用自然语言驱动的智能体实现对撞机物理全流程自动化
An End-to-end Architecture for Collider Physics and Beyond
- 通过自然语言+物理符号指令，自动完成从拉格朗日量到最终结果的全链路计算
- 在多种粒子物理场景中成功复现文献结果，包括大尺度参数扫描与排除限分析
- 适合高能物理研究者、自动化科研工具开发者及跨学科科学计算需求者
我们提出迄今为止首个能够执行对撞机现象学全流程任务的语言驱动智能体系统，采用解耦且领域无关的架构，实现高能物理现象学的自主研究。仅需自然语言提示结合标准物理符号，ColliderAgent即可从理论拉格朗日量出发，完成从理论建模到最终现象学输出的完整工作流，无需依赖特定软件包代码。该框架通过分层多智能体推理层与Magnus统一执行后端（支持现象学计算与模拟工具链）协同运行。我们在代表性文献复现任务中验证了系统性能，涵盖轻子偶子和轴子类粒子模型、高维有效算符、部分子级与探测器级分析，以及大规模参数扫描并得出排除限。这些成果展示了向更自动化、可扩展、可复现的对撞机物理、宇宙学乃至更广泛物理学研究迈进的可行路径。
原文摘要 · Abstract (English)
We present, to our knowledge, the first language-driven agent system capable of executing end-to-end collider phenomenology tasks, instantiated within a decoupled, domain-agnostic architecture for autonomous High-Energy Physics phenomenology. Guided only by natural-language prompts supplemented with standard physics notation, ColliderAgent carries out workflows from a theoretical Lagrangian to final phenomenological outputs without relying on package-specific code. In this framework, a hierarchical multi-agent reasoning layer is coupled to Magnus, a unified execution backend for phenomenological calculations and simulation toolchains. We validate the system on representative literature reproductions spanning leptoquark and axion-like-particle scenarios, higher-dimensional effective operators, parton-level and detector-level analyses, and large-scale parameter scans leading to exclusion limits. These results point to a route toward more automated, scalable, and reproducible research in collider physics, cosmology, and physics more broadly.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。