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).
Figure 2: Our multi-agent architecture . A hybrid query classifier selects among (i) tool calls, (ii) deterministic calculation, (iii) document-grounded retrieval QA, and (iv) Quranic retrieval routes, before assembling the final response with references.
Work
Calc.
Quran
NL2SQL
Evidence
Tools
Ours (Fig. 2 )
✓
✓
✓
✓
✓
Karpas et al. (2022) (MRKL)
✗
✗
✗
✓
✓
Yao et al. (2023) (ReAct)
✗
✗
✗
✓
✓
Schick et al. (2023) (Toolformer)
✗
✗
✗
✓
✓
Asai et al. (2024) (Self-RAG)
✗
✗
✗
✓
✗
Yan et al. (2024) (CRAG)
✗
✗
✗
✓
✗
Table 1: Comparison against widely-recognized tool/agent/RAG architectures. Calc. refers to explicit rule engines. Quran denotes verse-anchored retrieval distinct from generic document search.
Dataset (Ref.)
Format
Lang
Size
Metric(s)
PalmX ( Alwajih et al., 2025 )
MCQ
ar
1,000
Acc
QIAS (T1) ( Bouchekif et al., 2025 )
MCQ
ar
1,000
Acc
IslamTrust ( Lahmar et al., 2025 )
MCQ
ar+en
406
Acc
IslamicFaithQA ( Bhatia et al., 2026b )
GenQA
ar+en
3,810
Acc (LLM-J)
FatwaQA ( SahmBenchmark, 2025 )
GenQA
ar
2,000
Acc (LLM-J)
Table 2: Evaluation datasets used in this work. Lang: ar =Arabic, en =English. GenQA: generative question answering. LLM-J: LLM-judge.
Dataset
GPT-4.1
GPT-5
G3-F
G3-P
ALLaM
Fanar
Ours
PalmX
52.9
82.3
81.2
84.4
45.5
72.5
85.5
QIAS T1
89.2
93.0
91.5
94.5
52.4
63.5
72.2
IslamTrust
94.7
95.2
94.8
95.6
57.4
83.2
94.2
IslamicFaithQA
41.4
51.2
53.4
56.6
42.7
48.2
65.4
FatwaQA
32.3
63.6
54.6
67.0
31.5
44.5
65.1
Average
62.1
77.1
75.1
79.6
45.9
62.4
76.5
Table 3: Accuracy (%) across benchmarks. G3-F: Gemini-3-Flash, G3-P: Gemini-3-Pro.
Table 6: Prayer time calculation methods and their angular parameters. The Fajr angle determines when morning twilight begins, while the Isha angle marks when evening twilight ends.
Figure 3: Inheritance calculation workflow. Disputed cases return parallel outcomes instead of collapsing to a single ruling.
Heir
Conditions
Share
Husband
No children
1/2
Husband
Has children
1/4
Wife
No children
1/4
Wife
Has children
1/8
Father
Has children/grandchildren
1/6
Mother
Has children/grandchildren
1/6
Appendix
Table 7: Representative fixed share (Fard) allocations from Quranic specifications.
Fanar
NL2SQL
Task
N
Correct
Wrong
Total
Acc
Correct
Wrong
Total
Acc
Analytical/Retrieval
0
39
23
62
62.90
62
0
62
100.00
Analytical/Retrieval
1
50
12
62
80.65
62
0
62
100.00
Analytical/Retrieval
5
59
3
62
95.16
62
0
62
100.00
Counting
0
38
36
74
51.35
57
17
74
77.03
Counting
1
59
15
74
79.73
66
8
74
89.19
Appendix
Table 8: Execution-based benchmark accuracy (%) comparing Fanar (with thinking) vs. the specialized NL2SQL model on queries sampled from Fanar usage logs. Totals: analytical/retrieval n=62 , counting n=74 .
Question answering studies how AI systems interpret questions and produce accurate, evidence-grounded answers across text, dialogue, and multimodal data.
Large language models (LLMs) are increasingly used for knowledge-intensive question answering, including religious and legal questions. Islamic knowledge is a particularly demanding setting: answers are expected to be grounded in authoritative sources, citations must be exact, Arabic varieties differ substantially from the language of classical sources, and legitimate jurisprudential disagreement must be represented rather than collapsed into a single answer. This survey reviews the emerging field of Islamic LLMs and trustworthy Islamic AI. We organize the literature around Arabic NLP and Arabic-centric LLMs, Islamic NLP resources, Qur'anic question answering, Islamic knowledge benchmarks, retrieval-augmented generation, Islamic legal reasoning, inheritance reasoning, hallucination evaluation, and trustworthiness. We argue that fluency in Arabic is not sufficient for Islamic AI. Reliable systems require curated sources, retrieval and verification modules, citation-aware generation, madhhab-aware reasoning, human expert evaluation, and benchmarks that measure not only answer accuracy but also faithfulness, source validity, and reasoning quality. The survey concludes with a research agenda for hallucination-resistant Islamic AI systems.
Large language models are increasingly consulted for Islamic knowledge, yet no comprehensive benchmark evaluates their performance across core Islamic disciplines. We introduce IslamicMMLU, a benchmark of 10,013 multiple-choice questions spanning three tracks: Quran (2,013 questions), Hadith (4,000 questions), and Fiqh (jurisprudence, 4,000 questions). Each track is formed of multiple types of questions to examine LLMs capabilities handling different aspects of Islamic knowledge. The benchmark is used to create the IslamicMMLU public leaderboard for evaluating LLMs, and we initially evaluate 26 LLMs, where their averaged accuracy across the three tracks varied between 39.8% to 93.8% (by Gemini 3 Flash). The Quran track shows the widest span (99.3% to 32.4%), while the Fiqh track includes a novel madhab (Islamic school of jurisprudence) bias detection task revealing variable school-of-thought preferences across models. Arabic-specific models show mixed results, but they all underperform compared to frontier models. The evaluation code and leaderboard are made publicly available.
Ali Abdelaal, Mohammed Nader Al Haffar, Mahmoud Fawzi +1
Large language models can generate fluent responses to Islamic questions while introducing factual errors that are difficult to identify. This paper presents our system for HalluScoring 2026 Task 2.1, Islamic Hallucination Detection and Find the Truth. The task requires a unified two-step prediction: determining whether an Arabic answer generated by an LLM is hallucinated and selecting the verified answer from six closely related candidate options. We use the Islamic knowledge dataset provided by the shared task, which contains 600 question--answer instances, including 341 hallucinated and 259 non-hallucinated answers. Our system is based on the fine-tuned google/gemma-4-12B-it model and uses deterministic decoding during inference. The generated outputs are normalized to extract the hallucination label and the selected option. The system achieves a Macro-F1 score of 0.928 and a label accuracy of 0.935 for hallucination detection, together with an option accuracy of 0.895 for answer selection. These results yield a combined score of 0.912, demonstrating strong performance across both stages of the task. The lower option-selection accuracy indicates that distinguishing the verified answer from plausible alternatives remains more challenging than detecting hallucinated responses.