Reproduce “SKILLFOUNDRY: Building Self-Evolving Agent Skill Libraries from Heterogeneous Scientific Resources” on OpenResearch
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About this paper
Shuaike Shen, Wenduo Cheng, Mingqian Ma, Alistair Turcan, Martin Jinye Zhang, Jian Ma
SkillFoundry packages procedural knowledge from scientific resources into reusable skills, then tests and revises them for a library. It targets under-covered branches in a scientific knowledge tree and extracts task instructions, inputs, outputs, dependencies, and execution assumptions. In benchmark tests, mined skills improve accuracy on five of six datasets, while execution success remains at 100%. These results do not establish broader usefulness.
Key insights
- Scientific procedural knowledge embedded across diverse and often unstructured resources can be systematically mined and automatically transformed into validated, executable agent skills.
- A self-evolving framework guided by a domain knowledge tree can efficiently prioritize skill acquisition and ensure both internal validity and novelty of the generated skill library.
- Multi-stage validation, including execution, system, and synthetic-data testing, is critical for establishing the reliability of automatically extracted skills and identifying genuinely new capabilities.
Summary by alphaXiv.
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