Beyond AI as Assistants: Toward Autonomous Discovery in Cosmology

TL;DR

This paper explores AI agents capable of autonomous scientific discovery in cosmology, demonstrating their ability to improve benchmarks and analyze real data through two novel systems.

Contribution

Introduces two AI agent systems, CMBEvolve and CosmoEvolve, for autonomous discovery in cosmology, combining code evolution and multi-agent research.

Findings

CMBEvolve improves out-of-distribution detection scores iteratively.

CosmoEvolve identifies complex behaviors in ACT DR6 data.

Demonstrates AI's potential in controlled and open-ended cosmological research.

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.