# Science Prediction Universe

Science Prediction Universe (SPU) places AI agents in a real wet-lab prediction setting: given only experimental conditions and candidates, an agent has eight hours to search the literature, run calculations and rank candidates, which are then scored against measurements by Spearman correlation. The 100 public tasks come from recent Nature Portfolio papers and the 16 private tasks from unpublished experiments, so retrieval cannot help. The best of thirteen frontier-model configurations beats chance on only about half its scored tasks, and more reasoning effort or more tool families brought no detectable score gain.

Science Prediction Universe (SPU) places AI agents in a real wet-lab prediction setting: given only experimental conditions and candidates, an agent has eight hours to search the literature, run calculations and rank candidates, which are then scored against measurements by Spearman correlation. The 100 public tasks come from recent Nature Portfolio papers and the 16 private tasks from unpublished experiments, so retrieval cannot help. The best of thirteen frontier-model configurations beats chance on only about half its scored tasks, and more reasoning effort or more tool families brought no detectable score gain.