Arabic sentence segmentation without punctuation or paragraph boundaries requires models to infer boundaries only from lexical, syntactic, and discourse context. We describe FAAMG’s submission in the closed track of AraSeg 2026 Subtask 4 (NoPnx-NP), restricted to only 174 training documents for fine-tuning. Our system is a weighted ensemble of eight models based on AraBERT, SaT, and CAMeLBERT BiLSTM-CRF. The ensemble covers a variety of regularization, encoder capacity, and sequence modeling. The resulting system achieves 86.7 document-macro F1 on the Blind Test set ranking fourth on the leaderboard. Through leave-one-out ablations we show that the ensemble contributes complementary information over the individual models.