MAWARITH: A Dataset and Benchmark for Legal Inheritance Reasoning with LLMs

TL;DR

MAWARITH introduces a comprehensive dataset and benchmark for legal inheritance reasoning with LLMs, enabling models to perform complex, multi-step legal reasoning in Arabic inheritance cases with step-by-step solutions.

Contribution

The paper presents MAWARITH, a large-scale dataset supporting full reasoning chains in inheritance law, and proposes MIR-E, a multi-stage evaluation metric for detailed reasoning assessment.

Findings

Gemini-2.5-flash achieves ~90% MIR-E score.

Other LLMs remain below 50% MIR-E.

Error analysis reveals common failure patterns.

Abstract

Islamic inheritance law ('ilm al-mawarith) is challenging for large language models because solving inheritance cases requires complex, structured multi-step reasoning and the correct application of juristic rules to compute heirs' shares. We introduce MAWARITH, a large-scale annotated dataset of 12,500 Arabic inheritance cases for training and evaluating models on the full reasoning chain: (i) identifying eligible heirs, (ii) applying blocking (hajb) and allocation rules, and (iii) computing exact inheritance shares. Unlike prior datasets that restrict inheritance case solving to multiple-choice questions, MAWARITH supports the full reasoning chain and provides step-by-step solutions, including intermediate legal decisions and justifications based on classical juristic sources and established inheritance rules, as well as exact share calculations. To evaluate models beyond final-answer accuracy, we propose MIR-E (Mawarith Inheritance Reasoning Evaluation), a weighted multi-stage metric that scores key reasoning stages and captures error propagation across the pipeline. We evaluate six LLMs in a zero-shot setting. Gemini-2.5-flash achieves about 90% MIR-E on both validation and test, while Fanar-C, Fanar-Sadiq, LLaMA 3, and Qwen 3 remain below 50%. Our error analysis identifies recurring failure patterns, including scenario misinterpretation, errors in heir identification, errors in share allocation, and missing or incorrect application of key inheritance rules such as 'awl and radd. The MAWARITH dataset is publicly available at https://gitlab.com/islamgpt1/qias_shared_task_2026.