把 GitHub 仓库蒸馏成技能，AI 研究员成绩翻倍
AI 做科研的瓶颈不是不会推理，而是不会「操作」:论文和代码库里的知识是给人看的，太长、太散，任务进行中根本来不及读。这篇把 1000 个最常用的机器学习仓库蒸馏成 5000 多个「技能」——每个技能就是一段验证过的操作步骤，比如「怎么调这个模型的超参数」「这个库的坑在哪」。带着这些技能上场的 AI 研究员，在 MLE-bench 上分数比不带技能的同一套系统高 134%，其他三个基准也涨了 9% 到 34%。它不是你明天能用上的东西，但它指向一个趋势：AI 的进步不再只靠更大的模型，而是靠把人类积累的「怎么做」变成 AI 能直接调用的资产。
📄 原文摘要(英文)
Autonomous agents are beginning to carry out machine-learning (ML) research end to end. These agents combine a model backbone with a harness for planning, execution, memory, and verification, but this architecture still leaves domain-specific know-how outside the agent. We call this missing layer operational knowledge, the know-how that separates knowing a method from making it work. That knowledge is not absent from the field. It appears in repositories and papers, but in forms written for human readers and too large to load during a task. Once distilled into compact, verified skills, this knowledge can be reused across tasks rather than rediscovered during each run. We present DisCo, a skill-powered research agent that creates skills and uses them during research. Its distillation runs in two complementary forms: task-agnostic, condensing the field's widely used repositories into reusable skills, and task-oriented, producing the skills a concrete task calls for. The former, applied across the open ecosystem, yields the AREX-Skill Library, with 5,000+ verified skills distilled from 1,000 widely used ML repositories and organized into 20 areas and 178 capability families. With the GPT-5.5 backbone, research harness, and downstream execution budget held fixed, the skill-equipped research agent scores 134.3% higher on MLE-bench, 34.4% higher on PaperBench, 9.2% higher on FrontierCS, and 14.0% higher on PassNet than the same agent without skills. These gains come from adding distilled operating context under that fixed setup.