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

We present NAMAA Community’s systems for the four closed-track Arabic sentence-segmentation subtasks of AraSeg 2026. Heterogeneous ensembles combining LoRA-adapted bidirectional language models, XLM-R variants, a character-level SaT model, and punctuation-aware models outperform their development-selected single-model baselines. Large weights assigned to weaker but architecturally distinct members are consistent with complementary errors, although they do not establish causality. A training-only logistic MEMM improves F1 by +0.9 and +0.6 points on the punctuation-free subtasks but degrades both punctuated ones; the same directional pattern appears on the blind split. Aligning punctuated and punctuation-free inputs reveals F1 points of oracle headroom against the contemporaneous NoPnx locks, while only 52.5% of removed tokens are covered by the restoration label inventory. Genre-proxy analysis localizes the largest degradation to scriptural text. On the blind split, in the closed track, our systems achieved F1 scores of 94.4/91.3/89.9/87.0 on PA, NP, NoPnx-PA, and NoPnx-NP, respectively, ranking 6th/3rd/4th/3rd. The same systems, entered unchanged in the open track, rank 4th/2nd/3rd/3rd. Implementation and experimental resources are publicly available.[<https://github.com/NAMAA-ORG/NAMAA-Community-AraSeg-2026>]

Encoder-only transformer models remain widely used for discriminative NLP tasks, yet recent architectural advances have largely focused on English. In this work, we present AraModernBERT, an adaptation of the ModernBERT encoder architecture to Arabic, and study the impact of transtokenized embedding initialization and native long-context modeling up to 8,192 tokens. We show that transtokenization is essential for Arabic language modeling, yielding dramatic improvements in masked language modeling performance compared to non-transtokenized initialization. We further demonstrate that AraModernBERT supports stable and effective long-context modeling, achieving improved intrinsic language modeling performance at extended sequence lengths. Downstream evaluations on Arabic natural language understanding tasks, including inference, offensive language detection, question-question similarity, and named entity recognition, confirm strong transfer to discriminative and sequence labeling settings. Our results highlight practical considerations for adapting modern encoder architectures to Arabic and other languages written in Arabic-derived scripts.