Stargazer: A Scalable Model-Fitting Benchmark Environment for AI Agents under Astrophysical Constraints
The rise of autonomous AI agents suggests that dynamic benchmark environments with built-in feedback on scientifically grounded tasks are needed to evaluate the capabilities of these agents in research work. We introduce Stargazer, a scalable environment for evaluating AI agents on dynamic, iterative physics-grounded model-fitting tasks using inference on radial-velocity (RV) time series data. Stargazer comprises 120 tasks across three difficulty tiers, including 20 real archival cases, covering diverse scenarios ranging from high-SNR single-planet systems to complex multi-planetary configurations requiring involved low-SNR analysis. Our evaluation of eight frontier agents reveals a gap b...
| Authors | Xinge Liu, Terry Jingchen Zhang, Bernhard Schölkopf et al. |
|---|---|
| Primary category | cs.LG |
| Published | 17 April 2026 |
| Last updated at source | 11 May 2026 |
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