We present WathiqTeam’s system for the AraGenre 2026 Shared Task on hierarchical Arabic genre classification. The task requires assigning each Arabic text both one of six broad genres and one of 74 fine-grained specific genres organised beneath them. This setting is particularly challenging because many specific genres overlap in topic and vocabulary, while the hierarchy is highly asymmetric: the six broad genres contain substantially different numbers of specific children. We address these challenges with a broad-first retrieval architecture. Multilingual E5 compares Arabic texts with English genre definitions, an Arabic sentence encoder retrieves generated Arabic prototypes, and BM25 preserves exact lexical and structural cues. At the broad level, class-balanced pooling prevents parents with larger prototype pools from receiving more votes. After hard hierarchical gating, Weighted Reciprocal Rank Fusion combines the three independent rankings for the remaining specific labels. Qwen3 reranks only uncertain top-two broad decisions and is constrained by internal ensemble support. The submitted system achieved 0.4559 Hierarchical Macro F1, with 0.6250 Broad Macro F1 and 0.2869 Specific Macro F1. Component-oriented official runs show that class balancing and level-specific fusion produced most of the gain, while selective LLM reranking provided a small final improvement.

We describe our submission to ArA-DF 2026, the ArabicNLP shared task on Arabic speech deepfake detection, in which systems classify audio as real or deepfake. We participated in both tracks: Track 1 (Dialect Generalization) and Track 2 (Acoustic Robustness). Our systems investigated finetuning three large self-supervised speech encoders — WavLM, wav2vec2-XLS-R, and HuBERT — with a learnable attentive-pooling head. We evaluated each encoder separately and further combined encoders through a logistic-regression meta-classifier. In addition to improve robustness we apply an acoustic degradation pipeline (additive noise, lossy codec re-encoding, band-limiting, and gain) to 50% of the training data. For track one the best achieved result is, a fully fine-tuned WavLM with attentive pooling and augmentation, which scored 2.19% EER test set, and a deepfake-pretrained XLS-R achieves 7.80% EER on Track2 which out performed the official baseline. Our ablation study shows that augmentation and backbone choice are the primary factors, while adding correlated ensemble members yields no gain.

Large Language Models (LLMs) have significantly improved general-purpose question answering but remain prone to hallucinations in fact-sensitive domains such as religion. IslamicEval 2026 addresses these challenges by providing a fine-grained benchmark for evaluating hallucination and relevance in Arabic Islamic citations. This paper presents our approaches to three tasks: Citation Span Detection (Subtask 1), Hallucination Identification (Subtask 2), and Answer Relevance (Subtask 4). For Citation Span Detection, we formulate the problem as BIO-based token classification and fine-tune AraBERT using overlapping sliding-window encoding to process long responses effectively. For Hallucination Identification, we introduce a segment-level verification framework that augments each citation with adaptively extracted contextual evidence and citation-type information. For Answer Relevance, we combine hybrid lexical-semantic retrieval over authenticated Qur’anic and Hadith corpora with Transformer-based classification, enriching retrieved evidence with citation-specific context such as neighboring Ayat and explicit Surah–verse references. On the official test set, the proposed solutions achieved an F1 of 0.9725, an accuracy of 86.62%, and a macro-F1 of 0.7721 for Subtasks 1, 2, and 4, respectively, demonstrating the effectiveness of the proposed approaches.

Arabic sentence segmentation becomes substantially more difficult when punctuation and paragraph boundaries are unavailable. The AraSeg Shared Task 2026 provides a controlled benchmark for studying this problem under four conditions that progressively remove structural information. We present the Wathiq-SPU submission to all four closed-track conditions: Paragraph-Aware (PA), No-Punctuation Paragraph-Aware (NoPnx-PA), No-Paragraph (NP), and No-Punctuation No-Paragraph (NoPnx-NP). We formulate sentence segmentation as token-level binary boundary classification using pretrained Arabic Transformer encoders and combine calibrated predictions with track-specific ensemble strategies that adapt to the amount of available structural information. CAMeLBERT-MSA serves as our primary encoder, while the most challenging NoPnx-NP condition additionally benefits from AraELECTRA-based backbone diversity. Our experiments reveal that the most effective source of model diversity depends on task difficulty: diversity among independently trained models provides robust gains when some structural information is available, whereas backbone diversity becomes increasingly important when both punctuation and paragraph cues are removed. The selected systems achieve F1 scores of 93.4, 87.9, 90.0, and 84.8 on the PA, NoPnx-PA, NP, and NoPnx-NP tracks, respectively