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.

Multimodal hate speech detection requires interpreting how an image and its accompanying text jointly construct a target, stance, or derogatory implication. In Arabic memes, this relation is often shaped by locally recognizable public figures, political references, visual analogies, and indirect humor, making either modality insufficient on its own. We tackle this problem in Track A of ArGuard 2026, covering binary hateful meme detection (A1) and fine-grained multi-label classification (A2). Our experiments compare jointly trained dual encoders, parameter-efficient vision–language model (VLM) adaptation, and training-free prompting. We fine-tune Qwen3-VL, Llama 3.2 Vision, and Gemma 4 with QLoRA using task-specific binary and multi-label objectives. We also build CLIP–MARBERTv2 and SigLIP–AraBERT-Twitter cross-attention systems and evaluate frozen VLMs with zero-shot prompting and in-context learning. Qwen3-VL achieves our highest A1 score (0.7610), while SigLIP–AraBERT-Twitter leads A2 (0.3851), where label sparsity and multi-label prediction remain challenging.

Fine-grained Arabic readability assessment is challenging due to the language’s rich morphology and the ordered nature of readability labels. We investigate this problem in the sentence-level Strict Track of the BAREC Shared Task 2026, which uses a 19-level readability scale. We evaluate six controlled configurations spanning engineered linguistic and morphological features, TF-IDF/SVD lexical representations, contextual transformer encoders, multi-encoder fusion, output-level ensembling, and ordinal-aware training, followed by a final refinement using a single EmbeddingGemma encoder. Contextual transformer representations provide the largest improvement over classical feature-based baselines, while multi-encoder fusion and voting yield smaller additional gains. In contrast, word-repetition and adjacent-word-swap augmentation degrades performance, indicating that these perturbations do not reliably preserve readability labels. Our strongest configuration combines EmbeddingGemma with Gaussian soft labels, KL-divergence training, expected-value decoding, class weighting, and best-checkpoint selection, achieving 84.0 QWK on the test set and 82.4 QWK on the official blind test set. Overall, the results suggest that explicitly accounting for the ordinal structure of readability labels is a promising alternative to simply increasing architectural or ensemble complexity. We further summarize the resulting design insights into a practical framework for Arabic ordinal readability modeling.

Arabic knowledge graph construction is a challenging problem due to the linguistic complexity of Arabic and the need for robust generalization across domains. This paper presents our systems for the KnowledgeGraphEval 2026 shared task on Arabic knowledge graph construction and domain adaptation, covering two subtasks: AdaptNER for cross-domain named entity recognition and relation extraction (RE). For AdaptNER, we employ an entity-span ensemble combining two BIO sequence taggers with constrained conditional random field (CRF) decoding and a GlobalPointer span classifier. Our system achieved a micro-F1 score of 76.00%, ranking first in the official evaluation. For relation extraction, we use a majority-vote ensemble of three LoRA-fine-tuned causal language models. This approach achieved a micro-F1 score of 93.48%, ranking fifth. These results demonstrate the effectiveness of model ensembling and complementary prediction strategies for Arabic named-entity recognition and relation extraction.

Large language models may generate inaccurate Islamic citations when answering religious questions. We present the Namaa Community system for Subtask 2 of IslamicEval 2026, which identifies hallucinations in Qur’anic verses (Ayah), hadith texts (matn), chains of narration (isnad), and claimed sources. We formulate the task as retrieval-grounded verification: citation spans are normalized and matched against the Qur’an and the six major hadith collections using character n-gram retrieval and edit-distance re-ranking. Type-specific verifiers assess Ayah and matn similarity, while claimed sources and isnads are checked against their parent citations. The deterministic pipeline requires no language model or GPU, and every decision is traceable to a canonical record. It achieves a macro accuracy of 0.846 on the development set, 17.9 percentage points above a per-type majority-class baseline, and 0.668 on the official blind test; we trace most of that shortfall to a submission-time configuration error and decompose the remainder by segment type. Our code and submissions are publicly available at https://github.com/NAMAA-ORG/Namaa-Community-at-IslamicEval-2026.

Arabic encompasses diverse spoken varieties with limited parallel data, making both translation and automatic evaluation difficult beyond Modern Standard Arabic. We present the NAMAA Community submissions to all three AlexandriaX-2026 subtasks: English-to-dialect dialogue translation, cross-dialect translation, and dialectal MT error detection. Our systems combine few-shot prompting, fine-tuned encoder-decoder and decoder-only models, and encoder- and LLM-based span detection. Held-out ablations show that minimum-Bayes-risk consensus can suffer from model-family imbalance; we therefore use per-dialect routing for translation and 2-of-3 character-level agreement for error-span detection. Our systems rank fourth and fifth of six in the unconstrained and constrained Subtask 1 tracks, third of four on Subtask 2, and second of six on Subtask 3. The Subtask 3 system achieves 0.5448 overlap-F1, exceeding the overall winner on span localization, with category assignment remaining the main weakness. Code and resources are publicly available.[<https://huggingface.co/collections/NAMAA-Space/namaa-at-alexandriax-2026>]