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原文摘要

In many scientific tasks, scientists repeatedly choose a small set of candidates to test, learn from the results, and use the observations to guide the next selection. Methods based on active learning~(AL) or Bayesian optimization~(BO) have been proposed for these iterative screening campaigns with a surrogate model. Yet, selecting the surrogate model and acquisition function for a new dataset remains challenging due to limited empirical evidence, while the effectiveness of large language models (LLMs) in making such decisions remains unclear. To address these gaps, we introduce SciSelect-Gym, an environment for simulating AL/BO data selection campaigns and evaluating configuration choices. We conducted simulations of AL/BO data selection campaigns across several settings and more than 100 molecular datasets. Using completed simulation campaigns as context improves LLM recommendation shortlists on held-out datasets more consistently than it improves the quality of their first choice. SciSelect-Gym provides a simulation platform and recommendation protocols for learning how to choose configurations before an acquisition campaign. An implemented agent use case connects these campaign records and simulation tools to LLM-generated configuration-selection policies. The code is provided as supplementary material.