most citedFrom RAG to Agentic RAG for Faithful Islamic Question Answering

1 citations · 1 across the 3 of their papers we have counts for

collaborators

9 papers

cs.CV2026

When Do VLMs Help Arabic Manuscript OCR? A Cross-Dataset Study

Moshiur Farazi, Firoj Alam, Abderrahmane Maaradji +3

Vision-language models (VLMs) are increasingly being used for document understanding, yet their role in Arabic and Islamic manuscript recognition remains underexplored. To address…

cs.SD2026

WASIL: In-the-Wild Arabic Spoken Interactions with LLMs

Zien Sheikh Ali, Hamdy Mubarak, Soon-Gyo Jung +3

Large Language Models (LLMs) voice assistants are commonly built as cascaded Automatic Speech recognition (ASR) to LLM systems, where recognition errors can distort user intent. Di…

cs.CL20261 cited

From RAG to Agentic RAG for Faithful Islamic Question Answering

Gagan Bhatia, Hamdy Mubarak, Mustafa Jarrar +8

Large Language Models (LLMs) are increasingly used for Islamic question answering, where ungrounded responses may carry serious religious consequences. Yet standard MCQ/MRC-style e…

cs.CL2026

Fanar-Sadiq: A Multi-Agent Architecture for Grounded Islamic QA

Ummar Abbas, Mourad Ouzzani, Mohamed Y. Eltabakh +7

Large language models (LLMs) can answer religious knowledge queries fluently, yet they often hallucinate and misattribute sources, which is especially consequential in Islamic sett…

cs.CL2026

Fanar 2.0: Arabic Generative AI Stack

FANAR TEAM, Ummar Abbas, Mohammad Shahmeer Ahmad +34

We present Fanar 2.0, the second generation of Qatar's Arabic-centric Generative AI platform. Sovereignty is a first-class design principle: every component, from data pipelines to…

cs.CL2025

PalmX 2025: The First Shared Task on Benchmarking LLMs on Arabic and Islamic Culture

Fakhraddin Alwajih, Abdellah El Mekki, Hamdy Mubarak +3

Large Language Models (LLMs) inherently reflect the vast data distributions they encounter during their pre-training phase. As this data is predominantly sourced from the web, ther…