G-SHARE: A Guideline-Based Structured Reasoning Framework for Human-Factor Event Diagnosis
This paper proposes G-SHARE, a framework that operationalizes the CNNP nine-step human-factor event diagnosis guideline into a multi-stage pipeline including evidence extraction, stepwise reasoning, and post-hoc consistency repair. Evaluated on real nuclear industry data, G-SHARE significantly outperforms one-shot prompting and machine learning baselines, demonstrating the value of structured reasoning and consistency enforcement for robust diagnosis.
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[Submitted on 7 May 2026]
Title:G-SHARE: A Guideline-Based Structured Reasoning Framework for Human-Factor Event Diagnosis
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Abstract:Human-factor event diagnosis is essential for learning from operational events in nuclear power plants, yet its quality depends strongly on expert interpretation of narrative reports and guideline-based this http URL data-driven or one-shot large language model approaches often lack structured reasoning, have limited alignment with formal diagnostic guidelines, and may generate logically inconsistent conclusions. To address this issue, this study proposes G-SHARE, a guideline-based structured reasoning framework that operationalizes the CNNP nine-step human-factor event diagnosis guideline into a multi-stage diagnostic this http URL framework consists of evidence extraction, stepwise diagnostic reasoning, and post-hoc consistency repair, enabling explicit use of report evidence, intermediate rationale generation, and logical validation of diagnostic outputs. A dataset of real human-factor event reports was constructed from Chinese nuclear industry sources, and a gold-standard subset annotated by domain experts was used for evaluation. Results show that G-SHARE substantially outperforms one-shot prompting and traditional machine learning baselines, with the strongest version achieving the best overall accuracy and macro-F1. Ablation results further indicate that structured reasoning and consistency enforcement are critical to robust diagnosis, especially under weak prompting conditions. The findings demonstrate the value of transforming expert diagnostic guidelines into auditable reasoning workflows, providing a practical pathway for intelligent human-factor analysis in safety-critical industries.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.11892 [cs.CL]
(or arXiv:2607.11892v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.11892
arXiv-issued DOI via DataCite
Submission history
From: Xingyu Xiao [view email] [v1] Thu, 7 May 2026 00:36:52 UTC (18,004 KB)
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