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Abstract <p>Integrating Large Language Models (LLMs) into high-energy physics (HEP) drives a paradigm shift in how researchers design experiments, analyze data, and automate complex workflows. A survey reveals various scenarios in which LLMs utilize pre-trained models through the Retrieval-Augmented Generation (RAG) architecture to meet their requirements. It analyzes our experimental scenario to show how the RAG architecture helps improve an administrative manual for the segment of sever network. Finally, it describes possible directions for future developments.</p>

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Large Language Models in High Energy Physics (Succinct Survey) and Directions of Future Developments

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • A. E. Shevel,
  • A. A. Oreshkin,
  • A. V. Shvetsov,
  • A. V. Naikov

摘要

Abstract

Integrating Large Language Models (LLMs) into high-energy physics (HEP) drives a paradigm shift in how researchers design experiments, analyze data, and automate complex workflows. A survey reveals various scenarios in which LLMs utilize pre-trained models through the Retrieval-Augmented Generation (RAG) architecture to meet their requirements. It analyzes our experimental scenario to show how the RAG architecture helps improve an administrative manual for the segment of sever network. Finally, it describes possible directions for future developments.