AI-Supported Platform for System Monitoring and Decision-Making in Nuclear Waste Management with Large Language Models

TL;DR

This paper introduces an AI-powered multi-agent retrieval-augmented system that enhances regulatory compliance and safety decision-making in nuclear waste management through structured collaboration and real-time document retrieval.

Contribution

It presents a novel multi-agent RAG framework utilizing LLMs and document retrieval for improved decision accuracy in complex regulatory environments.

Findings

Regulatory Agent achieves higher relevance scores.

Safety Agent effectively manages complex risk assessments.

Agents show increased agreement and coherence over discussion rounds.

Abstract

Nuclear waste management requires rigorous regulatory compliance assessment, demanding advanced decision-support systems capable of addressing complex legal, environmental, and safety considerations. This paper presents a multi-agent Retrieval-Augmented Generation (RAG) system that integrates large language models (LLMs) with document retrieval mechanisms to enhance decision accuracy through structured agent collaboration. Through a structured 10-round discussion model, agents collaborate to assess regulatory compliance and safety requirements while maintaining document-grounded responses. Implemented on consumer-grade hardware, the system leverages Llama 3.2 and mxbai-embed-large-v1 embeddings for efficient retrieval and semantic representation. A case study of a proposed temporary nuclear waste storage site near Winslow, Arizona, demonstrates the framework's effectiveness. Results show the Regulatory Agent achieves consistently higher relevance scores in maintaining alignment with legal frameworks, while the Safety Agent effectively manages complex risk assessments requiring multifaceted analysis. The system demonstrates progressive improvement in agreement rates between agents across discussion rounds while semantic drift decreases, indicating enhanced decision-making consistency and response coherence. The system ensures regulatory decisions remain factually grounded, dynamically adapting to evolving regulatory frameworks through real-time document retrieval. By balancing automated assessment with human oversight, this framework offers a scalable and transparent approach to regulatory governance. These findings underscore the potential of AI-driven, multi-agent systems in advancing evidence-based, accountable, and adaptive decision-making for high-stakes environmental management scenarios.