We present a computationally efficient ap- proach for detecting AI-generated Arabic text as part of the AbjadGenEval shared task. Our method combines Supervised Con- trastive Learning with a Stacking Ensemble of AraBERT and XLM-RoBERTa models. Our training pipeline progresses through three stages: (1) standard fine-tuning without con- trastive loss, (2) adding supervised contrastive loss for better embeddings, and (3) further fine-tuning on diverse generation styles. On our held-out test split, the stacking ensemble achieves F1=0.983 before fine-tuning. On the official workshop test data, our system achieved 4th place with F1=0.782, demonstrating strong generalization using only encoder-based trans- formers without requiring large language mod- els. Our implementation is publicly available