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DisCo distills GitHub repositories into AI4AI skills to boost research agent performance

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The authors present DisCo, a skill-powered research agent designed to bridge the gap in operational knowledge by distilling domain-specific know-how from external sources. The system condenses widely used machine learning repositories into reusable, verified skills through two complementary forms: task-agnostic distillation for broad utility and task-oriented distillation for specific needs.

DisCo generates the AREX-Skill Library, containing over 5,000 verified skills distilled from 1,000 ML repositories across 20 areas and 178 capability families.
When paired with a GPT-5.5 backbone and fixed execution budget, the skill-equipped agent outperforms the baseline without skills by 134.3% on MLE-bench, 34.4% on PaperBench, 9.2% on FrontierCS, and 14.0% on PassNet.

These results demonstrate that adding distilled operating context allows autonomous agents to reuse knowledge across tasks rather than rediscovering it during each run.

Importance 2/3 New harness with differentiators arXiv cs.CL OpenAI AI agents Research paper

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