AI自主发现宇宙学新规律，能自动优化代码并分析真实观测数据。
Beyond AI as Assistants: Toward Autonomous Discovery in Cosmology
- 用大模型引导代码演化与树搜索实现目标驱动的智能研究
- 在弱引力透镜图中检测异常，提升基准得分并生成分析级诊断
- 适合对自动化科研、多智能体系统感兴趣的学者
人工智能代理正推动科学发现从辅助工具向自主研究迈进。本文提出两个互补的智能系统： exttt{CMBEvolve} 通过大语言模型引导代码演化与树搜索，解决有明确量化目标的任务； exttt{CosmoEvolve} 则构建虚拟多智能体实验室，应对开放式的科学工作流。初步实验中， exttt{CMBEvolve} 在弱引力透镜图的分布外检测任务中通过迭代优化代码提升了基准分数； exttt{CosmoEvolve} 自主分析 ACT DR6 数据，发现了非平凡的成对与尺度依赖行为，并生成分析级诊断结果。这些案例表明，宇宙学既可提供受控基准任务，也能作为真实开放研究问题，助力智能科学家系统的开发。
原文摘要 · Abstract (English)
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.
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