Terry Jingchen Zhang

The rise of autonomous AI agents suggests that dynamic benchmark envi- ronments 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 between numerical optimization and adherence to physical constraints: although agents often achieve a good statistical fit, they frequently fail to recover correct physical system pa- rameters, a limitation that persists even when agents are equipped with domain-expert skills. Furthermore, increasing test-time compute yields only marginal gains, with excessive token usage often reflecting recursive failure loops rather than meaningful exploration. STARGAZER presents an opportunity to train, evaluate, scaffold, and scale strategies on a model- fitting problem of practical research relevance today. Our simulation-driven methodology may also provide a useful design pattern for other scientific model-fitting domains.