SkillPyramid: A Hierarchical Skill Consolidation Framework for Self-Evolving Agents
The article introduces SkillPyramid, a framework designed to enhance the skill consolidation of self-evolving AI agents. This framework addresses the limitations of current AI systems in skill construction and transfer by enabling agents to reuse existing skills for broader task generalization. Experimental results demonstrate that SkillPyramid significantly improves performance metrics, including a 38% increase in average reward and a 27.7% reduction in execution steps.
- ▪SkillPyramid is a hierarchical skill consolidation framework for AI agents.
- ▪It allows agents to compose, validate, and incorporate new skills during task execution.
- ▪Experiments show a 38% increase in average reward and a 27.7% reduction in execution steps.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2606.03692 |
| Publication time | Wed, 03 Jun 2026 00:00:00 -0400 |
| Retrieval time | 2026-06-03T04:11:55.408Z |
| Last seen | 2026-06-03T04:11:55.408Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Artificial Intelligence arXiv:2606.03692 (cs) [Submitted on 2 Jun 2026] Title:SkillPyramid: A Hierarchical Skill Consolidation Framework for Self-Evolving Agents Authors:Yuan Xiong, Ziqi Miao, Qian Chen, Lijun Li, Yequan Wang, Shizhu He, Jun Zhao, Kang Liu View a PDF of the paper titled SkillPyramid: A Hierarchical Skill Consolidation Framework for Self-Evolving Agents, by Yuan Xiong and 7 other authors View PDF HTML (experimental) Abstract:Recent AI agents can flexibly invoke skills to solve complex tasks, but their long-term improvement is fundamentally constrained by a lack of systematic skill construction, accumulation, and transfer.
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