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)
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)
arXiv:2509. 00761v4 Announce Type: replace Abstract: Large language models are increasingly deployed for legal question answering, where evaluations typically focus on multiple-choice accuracy.
By Boqin Yuan, Ziqi Wang
An LLM-based agent is a loop that reads itself. Agentic frameworks externalize identity, memory, and disposition into editable files.