We describe our submissions to Task A of the ArGuard 2026 shared task on harmful-content detection in Arabic memes: binary hateful-meme detection (Subtask A1) and fine-grained multilabel categorization (Subtask A2). Our A1 system is a probability-averaging ensemble of 22 fine-tuned models: 18 Qwen3-VL vision-language members (8B and 32B) trained with LoRA and scored via first-token logits, and 4 Arabic text-encoder members (MARBERTv2, AraBERTv02-twitter), combined at a fixed 0.85/0.15 modality mix. The system ranked first on Subtask A1 with 0.8234 macro F1, 2.2 points above our best single member. For A2, six LoRA-tuned VLMs with sigmoid multilabel heads, an A1-consistency mask, and flat 0.5 thresholds scored 0.3900 macro F1. Post-hoc analysis on the released gold labels shows that our single global threshold survived an unannounced 8-point drop in the share of hateful memes in the test set, and that most remaining errors in both subtasks separate benign sarcasm or humor from mockery aimed at a protected target.

We describe the iMak AI Lab submissions to the closed track of the AraSeg Shared Task 2026 on Arabic sentence segmentation, covering all four subtasks: Paragraph-Aware (PA), No-Paragraph (NP), No-Punctuation Paragraph-Aware (NOPNX-PA), and NoPunctuation No-Paragraph (NOPNX-NP). Our system treats segmentation as binary token classification with pretrained Arabic encoders finetuned only on the 128K-token AraSeg training split. The single-model recipe encodes documents in overlapping 512-token windows and trains for three times more epochs than the standard schedule, which is the largest source of improvement in our pipeline. Per-subtask ensembles are then built by greedy forward selection over a pool of seven encoder families, several seeds, and multi-variant joint-training variants. Decoding applies deterministic structural constraints and a decision threshold tuned on the development set; no open-test labels or leaderboard feedback enter any modeling decision. The systems reach blind-test F1 scores of 94.1 (PA), 91.0 (NP), 89.5 (NOPNX-PA), and 85.8 (NOPNX-NP), above the official baseline on all four subtasks. We report ablations for each design decision, including several that did not help.