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Matching papers

RESOURCE2SKILL: Distilling Executable Agent Skills from Human-Created Multimodal Resources

129 upvotes · 16 JUL 2026 · Yijia Fan, Zonglin Di, Zimo Wen et al.

This paper develops a system to extract and represent skills from human-created resources like videos, code, and articles, allowing software agents to learn from these multimodal inputs. Practitioners might care about this work because it could enable more effective training of agents in various domains.

multimodal resourcesskill representationhierarchical knowledgeagent learningknowledge graph-based systems

ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environments

69 upvotes · 16 SEP 2026 · Hejia Geng, Zesen Huang, Haoyang Li et al.

This paper creates a system called ScienceIDE that converts scientific code into environments that can be used to train artificial agents to perform scientific tasks. Practitioners might care because this could lead to more efficient and effective ways to develop scientific intelligence.

scientific codeagent learningenvironmentsreinforcement learningnatural language processing