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.

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