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LLM-guided planning system boosts accuracy on nuclear regulatory documents

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an LLM-guided planning system designed to improve multi-hop reasoning over complex nuclear regulatory documents. This system frames the task as a planning problem, where an LLM agent navigates a document tree using tools like browse, read, and search, maintaining a dynamic knowledge graph. Tested on a benchmark of 200 questions related to NuScale Final Safety Analysis Report documents, the system achieved 81.5% accuracy, significantly outperforming other RAG methods like PageIndex, LightRAG, HippoRAG, and GraphRAG. AI

IMPACT This approach could enhance the efficiency and accuracy of legal and regulatory document analysis by leveraging LLMs for complex reasoning tasks.

RANK_REASON The cluster contains a research paper detailing a new method for LLM-guided planning in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM-guided planning system boosts accuracy on nuclear regulatory documents

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The cluster contains a research paper detailing a new method for LLM-guided planning in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mingyu Jeon, Bokyeong Kim, Suwan Cho, Jae Young Suh, Yonggyun Yu ·

    LLM-Guided Planning for Multi-hop Reasoning over Multimodal Nuclear Regulatory Documents

    arXiv:2606.29399v1 Announce Type: new Abstract: Reviewing nuclear regulatory documents requires multi-hop reasoning across tens of thousands of pages, where judgments depend on evidence assembled across multiple chapters. We frame this task as planning: an LLM-based agent observe…

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