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This study evaluates large language models on complex Arabic Islamic inheritance cases requiring legal interpretation and multi-step numerical reasoning, revealing a significant reliability gap between commercial and open-source architectures. Commercial models consistently outperform their counterparts in maintaining consistency across dependent decisions, with Gemini 2.5 Flash achieving the highest accuracy among tested systems. The findings highlight critical challenges for deploying AI in high-stakes domains where precision and stability are paramount over raw capability or cost efficiency.
Surse care acoperăarXiv, Analytics Vidhya, Fortune, AWS
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Which Models Perform Better in Inheritance Reasoning?

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arXiv:2606.13751v1 Announce Type: new Abstract: 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 \textit{commercial} and \textit{open-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 \textit{Gemini 2.5 Flash}, with an MRE of $0.989$.

Title:Which Models Perform Better in Inheritance Reasoning?

Abstract: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 \textit{commercial} and \textit{open-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 \textit{Gemini 2.5 Flash}, with an MRE of $0.989$.

Computation and Language (cs.CL)

arXiv:2606.13751 [cs.CL]

(or arXiv:2606.13751v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.13751 Focus to learn more arXiv-issued DOI via DataCite

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