mlsanityData to 7 Oct 2026

Which Models Perform Better in Inheritance Reasoning?

arXiv 2606.13751 ↗First posted 11 Jun 2026cs.CLcited by 0

On the map: LLM reasoning → Reasoning for agents, planning and code

Abstract (arXiv)

This paper presents the participation of team PSL in the QIAS 2026 Shared Task on Arabic Islamic inheritance reasoning. The task evaluates the ability of large language models to solve inheritance cases that require legal interpretation, multi-step reasoning, and precise numerical computation. We compare textitcommercial and textitopen-source models under a unified prompting strategy to assess their effectiveness in structured legal reasoning with minimal task-specific adaptation. Our results show a clear gap in reliability between the two model families. Commercial models demonstrate stronger performance in identifying eligible heirs, applying exclusion rules, and maintaining consistency across reasoning steps. In contrast, open-source models exhibit greater instability, particularly in cases involving dependent legal decisions and fractional share adjustments. The best performance is achieved by textitGemini 2.5 Flash, with an MRE of 0.989.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 5 of its 21 references that are in the map. First posted on arXiv 11 Jun 2026.

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5 of 21 references.

Closest ancestors · 2

Same direction, newest first

  1. 2026MAWARITH: A Dataset and Benchmark for Legal Inheritance Reasoning with LLMs8 Mar
  2. 2024OpenAI o1 System Card21 Dec

Other references · 3

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601

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  1. 2025Assessing Large Language Models on Islamic Legal Reasoning: Evidence from Inheritance Law Evaluation
  2. 2025Benchmarking the Legal Reasoning of LLMs in Arabic Islamic Inheritance Cases
  3. 2025QU-NLP at QIAS 2025 Shared Task: A Two-Phase LLM Fine-Tuning and Retrieval-Augmented Generation Approach for Islamic Inheritance Reasoning
  4. 2025CVPD at QIAS 2025 Shared Task: An Efficient Encoder-Based Approach for Islamic Inheritance Reasoning
  5. 2026CVPD at QIAS 2026: RAG-Guided LLM Reasoning for Al-Mawarith Share Computation and Heir Allocation
  6. 2026QU-NLP at QIAS 2026: Multi-Stage QLoRA Fine-Tuning for Arabic Islamic Inheritance Reasoning
  7. 2026QIAS 2026: Overview of the Shared Task on Islamic Inheritance Reasoning
  8. 2026IslamicLegalBench: Evaluating LLMs Knowledge and Reasoning of Islamic Law Across 1,200 Years of Islamic Pluralist Legal Traditions