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)
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
TruthInsightBench is a new benchmark designed to evaluate automated scientific discovery agents by presenting them with 40 blind tasks drawn from peer‑reviewed studies across ten domains. Each task provides only a neutral objective and frozen data, withholding source conclusions, expected values, and analysis paths, forcing agents to determine which claim the data support. A fixed LLM‑based judge scores agents on evidentiary maturity across six dimensions, using 29 artifact‑grounded items, enabling fully automated, repeatable evaluation without human grading.
By Zhibo Yang, Chen Zhang, Yuewei Zhang, Hao Wang
arXiv:2609.26035v1 Announce Type: new
Abstract: Conversational agents often express answers in a uniformly confident register. We test whether expressed uncertainty, provenance-aware assertion, and e...
By Sebastian Cochinescu
arXiv:2606. 04990v2 Announce Type: replace-cross Abstract: Large language model (LLM)-based agents are evolving from passive text generators into autonomous systems capable of planning, tool use, retrieval, memory access, environmental interaction, and multi-agent collaboration.
By Yiqi Wang, Jiaqi Zhang, Taotao Cai, Zirui Liu, Qingqiang Sun, Zequn Sun, Zhangkai Wu, Manqing Dong, Mingkai Zhang, Xuefei Yin, Yanming Zhu
arXiv:2606. 04990v1 Announce Type: cross Abstract: Large language model (LLM)-based agents increasingly solve complex tasks by interacting with external tools, retrieval systems, memory modules, environments, and other agents.
By Yiqi Wang, Jiaqi Zhang, Taotao Cai, Zirui Liu, Qingqiang Sun, Zequn Sun, Zhangkai Wu, Mingkai Zhang, Yanming Zhu