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

arXiv: https://arxiv.org/abs/2603.08501 · HTML: https://mlsanity.com/paper/2603.08501/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 27 of its 51 references that are in the map.
- First posted on arXiv 9 Mar 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 27 of 27)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://mlsanity.com/paper/2208.03299/) (2022)
- [WildChat: 1M ChatGPT Interaction Logs in the Wild](https://mlsanity.com/paper/2405.01470/) (2024)
- [LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset](https://mlsanity.com/paper/2309.11998/) (2023)
- [MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning](https://mlsanity.com/paper/2205.00445/) (2022)
- [Fanar: An Arabic-Centric Multimodal Generative AI Platform](https://mlsanity.com/paper/2501.13944/) (2025)
- [SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge](https://mlsanity.com/paper/2509.07968/) (2025)
- [Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities](https://mlsanity.com/paper/2505.20099/) (2025)
- [NativQA: Multilingual Culturally-Aligned Natural Query for LLMs](https://mlsanity.com/paper/2407.09823/) (2024)
- [A Survey of Multimodal Hallucination Evaluation and Detection](https://mlsanity.com/paper/2507.19024/) (2025)
- [PalmX 2025: The First Shared Task on Benchmarking LLMs on Arabic and Islamic Culture](https://mlsanity.com/paper/2509.02550/) (2025)
- [AraHalluEval: A Fine-grained Hallucination Evaluation Framework for Arabic LLMs](https://mlsanity.com/paper/2509.04656/) (2025)
- [Sacred or Synthetic? Evaluating LLM Reliability and Abstention for Religious Questions](https://mlsanity.com/paper/2508.08287/) (2025)
- [From RAG to Agentic RAG for Faithful Islamic Question Answering](https://mlsanity.com/paper/2601.07528/) (2026)
- [Improving LLM Reliability with RAG in Religious Question-Answering: MufassirQAS](https://mlsanity.com/paper/2401.15378/) (2024)
- [Cross-Language Approach for Quranic QA](https://mlsanity.com/paper/2501.17449/) (2025)
- [MizanQA: Benchmarking Large Language Models on Moroccan Legal Question Answering](https://mlsanity.com/paper/2508.16357/) (2025)
- [QU-NLP at QIAS 2025 Shared Task: A Two-Phase LLM Fine-Tuning and Retrieval-Augmented Generation Approach for Islamic Inheritance Reasoning](https://mlsanity.com/paper/2508.15854/) (2025)
- [QuranMorph: Morphologically Annotated Quranic Corpus](https://mlsanity.com/paper/2506.18148/) (2025)
- [Can LLMs Write Faithfully? An Agent-Based Evaluation of LLM-generated Islamic Content](https://mlsanity.com/paper/2510.24438/) (2025)
- [FARSIQA: Faithful and Advanced RAG System for Islamic Question Answering](https://mlsanity.com/paper/2510.25621/) (2025)

## Built on by (most cited first, 3 of 3)

- [Islamic Large Language Models: From Knowledge Acquisition to Trustworthy and Hallucination-Resistant AI](https://mlsanity.com/paper/2606.16629/) (2026)
- [IslamicTurathBench: A Multi-Task, Multi-Discipline Benchmark for Evaluating Large Language Models on the Islamic Scholarly Tradition (turath)](https://mlsanity.com/paper/2608.04703/) (2026)
- [Hadith computational science in the age of large language models: a critical narrative review](https://mlsanity.com/paper/2608.20364/) (2026)

## Abstract (arXiv)

Large language models (LLMs) can answer religious knowledge queries fluently, yet they often hallucinate and misattribute sources, which is especially consequential in Islamic settings where users expect grounding in canonical texts (Qur'an and Hadith) and jurisprudential (fiqh) nuance. Retrieval-augmented generation (RAG) improves grounding, however, a single retrieve-then-generate pipeline is insufficient for diverse Islamic queries, including verbatim scripture, citation-grounded guidance, and rule-constrained computations such as zakat and inheritance. To address these challenges, we present Fanar-Sadiq, a bilingual Arabic-English Islamic QA system built on a multi-agent, tool-augmented architecture. It is a core component of the Fanar AI platform. Fanar-Sadiq routes Islamic queries to specialized modules within an agentic tool architecture. It supports intent-aware routing, retrieval-grounded fiqh answers with normalized citations and verification traces, exact verse lookup with quotation validation, and deterministic Sunni zakat and inheritance calculators with madhhab-sensitive branching. We evaluate the end-to-end system on public Islamic QA benchmarks and show strong effectiveness and efficiency. It is publicly accessible through an API and Web application and has received over 1.9M accesses in less than a year (https://api.fanar.qa/docs).