ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environments
ScienceIDE turns scientific code repositories into agent-trainable environments and trains PhAI-IDE models at 72B, 9B, and 4B scales.
ScienceIDE is infrastructure that transforms scientific repositories into executable environments supporting task generation, execution, and scientific verification, guided by expert-defined cases and acceptance criteria. Using verified interaction trajectories, the authors train PhAI-IDE-72B, PhAI-IDE-9B, and PhAI-IDE-4B. The model family improves held-out scientific-code repair and selected general benchmarks in code, reasoning, and knowledge, indicating positive transfer. Code is released on GitHub.
- Addresses the 'scientific experience bottleneck' from fragmented toolchains and implicit conventions
- Environments support supervised fine-tuning, reinforcement learning, and evaluation
- Training uses verified agent interaction trajectories from transformed repositories
- Gains shown in scientific-code repair plus general code, reasoning, and knowledge benchmarks
Full article159 words · extracted from arxiv.org · click to collapse
Scientific code repositories encode decades of human knowledge in executable models, methods, and tools. Yet fragmented toolchains, implicit domain conventions, and specialized correctness criteria make this knowledge difficult to convert into reliable learning experience-a challenge we call the scientific experience bottleneck. We introduce ScienceIDE, infrastructure for turning the world's scientific code into programmable environments for scientific agents. Guided by expert-defined scientific cases and acceptance criteria, agents transform repositories into executable environments that support task generation, execution, and scientific verification. These environments provide a shared foundation for supervised fine-tuning, reinforcement learning, and evaluation. Using verified interaction trajectories, we train PhAI-IDE-72B, PhAI-IDE-9B, and PhAI-IDE-4B. The model family shows gains in held-out scientific-code repair and across selected general-purpose benchmarks in code, reasoning, and knowledge, providing evidence of positive transfer from scientific experience to broader capabilities. ScienceIDE lays the foundation for an integrated workspace for agent learning and scientific practice, making humanity's scientific software a shared substrate for developing scientific intelligence. Code: https://github.com/aitofound/ScienceIDE
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.19134