AI 中文总结
本研究开发了一种基于大语言模型的智能体，通过元技能自主完成相对论重离子碰撞的流体动力学模拟与分析，可应用于CLVisc代码，支持核物理研究的自动化工作流程。
AI 中文摘要
我们使大语言模型（LLM）智能体能够自主完成夸克胶子等离子体演化的端到端流体动力学模拟，以及相对论重离子碰撞中最终强子谱的计算。我们设计了一种元技能，该技能允许智能体探索项目源代码、定制专门技能并对其进行迭代优化。将此元技能应用于(3+1)D粘性流体动力学代码CLVisc，智能体构建了编码其操作知识的CLVisc技能，随后独立执行完整的科学工作流程：设计参数扫描、运行模拟、比较集合结果以及生成可发表的图表。关键在于，智能体无需明确指令，就能利用文献支撑的重离子物理学知识选择具有物理意义的可观测物理量并解释结果。我们在两种场景中演示了该工作流程：一是与温度相关的剪切粘度与熵密度之比η/s，二是在质心系能量√s_NN=5.36 TeV的氧-氧（O+O）碰撞中，利用四种从头算（ab initio）对¹⁶O的描述来研究核结构效应。在这两种场景中，智能体都能自主规划、执行和分析，设计新的初始态可观测物理量以解释最终观测结果并提取定性知识。该元技能与代码版本及蒙特卡洛生成器无关，有望为高能核物理学领域的多智能体系统发展提供支持。
英文摘要
We enable large language model (LLM) agents to autonomously perform end-to-end hydrodynamic simulations of the quark-gluon plasma evolution and calculation of final hadron spectra in relativistic heavy-ion collisions. We design a meta skill that allows an agent to explore a project's source code, craft a specialized skill, and iteratively refine it. Applying this meta skill to the (3+1)D viscous hydrodynamic code CLVisc, the agent builds a CLVisc skill encoding its operational knowledge and then independently executes full scientific workflows: designing parameter scans, running simulations, comparing ensemble results, and producing publication-ready figures. Crucially, the agent draws on literature-informed heavy-ion physics to select physically meaningful observables and interpret outcomes without explicit instruction. We demonstrate the pipeline in two scenarios: temperature-dependent shear viscosity over entropy density $η/s$, and nuclear-structure effects in O+O collisions at $\sqrt{s_{\mathrm{NN}}} = 5.36$~TeV using four \textit{ab initio} descriptions of $^{16}$O. In both, the agent plans, executes, and analyzes autonomously, devising new initial-state observables to explain final observations and extract qualitative knowledge. The meta skill is agnostic to code versions and Monte Carlo generators, promising future multi-agent systems in high-energy nuclear physics.
Comments15 pages, 6 figures
