This paper introduces resources for the computational study of scientific exegesis (Tafsir Ilmi): a structured ontology, a curated dataset of 194 scientifically relevant Quranic verses linked to 260 exegetical records from two authoritative Tafsir books, and an annotation framework that organizes scientific references by topic and sequential context. Existing Quranic resources treat verses as unstructured text, losing the logical order and causal relationships of scientific concepts documented in exegesis. To address this, we present QurSci-Onto, a three-layer ontology that categorizes verses by scientific domain, links them to authoritative Tafsir, and provides a framework for representing sequential processes through stage-based annotations. Our dataset includes page-level citations and covers 8 major scientific topics across 73 nodes. While the full corpus is tagged with broad categories and scientific topics, a specialized subset features granular node-level mappings to capture complex scientific narratives. We release QurSci-Onto as a foundational resource for Arabic semantic NLP and demonstrate that it enables significant improvements in semantic retrieval and enables multi-hop sequential reasoning capabilities over unstructured baselines.

This paper presents our submission to the AbjadGenEval shared task on AI-generated text detection in Arabic and Urdu. To address the challenges of morphologically rich and low-resource environments, we developed a composite framework leveraging monolingual specialists (AraBERTv2, CAMeLBERT-DA) and multilingual transformers. Our system achieved robust in-domain performance with Test F1-scores of 0.75 for Arabic and 0.86 for Urdu. Methodologically, we tested both raw and normalized text to distinguish whether models detect based on semantic content or on surface artifacts such as punctuation and formatting patterns. Furthermore, our cross-lingual investigations reveal directional performance differences, where Urdu-trained models achieve 0.75 F1 on Arabic, while Arabic-trained models achieve only 0.61 F1 on Urdu. Despite this difference, both directions maintained notably high recall for the machine class, indicating that the model learns cross-lingual machine detection patterns across the Perso-Arabic script. Finally, transfer performance collapsed when internal layers were frozen, demonstrating that full fine-tuning is essential for cross-lingual detection. However, the observed performance differences may partly reflect data imbalance rather than purely linguistic factors.