Sentence boundary detection in Arabic is inherently challenging due to ambiguous orthographic punctuation conventions, prolific coordinating conjunction clitics, and extensive stylistic variability across literary, religious, news, and web domains. In this paper, we present our system submission to the Closed Track of the AraSeg 2026 Shared Task. We formulate sentence boundary detection as a token-level sequence classification problem utilizing an ensemble of fine-tuned Arabic Transformer encoders: AraBERTv02, CAMeLBERT-MSA, and ARBERTv2. Our pipeline integrates an alignment-safe normalization strategy that strictly preserves gold token spans, overlapping sliding-window logit margin aggregation to counteract chunk-boundary degradation, stochastic punctuation masking during training, and calibrated positive-class loss weighting. In the official blind evaluation across eight diverse genres, our framework demonstrated strong boundary recovery across all four task tracks, securing 2nd place in both the Paragraph-Aware setting (F₁ = 95.0%) and the No-Paragraph setting (F₁ = 91.3%).