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 t
The QIAS 2026 Shared Task provides a timely evaluation benchmark for LLMs in complex legal reasoning, highlighting current capabilities and limitations.
This research provides a clear quantitative assessment of commercial versus open-source LLM performance in specialized, high-stakes legal domains like Islamic inheritance, which requires intricate multi-step reasoning.
The explicit performance gap between model types for legal interpretation and precise numerical computation signals areas where current AI capabilities are either robust or significantly lacking.
- · Commercial LLM developers
- · Legal tech sector
- · Jurisdictions adopting AI in legal aid
- · Open-source LLM developers (for current legal applications)
- · Traditional legal research methods (in specific use cases)
Further investment and focus will be directed towards improving LLM accuracy in complex legal and numerical tasks.
There will be increased demand for specialized legal datasets and benchmarks to train and validate AI models for specific legal systems.
The development of AI-powered legal interpretation tools could democratize access to legal services in complex domains, particularly in regions where such expertise is scarce.
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