Detecting polarization in online discourse is important for understanding social fragmentation , yet it remains difficult for Arabic due to dialect variation, informal writing, and implicit framing. In this paper, we study Arabic polarization modeling in the SemEval-2026 Task 9 (POLAR) setting, focusing on polarization detection (ST1) and polarization type classification (ST2). We compare three approaches: encoder fine-tuning, zero-shot prompting, and retrieval-augmented in-context learning (RAG-ICL), across six Arabic encoders and different LLMs. For ST1, RAG-ICL with Gemma-3-27b-it achieves the best result (test macro F1 = 0.83), while remaining competitive with the best fine-tuned encoder (0.82), and substantially outperforming zero-shot prompting. For ST2, a pipeline that first applies the best ST1 encoder as a hard filter and then performs RAG-ICL achieves a macro F1 = 0.62. Prompt-language effects are model-and task-dependent, with some settings doing better with English prompts and others with Arabic prompts. Chain-of-thought, self-refinement, and contrastive prompting do not outperform standard RAG-ICL.

We present ASCAT (Arabic Scientific Corpus for Advanced Translation), a high-quality English-Arabic parallel benchmark corpus designed for scientific translation evaluation constructed through a systematic multi-engine machine translation and expert post-editing pipeline. Unlike existing Arabic-English corpora that rely on short sentences or single-domain text, ASCAT targets full scientific abstracts averaging 125.3 words (English) and 111.78 words (Arabic), drawn from five scientific domains: physics, mathematics, computer science, quantum mechanics, and artificial intelligence. Each abstract was translated using three complementary architectures generative AI (Gemini), transformer-based models (Hugging Face quickmt-en-ar), and commercial MT APIs (Google Translate, DeepL) and subsequently post-edited by domain experts at the lexical, syntactic, and semantic levels. The resulting corpus contains 67,293 English tokens and 60,026 Arabic tokens, with an Arabic vocabulary of 17,604 unique words reflecting the morphological richness of the language. We benchmark three state-of-the-art LLMs on the corpus GPT-4o-mini (BLEU: 37.07), Gemini-3.0-Flash-Preview (BLEU: 30.44), and Qwen3-235B-A22B (BLEU: 23.68) demonstrating its discriminative power as an evaluation benchmark. ASCAT addresses a critical gap in scientific MT resources for Arabic and is designed to support rigorous evaluation of scientific translation quality and training of domain-specific translation models.

Arabic information retrieval increasingly relies on multi-stage pipelines in which a fast first-stage retriever produces candidate passages and a neural reranker refines relevance. While transformer cross-encoders deliver strong effectiveness through joint query–passage encoding, multilingual rerankers achieve competitive performance on Arabic benchmarks. However, systematic analysis of calibration, robustness, and deployment behavior in Arabic-specific settings remains limited. We present GATE-Reranker, a compact Arabic cross-encoder initialized from an Arabic semantic embedding backbone and fine-tuned on large-scale mMARCO-style Arabic triplets. The model scores each query–passage pair via full self-attention and a lightweight regression head, enabling plug-and-play second-stage reranking for Arabic search and RAG systems. We evaluate on three Arabic benchmarks covering binary relevance discrimination, controlled multi-negative reranking, and large-scale mMARCO evaluation. While remaining competitive with strong multilingual rerankers in ranking effectiveness, GATE-Reranker demonstrates significantly improved calibration and discriminative behavior. These properties translate into more reliable downstream performance in retrieval and RAG pipelines, while maintaining low GPU memory and latency on a Tesla T4.

This paper presents Ketaba-OCR-LoRA, a system developed for the NakbaNLP 2026 Shared Task on Arabic Manuscript Understanding (Subtask 2), which targets the transcription of the historically significant Omar Al-Saleh Memoir Collection written in Ruq’ah and Naskh scripts. We propose a parameter-efficient adaptation of a publicly available pretrained Arabic-English Handwritten Text Recognition (HRT) model, originally trained on handwritten corpora including the Muharaf dataset. Instead of adapting general Vision-Language Models from scratch, we fine-tune the HRT backbone using Low-Rank Adaptation (LoRA) and 4-bit quantization (QLoRA), reducing memory requirements from 40GB to approximately 8GB. Our final submission combines multiple model variants through a novel Linear+Boost weighted ensemble strategy. Our approach achieves a CER of 0.0819 and WER of 0.2588 on the blind test set (per-line evaluation), ranking 1st on per-line evaluation; on the official corpus-wide leaderboard, we rank 3rd (CER 0.0938, WER 0.2996). This work demonstrates that specialized pretrained HRT models substantially outperform general-purpose Vision-Language Models for Arabic manuscript transcription, and that parameter-efficient fine-tuning provides a practical and reproducible approach for low-resource cultural heritage digitization.

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

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.

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.

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]

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]

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.