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English(EN) LLM-Guided Planning for Multi-hop Reasoning over Multimodal Nuclear Regulatory Documents

LLM引导的规划系统提高了核监管文件的准确性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个LLM引导的规划系统，旨在提高对复杂核监管文件的多跳推理能力。该系统将任务构建为一个规划问题，其中LLM代理使用浏览、阅读和搜索等工具导航文档树，并维护一个动态知识图谱。在针对NuScale最终安全分析报告文件的200个问题的基准测试中，该系统达到了81.5%的准确率，显著优于PageIndex、LightRAG、HippoRAG和GraphRAG等其他RAG方法。 AI

影响 通过利用LLM处理复杂的推理任务，这种方法可以提高法律和监管文件分析的效率和准确性。

排序理由 该集群包含一篇研究论文，详细介绍了在特定领域中LLM引导规划的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

LLM引导的规划系统提高了核监管文件的准确性

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该集群包含一篇研究论文，详细介绍了在特定领域中LLM引导规划的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

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

    基于大型语言模型的规划用于多模态核监管文档的多跳推理

    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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