AraGenre 2026 requires hierarchical classification of Arabic texts into evaluation-specific genres unseen during training, using supplied English definitions and taxonomy information. We present a transductive zero-shot system combining multilingual retrieval over multi-view label representations with per-genre z-normalization, calibrated Qwen3-8B candidate reranking, sibling-focused reranking, metric-aware macro-F1 decoding, and margin-gated shortlist widening. On the official 27,972-instance test set, the system achieves 0.5800 Hierarchical Macro F1, comprising 0.749 broad and 0.411 specific macro F1. Per-genre normalization provides the largest build-up improvement (+0.095), while an encoder-only diagnostic attributes +0.082 to LLM reranking. Calibrated log-probability fusion, multi-view labels, and decoding refinements provide further complementary gains.

AraGenre 2026 is a shared task on hierarchical, definition-guided Arabic genre classification under limited supervision and substantial distribution shift. Systems assign each text a broad label and a specific label using an inventory of six broad branches and 74 defined final genres. The final benchmark contains 27,972 source-derived Arabic segments, spanning Modern Standard Arabic, Classical Arabic, and dialectal material, while the released labelled examples are small and primarily synthetic. Final specific classes are absent from the released labelled data, but their English names and definitions are provided. The task received 46 registrations and 373 submissions, with 17 ranked final entries and 11 system description papers. Thaka achieved the highest official Hierarchical Macro F1 at 73.52, followed by HP at 71.69 and NAMAA-1 at 70.13. Broad classification was consistently stronger than specific classification. The documented systems frequently combine label semantics with multilingual retrieval, LLM judging, pseudo-labelling, reranking, or calibration. We clarify the resource-driven taxonomy and inherited gold labels, distinguish combined distribution shift from isolated unseen-label effects, and provide systematic validation of the final evaluation data.