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AGS-KSU at QIAS 2026: A Comparative Study of Prompting and LLM Approaches for Structured Islamic Inheritance Reasoning

This paper describes our submission to the QIAS 2026 shared task on structured Islamic inheritance reasoning, based on the MAWARITH benchmark (Bouchekif et al., 2026). The task requires multi-step structured prediction for Arabic inheritance cases, including heir identification, blocking, share assignment, adjustment detection, and final distribution, evaluated with the MIR-E metric. We compare four system configurations: a QLoRA fine-tuned Qwen2.5-3B baseline, a multi-stage Fanar-Sadiq pipeline with deterministic validation and post-processing, and two GPT-5.4 prompting setups. On the official test set, the best result was achieved by GPT-5.4 with explicit inheritance rules and development examples used as in-context demonstrations, reaching a MIR-E score of 0.84, compared with 0.76 for a minimal-prompt GPT-5.4 variant. These results suggest that explicit rule conditioning and in-context demonstrations can improve performance in this setup. Since the compared systems vary in model family and prompting strategy, the findings should be interpreted as a comparison of task configurations rather than a controlled model-only comparison.