Modern visual document retrieval (VDR) models remain strongly English-centric, leaving Arabic substantially under-served in this rapidly advancing field. To address this gap, we introduce DEAM, the first open Arabic visual document retrieval dataset explicitly designed for fine-tuning and evaluating multimodal retrievers on Arabic. DEAM is built through a human-in-the-loop construction pipeline with annotators community annotators, combining Arabic document rendering, query creation, validation, and retrieval-oriented hard-negative mining. The dataset comprises18,275 rendered Arabic documents spanning nine source domains and 82,727 contrastive retrieval triplets, each including five mined hard negatives and one easy negative for contrastive fine-tuning. Zero-shot evaluation of 27 retrievers shows that multilingual text retrievers outperform visual retrievers on Arabic by 13.6 nDCG@10, except on layout-rich news pages. DEAM establishes a public foundation for Arabic visual document retrieval.https://huggingface.co/collections/NAMAA-Space/deam-for-arabic-multimodal-retrieval

Robust Arabic speech processing must accommodate dialectal variation, multilingual code-switching, and distribution shift. We present NAMAA Community’s systems for three NADI 2026 speech subtasks: Mixed-Dialect Arabic ASR, Code-Switched Arabic ASR, and 20-way Spoken Arabic Dialect Identification. For Mixed-Dialect ASR, an Arabic-specific Conformer–Transformer achieves 0.45 word error rate (WER) on the blind set, outperforming a development-selected normalization and consensus pipeline at 0.49. For Code-Switched ASR, two-stage LoRA adaptation of the Tunisian Whisper-derived FarukSTT checkpoint, with multilingual tokenization and unconstrained decoding, yields 20.38% WER and 8.06% character error rate (CER) on validation and 16.98% blind-test WER. For dialect identification, a four-model ensemble reaches 0.950 development accuracy but 0.31 on the out-of-domain blind set. Label-free class-spread and inter-model agreement diagnostics identify an outlying component; removing it improves blind accuracy to 0.36, and a refined three-model submission reaches 0.38. Across Subtasks 1.2 and 2, development-selected calibration did not reliably transfer to blind data, although the available comparisons do not isolate the causal effect of individual components.