标题： 大型物理模型：与大型语言模型和基础模型协作的方法 显示英文标题

标题： Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models

摘要： 本文探讨了理念并提供了开发和评估物理专用大规模人工智能模型的潜在路线图，我们称之为大型物理模型（LPMs）。 这些模型基于如大型语言模型（LLMs）等基础模型——在广泛数据上训练——针对物理研究的需求进行定制。 LPMs可以独立运行，也可以作为集成框架的一部分。 该框架可以包含专业工具，包括用于数学运算的符号推理模块、用于分析特定实验和模拟数据的框架，以及用于综合理论和科学文献的机制。 我们首先考察物理学界是否应该积极开发和优化专用模型，而不是仅仅依赖商业LLMs。 然后，我们概述了如何通过物理学、计算机科学和科学哲学专家之间的跨学科合作来实现LPMs。 为了有效整合这些模型，我们确定了三个关键支柱：开发、评估和哲学反思。 开发侧重于构建能够处理物理文本、数学公式和多样化物理数据的模型。 评估通过测试和基准测试来评估准确性和可靠性。 最后，哲学反思包括对LLMs在物理学中的更广泛影响的分析，包括它们生成新的科学理解的潜力，以及在研究中可能出现的新协作动态。 受粒子物理学实验合作组织结构的启发，我们提出了类似地跨学科和协作的方法来构建和优化大型物理模型。 此路线图提供了具体目标，定义了实现目标的路径，并识别了必须解决的挑战，以实现物理专用的大规模人工智能模型。 显示英文摘要

摘要： This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs). These models, based on foundation models such as Large Language Models (LLMs) - trained on broad data - are tailored to address the demands of physics research. LPMs can function independently or as part of an integrated framework. This framework can incorporate specialized tools, including symbolic reasoning modules for mathematical manipulations, frameworks to analyse specific experimental and simulated data, and mechanisms for synthesizing theories and scientific literature. We begin by examining whether the physics community should actively develop and refine dedicated models, rather than relying solely on commercial LLMs. We then outline how LPMs can be realized through interdisciplinary collaboration among experts in physics, computer science, and philosophy of science. To integrate these models effectively, we identify three key pillars: Development, Evaluation, and Philosophical Reflection. Development focuses on constructing models capable of processing physics texts, mathematical formulations, and diverse physical data. Evaluation assesses accuracy and reliability by testing and benchmarking. Finally, Philosophical Reflection encompasses the analysis of broader implications of LLMs in physics, including their potential to generate new scientific understanding and what novel collaboration dynamics might arise in research. Inspired by the organizational structure of experimental collaborations in particle physics, we propose a similarly interdisciplinary and collaborative approach to building and refining Large Physics Models. This roadmap provides specific objectives, defines pathways to achieve them, and identifies challenges that must be addressed to realise physics-specific large scale AI models.