We describe SynAnchor, Nasaq’s definition-guided system for AraGenre 2026. The task requires hierarchical Arabic genre classification under disjoint specific-genre inventories across training, development, and test, making generalization to unseen genres a central challenge. SynAnchor addresses this setting by generating Arabic anchor texts from genre definitions using GPT-4o and embedding them with BGE-M3 for similarity-based retrieval. High-confidence cases are classified directly, while ambiguous inputs are selectively adjudicated by GPT-5 among the three highest-ranked candidate genres. On the official test set of 27,972 texts spanning 74 unseen specific genres, SynAnchor achieves a Hierarchical Macro-F1 of 0.6006 on Codabench, compared with 0.2472 for the organizers’ definition-embedding baseline (approximately 2.4). Approximately 25% of test instances are resolved using retrieval alone, without LLM adjudication. The results suggest that combining definition-guided Arabic anchor synthesis with selective LLM reasoning can support hierarchical genre classification under changing label inventories.