---
title: "MAWARITH: A Dataset and Benchmark for Legal — Ghaly; 2026"
description: "MAWARITH addresses a gap in LLM evaluation: rule-bound legal reasoning in Islamic inheritance law (ʿilm al-mawārīth)."
authors: ["Abdessalam Bouchekif; Shahd Gaben; Samer Rashwani; Somaya Eltanbouly; Mutaz Al-Khatib; Heba Sbahi; Mohammed Ghaly;", "Emad Mohamed"]
year: 2026
venue: "arXiv:2603.07539"
source: "https://arxiv.org/abs/2603.07539"
doi: "10.48550/arxiv.2603.07539"
arxiv: "2603.07539"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/bouchekif-2026-mawarith-dataset-benchmark-legal-inheritance/"
---
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# MAWARITH: A Dataset and Benchmark for Legal Inheritance Reasoning with LLMs

**Authors:** Abdessalam Bouchekif; Shahd Gaben; Samer Rashwani; Somaya Eltanbouly; Mutaz Al-Khatib; Heba Sbahi; Mohammed Ghaly;, [Emad Mohamed](https://md.research.elamur.ai/free/en/authors/emad-mohamed/) · **Year:** 2026 · **Venue:** arXiv:2603.07539 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2603.07539) · [PDF](https://arxiv.org/pdf/2603.07539) · [DOI 10.48550/arxiv.2603.07539](https://doi.org/10.48550/arxiv.2603.07539) · [arXiv 2603.07539](https://arxiv.org/abs/2603.07539) · [OpenAlex](https://openalex.org/W7134808274)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [9](https://md.research.elamur.ai/free/en/papers/bouchekif-2026-mawarith-dataset-benchmark-legal-inheritance/cited-by/) in our library, of the 140884 papers whose bibliographies we read

## Summary
MAWARITH addresses a gap in LLM evaluation: rule-bound legal reasoning in Islamic inheritance law (ʿilm al-mawārīth). Existing benchmarks in this area reduce cases to multiple-choice selection, which hides whether the model actually reasoned correctly. The authors built 12,500 Arabic inheritance cases following the majority juristic opinion (al-jumhūr). Ground-truth shares came from the Al-mawarith inheritance calculator, which takes structured kinship inputs. These were turned into natural-language scenarios with step-by-step solutions: heir identification, blocking (ḥajb), share assignment, adjustment through ʿawl or radd, and final allocation, each justified by classical sources. They also propose MIR-E, a weighted metric that scores each reasoning stage and reflects how errors carry forward. In zero-shot tests on six LLMs, a commercial model reached roughly 90%, while every open-source model stayed below 50%. Most failures came from early structural steps, such as missing or invented heirs and wrong blocking. The resource matters because it allows training and diagnosing full reasoning chains, not just final answers.

## Key points
- 12,500 Arabic cases covering the full set of classical kinship relations, including extended agnatic relatives across generations. Public at gitlab.com/nlpresearcher/mawarith.
- Prior benchmarks were MCQ-only: QIAS 2025 has 2,200 questions and MirathQA is built from 1,394 cases.
- The six models evaluated were Gemini-2.5-flash, Qwen3-32B, Fanar-Sadiq, GPT-oss-120b, Llama-3.3-70b and Fanar-C-2-27B.
- Adjustment was checked only on cases where the earlier steps were correct:
  - Gemini-2.5-flash erred once in 373 cases (0.27%).
  - GPT-oss-120b had the highest error rate, 23.08%.
- Recurring errors:
  - misreading the scenario;
  - wrong heirs;
  - wrong shares;
  - calling a case radd without checking whether a residuary heir exists;
  - skipping the adjustment step;
  - giving one share to a whole group ("three brothers") instead of one per person.

## Interesting ideas
- Each pipeline stage is scored only on items where all upstream stages were right, so a stage's own error rate is not blurred by inherited mistakes. — For Mova (learn): Evaluate frame or world extraction only on sentences whose UD tree matched gold. This shows which stage is really weak instead of blaming the last one.
- Gold labels come from a deterministic solver run on structured inputs, and natural-language text with explanations is layered on top, so correctness holds by construction. — For Mova (learn): Generate world states (holdings, transfers) symbolically, then verbalize them into word problems. This gives silver training and test data whose answers are guaranteed.
- A weighted multi-stage metric gives partial credit per reasoning stage and records error propagation, instead of a single right/wrong on the final number. — For Mova (answer): Score maths word problem solving separately for entities found, operations chosen and arithmetic, so a regression can be traced to one stage.
- A typical model error is applying a rule while skipping its precondition (radd assumes no residuary heir), which suggests making preconditions explicit, checkable steps. — For Mova (world): Attach explicit guard conditions to world-update rules and log their evaluation, so every rule firing is explainable and wrong firings are easy to catch.
- The authors penalize reporting one value for a group ("three brothers") when per-individual output is required, separating group mentions from individual entities. — For Mova (world): Expand counted plural mentions into individual entities when quantities are distributed, so holdings and answers are per person rather than per noun phrase.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) · [Law](https://md.research.elamur.ai/free/en/shelves/law/)

Topics: [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Legal AI](https://md.research.elamur.ai/free/en/topics/legal-ai/) · [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/)

## Institutions

- [Hamad bin Khalifa University](https://md.research.elamur.ai/free/en/universities/hamad-bin-khalifa-university/) — University, Doha, Qatar · 58 works · 11 454 citations

Also on the byline: [Nazarbayev University](https://ror.org/052bx8q98).

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{ghaly2026mawarith,
  title = {{MAWARITH: A Dataset and Benchmark for Legal Inheritance Reasoning with LLMs}},
  author = {Ghaly;, Abdessalam Bouchekif; Shahd Gaben; Samer Rashwani; Somaya Eltanbouly; Mutaz Al-Khatib; Heba Sbahi; Mohammed and Mohamed, Emad},
  year = {2026},
  doi = {10.48550/arxiv.2603.07539},
  eprint = {2603.07539},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2603.07539}
}
```

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