independent label scoring with ALLaM-7B. The latter decomposes each paragraph into six explicit Yes/No decisions, giving every label direct supervision and a separate decision score; it achieves 0.6714 Macro-F1 on the evaluation phase and ranked 4th, while ablations show that additional class-balanced weighting degrades performance. For Task 2 we combine task-adaptive pretraining, Beginning-Inside-Outside Conditional Random Field (BIO-CRF) sequence labeling, boundary-jitter augmentation, character-space ensembling, and span post-processing over AraBERT-large, achieving an official F1 of 0.7523 (0.7931 on debates, 0.6417 on editorials) and ranking second. Across both tasks, our results support explicit label-wise supervision and span-level ensemble decisions as effective ways to handle label imbalance and boundary noise without aggressive reweighting, while short spans, rare classes, and genre variation remain challenging.