# Beyond AI as Assistants: Toward Autonomous Discovery in Cosmology

arXiv: https://arxiv.org/abs/2605.14791 · HTML: https://mlsanity.com/paper/2605.14791/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 3 references that are in the map.
- First posted on arXiv 14 May 2026.

## Builds on (most cited first, 1 of 1)

- [FAIR Universe Weak Lensing ML Uncertainty Challenge: Handling Uncertainties and Distribution Shifts for Precision Cosmology](https://mlsanity.com/paper/2604.14451/) (2026)

## Built on by (most cited first, 2 of 2)

- [Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap](https://mlsanity.com/paper/2608.05179/) (2026)
- [Workflow Closure Is Not Scientific Closure in Auto-Research Systems](https://mlsanity.com/paper/2605.26200/) (2026)

## Abstract (arXiv)

Recent advances in artificial intelligence (AI) agents are pushing AI beyond tools toward autonomous scientific discovery. We discuss two complementary agentic systems for cosmology: textttCMBEvolve, which targets tasks with explicit quantitative objectives through LLM-guided code evolution and tree search, and textttCosmoEvolve, which targets open-ended scientific workflows through a virtual multi-agent research laboratory. As preliminary demonstrations, we apply textttCMBEvolve to out-of-distribution detection in weak-lensing maps, where it iteratively improves the benchmark score through code evolution, and textttCosmoEvolve 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.