Abstract
Machine learning (ML) is increasingly used in astronomy, but choosing a suitable method for a scientific problem often requires expertise in both astronomy and ML. Large language models (LLMs) could help astronomers make such choices, yet this ability remains underexplored in existing benchmarks. We introduce Astro2Method, a benchmark of 500 expert-reviewed binary questions from 346 astronomy papers (2024/01-2026/06), based on clear performance differences and complete (Easy) or partial (Hard) explanations from the authors. Across five LLMs, direct prompting achieves 87.50-92.55% accuracy on Easy questions but only 58.87-64.52% on Hard. Using historical papers, neither retrieval-augmented generation nor most agent methods with additional reasoning or retrieval (including ReAct, Self-Ask, and CRITIC) bring consistent improvement. Agents required to search the web improve accuracy mainly when they find the papers the questions are based on, with no consistent improvement otherwise. Providing the source paper's pipeline description with method names masked improves Hard accuracy, matching or exceeding web search. Together, these results suggest that relevant experimental information helps distinguish ML methods, but inferring it from related studies and domain knowledge remains challenging for LLMs in astronomy.
Abstract (translated)
URL
https://arxiv.org/abs/2610.30282
