Scientific code repositories contain structured domain knowledge that can be systematically converted into agent training data, enabling models to learn both specialized scientific skills and general capabilities through verified interaction trajectories.
ScienceIDE converts scientific code repositories into learning environments for AI agents by automating the extraction of executable tasks, verification criteria, and domain knowledge. The system trains specialized models (PhAI-IDE family) on verified scientific code interactions, demonstrating that learning from scientific software improves both code repair and general reasoning capabilities.