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.
@article{arxiv.2605.14791,
title = {Beyond AI as Assistants: Toward Autonomous Discovery in Cosmology},
author = {Licong Xu and Thomas Borrett},
journal= {arXiv preprint arXiv:2605.14791},
year = {2026}
}
Comments
4 pages, 2 figures, Contribution to the 2026 Cosmology session of the 60th Rencontres de Moriond