Quranic Passage Retrieval (PR) could be a challenging task due to the linguistic complexity and the semantic gap between the Modern Standard Arabic (MSA) used in daily queries and the Classical Arabic (CA) of the Holy Quran. These factors hinder conventional retrieval methods. To handle these limitations and improve multi-verse retrieval and filter the zero-answer queries, this paper proposes a four-phase neural architecture designed to enhance retrieval accuracy and contextual understanding. The methodology combines hybrid candidate retrieval using AraColBERT dense indexing and BM25 sparse retrieval, followed by semantic reranking with a CAMeLBERTmix cross-encoder. A confidence gating mechanism is then applied to filter zero-answer queries, and an AraT5-based refinement module for multi-verse aggregation. The system is evaluated on an expanded version of the Quran QA 2022 dataset. Results show improved performance compared to the baseline models, achieving a Recall@10 of 0.7024 and a Mean Average Precision (MAP@10) of 0.4947. While the system exhibits a marginal tradeoff in absolute top-rank precision (MRR = 0.5807) compared to heavily optimised single models, the proposed architecture provides a substantially more comprehensive, reliable, and context aware solution for multi-verse Quranic passage retrieval.

翻译：古兰经段落检索（PR）是一项具有挑战性的任务，原因在于其语言复杂性以及日常查询所用现代标准阿拉伯语（MSA）与古兰经古典阿拉伯语（CA）之间的语义鸿沟。这些因素制约了传统检索方法。为解决上述局限，提升多节经文检索效果并过滤无答案查询，本文提出一种四阶段神经架构，旨在增强检索准确性与上下文理解能力。该方案结合了基于AraColBERT稠密索引与BM25稀疏检索的混合候选检索，随后通过CAMeLBERTmix交叉编码器进行语义重排序。接着应用置信度门控机制过滤无答案查询，并引入基于AraT5的精化模块实现多节经文聚合。系统在扩展版Quran QA 2022数据集上进行了评估。结果表明，与基线模型相比，性能显著提升，Recall@10达到0.7024，平均精度均值（MAP@10）为0.4947。尽管与高度优化的单一模型相比，本系统在绝对顶级排名精度上略有权衡（MRR = 0.5807），但所提出的架构为多节经文古兰经段落检索提供了更全面、可靠且具有上下文感知能力的解决方案。