宇宙学研究者终于有了能自主推进发现的AI工具——CMBEvolve和CosmoEvolve分别解决了定量优化和开放式探索两大痛点，做数据分析或理论建模的团队可以直接参考其方法。
这篇论文提出了两个面向宇宙学的AI智能体系统：CMBEvolve通过LLM引导的代码进化和树搜索，针对有明确量化目标的任务（如弱引力透镜图中的异常检测）进行优化；CosmoEvolve则构建虚拟多智能体研究实验室，用于开放式的科学工作流（如自主分析ACT DR6数据）。初步实验显示，CMBEvolve能通过代码进化迭代提升基准分数，CosmoEvolve能识别非平凡的成对和尺度依赖行为并生成分析级诊断。这项工作展示了宇宙学如何为AI科学家系统的开发提供可控基准和真实开放研究问题。
Beyond AI as Assistants: Toward Autonomous Discovery in Cosmology
Recent advances in artificial intelligence (AI) agents are pushing AI beyond tools toward autonomous scientific discovery. We discuss two complementary agentic systems for cosmology: \texttt{CMBEvolve}, which targets tasks with explicit quantitative objectives through LLM-guided code evolution and tree search, and \texttt{CosmoEvolve}, which targets open-ended scientific workflows through a virtual multi-agent research laboratory. As preliminary demonstrations, we apply \texttt{CMBEvolve} to out-of-distribution detection in weak-lensing maps, where it iteratively improves the benchmark score through code evolution, and \texttt{CosmoEvolve} to autonomous ACT DR6 data analysis, where it identifies non-trivial pair- and scale-dependent behaviour and produces analysis-grade diagnostics. These examples show how cosmology can provide both controlled benchmark tasks and realistic open-ended research problems for the development of AI scientist systems.