Beyond AI as Assistants: Toward Autonomous Discovery in Cosmology

• URL: http://arxiv.org/abs/2605.14791v1
• Date: Thu, 14 May 2026 12:58:12 GMT
• Title: Beyond AI as Assistants: Toward Autonomous Discovery in Cosmology
• Abstract summary: Recent advances in artificial intelligence (AI) agents are pushing AI beyond tools toward autonomous scientific discovery.<n>We discuss two complementary agentic systems for cosmology: textttCMBEvolve and textttCosmoEvolve<n>We show how cosmology can provide both controlled benchmark tasks and realistic open-ended research problems for the development of AI scientist systems.
• Score: 0.02455468619225742
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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