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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…
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…
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…
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…
BALSAM: A Platform for Benchmarking Arabic Large Language Models
Rawan Al-Matham, Kareem Darwish, Raghad Al-Rasheed +40
The impressive advancement of Large Language Models (LLMs) in English has not been matched across all languages. In particular, LLM performance in Arabic lags behind, due to data s…
Fanar: An Arabic-Centric Multimodal Generative AI Platform
Fanar Team, Ummar Abbas, Mohammad Shahmeer Ahmad +39
We present Fanar, a platform for Arabic-centric multimodal generative AI systems, that supports language, speech and image generation tasks. At the heart of Fanar are Fanar Star an…