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 ide...
| Authors | Licong Xu, Thomas Borrett |
|---|---|
| Primary category | astro-ph.IM |
| Published | 14 May 2026 |
| Last updated at source | 1 June 2026 |
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