The AUC DL Lab system addresses the AraSeg 2026 shared task on Arabic sentence segmentation across four conditions: paragraph-aware (PA), no-paragraph (NP), no-punctuation paragraph-aware (NOPNX-PA), and no-punctuation no-paragraph (NOPNXNP), in both the closed and open tracks. We cast segmentation as imbalanced word-level boundary classification and pair fine-tuned Arabic encoders with two complementary architectures: HDP-GRAPH, which models syntactic structure via dependency-graph message passing, and a quantized decoder-only LLM using Just Pass Twice (JPT) with QLoRA for contextual boundary classification. Predictions are calibrated and combined through differential-evolution ensemble weighting. On the blind test set, our closed track systems reach 94.5, 90.8, 89.2, and 86.0 F1 (ranking 5th, 6th, 6th, 7th), and our open-track systems reach 94.6, 90.9, 89.7, and 86.0 F1 (ranking 3rd, 4th, 4th, 5th) on PA, NP, NOPNX-PA, and NOPNX-NP, respectively. Architectural diversity grows more valuable as punctuation and paragraph cues are removed, while external data yields only marginal gains once in-domain coverage is adequate.