🔬 Even the top AI agents leave about a third of scientific code repair tasks unsolved within a one-hour budget. Title: ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environments URL: 🧩 Overview AItonomy, Qwen, and PhAI-Labs built ScienceIDE, infrastructure that turns 27 scientific codebases, spanning astrophysics to ocean modeling, into executable environments AI agents can actually learn from. It ships 64 environments and 2,812 tasks. ⚙️ Problem it solves Scientific code encodes decades of expertise, but fragmented toolchains and undocumented numerical conventions make it hard to turn that knowledge into reliable learning experience. 🛠 Methodology "Scientific checks" comparing numerical tolerances and conserved quantities validate task correctness, across 7 task categories like repair and implementation. For RL, a "truncation masking" trick prevents unfairly penalizing trajectories that hit the budget cutoff. 📊 Results Under a one-hour budget, Fable 5.1 leads the agent leaderboard at 67.1%, with Astra at 63.1%. Models trained on ScienceIDE experience (PhAI-IDE) gain up to +33.33 points on scientific code repair, and RL training lifts reward on LAPS tasks 2.4x. #AIAgents# #AI4Science#