The article reviews how transformer models, retrieval‑grounded pipelines, and large language models are reshaping hadith computational science. It critically evaluates existing literature, highlighting uneven progress: expanded data resources and mature segmentation tasks, yet persistent issues such as narrow corpora, weak benchmark comparability, and limited reproducibility. The authors argue that hadith computation should be viewed as an evidence‑infrastructure problem requiring knowledge integration, provenance, and expert supervision, and they propose a research agenda to strengthen the field’s methodological rigor.
By Md. Ashraful Haque (Greentech Apps Foundation, United Kingdom), Riasat Islam (Greentech Apps Foundation, United Kingdom, Queen Mary University of London, London, United Kingdom)
The paper presents a bilingual (English/Arabic) benchmark for evaluating how well open-weight, locally deployable language models can accurately recall specific Hadith texts. Five models—Mistral‑7B, Gemma3‑4B, Jais‑7B, ALLaM‑7B, and Phi4‑mini‑3.8B—were tested on 3,000 Hadiths per language across six canonical Sunni collections, with each model given only a bibliographic reference and asked to reproduce the exact text. None of the 30,000 responses achieved both high lexical and semantic fidelity, and Arabic outputs performed uniformly worse than English, indicating that these models, without retrieval augmentation, cannot reliably provide verifiable religious scripture.
By Dayam Nadeem, Safdar Mustafa, Ameen Ur Rehman, Shahab Saquib Sohail, Mohammad Nadeem, Usman Naseem
Ansari is a retrieval‑grounded Islamic AI assistant that has handled over 140,000 conversations in more than 25 languages since June 2023. It uses an agentic retrieval loop where a language model searches authenticated Islamic corpora—including the Qur’an, hadith collections, fiqh encyclopedias, and tafsir sources—and answers only based on retrieved content, providing citations for verification. The paper details Ansari’s architecture, multi‑platform deployment, evaluation results (including top performance on the IslamicMMLU leaderboard and strong resistance to false premises), and lessons for faith‑sensitive LLM deployments.
By M Waleed Kadous, Amr Elsayed, Abdullah Al Nahas, Ashraf Haress
Large language models (LLMs) can generate fluent Arabic answers, yet factual errors remain difficult to detect, localize, explain, and verify. Existing hallucination benchmarks often provide response-level labels, with limited support for identifying the exact erroneous content, explaining why it is incorrect, or selecting the correct factual answer.
A new large-scale Arabic fact‑checking dataset called Arafa has been created using an automated pipeline that generates claims from Arabic Wikipedia, mutates them into counterfactuals, and validates them against supporting or refuting evidence. The dataset contains 181,976 claim‑evidence pairs labeled as supported, refuted, or not enough information, and human evaluation shows high inter‑annotator agreement and strong validation accuracy. Fine‑tuned transformer models on Arafa achieve a Macro F1‑score of 77%, demonstrating its usefulness for Arabic fact‑checking tasks.
By Christophe Khalil, Shady Elbassuoni, Rida Assaf
Visual Sanad is a new benchmark that tests vision‑language models (VLMs) for false acceptance of Qur’anic verse attributions across 2,400 images derived from 100 English Qur’an verses. The dataset includes four factual states per verse and six visual conditions, from plain text to decorative layouts, and all pairs were source‑checked and human‑audited. Experiments on 24 model‑prompt runs show a sharp split: proprietary API‑served VLMs vary from near‑perfect verification to moderate false acceptance, while recent open‑weight VLMs often default to VALID and show weak discrimination, though de‑biased prompting can reduce false acceptance by shifting thresholds toward INVALID.
"whyItMatters":"The study reveals that fluent VLMs may treat displayed religious citations as self‑authenticating unless they possess strong citation‑verification ability, exposing a culturally grounded multimodal factuality failure."
By Niaz Mohaiman Abtahi, Reyana Shahid, Evan Maksud