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)
arXiv:2609.22038v1 Announce Type: new
Abstract: We introduce QuranicMMLU, a benchmark for evaluating generative AI on Quranic Arabic across multiple dimensions of linguistic complexity. Existing Qura...
By Rawan El Ghali, Umm Kulsoom, Anas Madkoor, Dima Faris Alsaudi, Roaa Abdelmagid, Roaa Ibrahim, Raghad Mousa, Hamza Aljaji, Abdullah Khanafer, Abdallah Alkanani, Salah Feras Alali, Rawan Khaled Mohamed, Ehsaneddin Asgari
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.
arXiv:2607. 24117v1 Announce Type: new Abstract: Modern multi-agent knowledge systems increasingly accumulate knowledge through chains of autonomous transformations rather than direct retrieval.
By Ali Zahid Raja
arXiv:2608. 07508v1 Announce Type: cross Abstract: Large language models are already advisors to millions of people of faith who bring them real decisions.
By M. Waleed Kadous (iaser.ai, Faith Family Technology Network), Benjamin Olsen (Faith Family Technology Network)
The paper introduces Muslim, an Arabic voice AI platform that delivers grounded Islamic knowledge to users in real time. It combines a NeMo Arabic ASR, an OpenAI-compatible LLM endpoint, and a self-hosted TTS system, supported by a deterministic multi-source retrieval layer across six Model Context Protocol servers. The authors release fine‑tuned Arabic Islamic model artifacts, describe an account‑based metering layer that prevents abuse, and present a three‑layer observability stack that monitors GPU‑bound agent hosts, reporting latency, accuracy, and engineering trade‑offs for production deployment.
By Yahya Mohamed Elnawasany