Summary

ScienceIDE uses expert-defined cases, acceptance criteria and verification to generate interaction trajectories for supervised fine-tuning, reinforcement learning and evaluation. The accompanying PhAI-IDE models show gains on selected scientific-code repair and general reasoning benchmarks.

Why it matters

Executable scientific experience could become a scalable bridge between coding agents and AI systems capable of conducting reproducible research workflows.

Sources