Modern searches for physics beyond the Standard Model produce rapidly expanding literature containing heterogeneous information, including textual analyses, numerical datasets, and graphical exclusion limits. Integrating these distributed sources remains a time-consuming and manual process for physicists. We present HEP-CoPilot, a retrieval-augmented multi-agent AI framework for the exploration and interpretation of high-energy physics literature. The system unifies textual information from publications, structured experimental data from HEPData, and reconstructed physics plots within a multimodal retrieval and reasoning architecture. By combining retrieval-augmented language models with coordinated agent workflows, it enables evidence-grounded reasoning over experimental analyses and structured interpretation of collider results. We evaluate the framework on recent CMS searches for physics beyond the Standard Model. Case studies show that HEP-CoPilot can retrieve relevant measurements, reconstruct exclusion limits directly from HEPData records, and perform cross-paper comparisons of experimental constraints. This enables consistent, physics-aware comparison across analyses without manual data integration. These results demonstrate that retrieval-augmented AI systems can function as scientific co-pilots for particle physics, facilitating navigation of complex literature, structuring heterogeneous evidence, and accelerating the interpretation pipeline for new physics searches.

翻译：现代超越标准模型物理学搜索产生了快速增长的文献，其中包含异构信息，包括文本分析、数值数据集和图形化排除界限。整合这些分布式资源对物理学家来说仍然是一个耗时且需要手动完成的过程。我们提出了HEP-CoPilot，一种用于高能物理文献探索与解释的检索增强多智能体AI框架。该系统在多模态检索与推理架构内统一了来自出版物的文本信息、HEPData的结构化实验数据以及重建的物理图形。通过将检索增强语言模型与协调的智能体工作流相结合，它能够实现对实验分析的基于证据的推理以及对对撞机结果的结构化解释。我们在最近的CMS超越标准模型物理学搜索上评估了该框架。案例研究表明，HEP-CoPilot能够检索相关测量值，直接从HEPData记录重建排除界限，并进行实验约束的跨论文比较。这实现了无需手动数据整合即可跨分析进行一致的、物理感知的比较。这些结果表明，检索增强AI系统可以作为粒子物理学的科学副驾驶，促进复杂文献导航、结构化异构证据，并加速新物理搜索的解释流程。