We describe our systems for both StanceEval- 2026 Arabic stance-detection tracks. Our MARBERT-centered pipeline optimized the primary macro-F1 over AGAINST and FAVOR directly, combining target-aware fine-tuning, probability ensembles, and guarded transfer experiments. We report one official test sub- mission per track. The Track 1 target-routed MARBERT system scored 0.8925 on devel- opment and 0.6743 on the official test set. The Track 2 MARBERT/Fanar/Qwen3 system scored 0.8360 on development and 0.8100 on the official test set. The released Track 1 evaluation input contained only Women Driv- ing, absent from its labeled train and develop- ment targets, making the nominal seen-target track an effective target-transfer test for our system. These results show that validation pro- tocols must reflect the intended target-transfer setting; otherwise, same-target development scores can substantially overstate transfer per- formance.