把全世界的科学代码变成AI的练功房
科学代码库是人类知识的活化石，但AI读不懂——工具链碎片化、领域规矩靠默契、对错标准太专业，这堵墙叫「科学经验瓶颈」。ScienceIDE 干的事很直接：让专家先定义「什么算做对」，再让AI把代码仓库改造成能自己出题、自己跑、自己判分的训练场。用这些真实交互轨迹训出的模型，修科学代码的能力变强了，连通用编程、推理、知识问答也跟着涨——科学经验没有锁死在实验室里，而是迁移成了更通用的智能。它不是你明天能装上的工具，但这是「让AI从人类积累的科研软件里自学」这条路上，少见的实打实的一步。
📄 原文摘要(英文)
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