Competing with AI Scientists: Agent-Driven Approach to Astrophysics Research
We present an agent-driven approach to the construction of parameter inference pipelines for scientific data analysis. Our method leverages a multi-agent system, Cmbagent (the analysis system of the AI scientist Denario), in which specialized agents collaborate to generate research ideas, write and execute code, evaluate results, and iteratively refine the overall pipeline. As a case study, we apply this approach to the FAIR Universe Weak Lensing Uncertainty Challenge, a competition under time constraints focused on robust cosmological parameter inference with realistic observational uncertainties. While the fully autonomous exploration initially did not reach expert-level performance, th...
| Authors | Thomas Borrett, Licong Xu, Andy Nilipour et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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