An Agentic Multimodal RAG Framework for Enhancing Nuclear Regulatory Review: Preserving Structural Integrity of Safety Analysis Reports

Analysis

Why This Paper Matters

Nuclear regulatory review is a critical process that ensures the safety and compliance of nuclear facilities. Safety Analysis Reports (SARs) are complex, multimodal documents containing text, tables, figures, and diagrams that must be meticulously reviewed. Traditional manual review is time-consuming and prone to human error. This paper introduces an agentic multimodal RAG framework designed to enhance the regulatory review process by preserving the structural integrity of SARs. By leveraging recent advances in retrieval-augmented generation (RAG) and agentic AI, the framework aims to automate and improve the accuracy of information extraction and analysis from these documents.

The significance of this work lies in its potential to streamline regulatory workflows, reduce human workload, and increase consistency in safety assessments. As nuclear energy continues to be a vital part of the global energy mix, ensuring rigorous and efficient regulatory oversight is paramount. The proposed framework represents a step toward AI-assisted regulatory review, which could lead to faster licensing processes and enhanced safety outcomes. Moreover, the techniques developed here may generalize to other domains requiring multimodal document understanding, such as legal, medical, and financial document analysis.

Technical Contributions

• Agentic Multimodal RAG Framework: Integrates agentic decision-making with multimodal retrieval to handle complex, multi-format documents. The agent likely orchestrates retrieval and reasoning steps to maintain structural integrity.
• Structural Integrity Preservation: Explicitly addresses the challenge of preserving the structural relationships within SARs, which is crucial for accurate interpretation.
• Evaluation and Ablations: Conducts comparative evaluations against multimodal RAG baselines and component ablations to validate the effectiveness of individual modules.
• Performance Improvement: Achieves a 6.5% accuracy improvement over Unified Multimodal RAG, demonstrating the benefit of the agentic approach.

Results

The framework achieves an overall accuracy of 69.0%, which is the highest among the evaluated systems. This exceeds the Unified Multimodal RAG baseline by 6.5 percentage points. While the absolute accuracy may seem moderate, it is important to consider the complexity of the task and the multimodal nature of the documents. The ablation studies provide insights into which components contribute most to performance, guiding future improvements. The results suggest that agentic control and structural preservation are key to handling the intricacies of regulatory documents.

Significance

This paper contributes to the growing body of work on applying AI to high-stakes, specialized domains. It demonstrates that agentic multimodal RAG can be effective for regulatory review, potentially reducing the burden on human experts and improving consistency. The focus on structural integrity is particularly relevant for documents where layout and relationships between elements carry meaning. As AI systems become more capable of understanding complex documents, their adoption in regulatory and compliance settings is likely to increase. This work also highlights the importance of evaluation in realistic scenarios, paving the way for further research on robust, domain-specific AI assistants. The moderate accuracy indicates that there is still room for improvement, but the framework provides a solid foundation for future advancements.