XING Chengliang;SONG Fei;WANG Yuhang;ZHANG Liuwan;

• 1:Qiuzhen College
• 2:Tsinghua University
• 3:Department of Physics
• 4:Tsinghua University

Abstract：

While AI shows great potential in empowering physics experiments,the highly innovative and rapidly iterating nature of these experiments poses challenges for traditional Large Language Models(LLMs).Reasoning on specific methods typically requires full-context injection of local knowledge bases,resulting in excessive context consumption and high inference costs.This study proposes an intelligent agent framework for physics experiments integrating dynamic context-aware management and collaborative reasoning.Adaptable to existing LLMs,the framework dynamically extracts and injects high-value information into the context,significantly reducing inference costs and improving efficiency without compromising reasoning quality compared to full-context methods.By supporting rapid secondary development and diverse scenario adaptation,the framework effectively overcomes computational and cost bottlenecks,offering an efficient and flexible paradigm for the automation and intelligent transformation of physics experiments.

Key Words： physics experiments;deep learning;context awareness;retrieval-augmented generation;collaborative reasoning

Abstract:

Keywords:

Foundation: 通专教育背景下大学实验物理教学内容的重构探索与实践（教育部拔尖计划2.0研究课题20211007）;; 清华大学AI赋能物理实验教学综合改革与立体化资源建设（53100100826）;清华大学自主科研计划资助（大学生学术研究推进计划20267020002）

Authors: XING Chengliang;SONG Fei;WANG Yuhang;ZHANG Liuwan;

DOI: 10.27024/j.wlygc.2026.06.25.02

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