We describe NLPBeasts’ submission to the Paragraph-Aware (PA) Closed track of the AraSeg 2026 shared task, which frames Arabic sentence segmentation as per-token binary boundary classification. We compare two closed-track-compliant systems trained with out any pretrained embeddings or language models: (1) a feature-based CRF using handcrafted punctuation, brackets, and paragraphs distance features, and (2) a character- and word-level BiLSTM with a linear-chain CRF decoder, both trained entirely from scratch on the shared task’s training and development splits. The feature-based CRF plateaus at a hard recall ceiling (≈82–83%) regardless of regularization strength, while the BiLSTM-CRF captures substantially more boundary cases through learned context. Our BiLSTM-CRF system, using only permitted training data with the development split reserved strictly for model selection, achieves P=86.37, R=90.51, and F1=87.60 on the blind test set. We additionally report ablations on chunk length and training hardware (CPU vs. GPU), neither of which meaningfully changes development F1, suggesting our system’s performance ceiling on this closed-track data size is data-limited rather than compute- or context-length-limited.