AlignEvoSkill: Towards Knowledge-Aware and Task-Aligned Agent Skill Evolution
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911717027479552 |
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| author | Wang, Dingzirui Zhang, Xuanliang Xu, Keyan Zhu, Qingfu Che, Wanxiang Deng, Yang |
| author_facet | Wang, Dingzirui Zhang, Xuanliang Xu, Keyan Zhu, Qingfu Che, Wanxiang Deng, Yang |
| contents | Reusable skills play a key role in improving LLM-based agents, but existing skill-evolution methods often fail to ensure that evolved skills both cover the knowledge required by the task and remain aligned with the target task. As a result, evolved skills could be incomplete or irrelevant. To address this limitation, we propose AlignEvoSkill, a skill-evolution framework that jointly models knowledge coverage and task alignment. Given failed task trajectories, AlignEvoSkill first identifies task-relevant knowledge tags, retrieves complementary prior skills, and adapts them into candidate skills that address missing knowledge. It then selects high-quality candidates using a joint filtering criterion based on knowledge-coverage and task-alignment scores. Experiments on 3 benchmarks with4 LLM backbones show a 34.7% relative gain of AlignEvoSkill over the non-evolution baseline and achieves a new SOTA in skill evolution with lower cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23149 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AlignEvoSkill: Towards Knowledge-Aware and Task-Aligned Agent Skill Evolution Wang, Dingzirui Zhang, Xuanliang Xu, Keyan Zhu, Qingfu Che, Wanxiang Deng, Yang Computation and Language Reusable skills play a key role in improving LLM-based agents, but existing skill-evolution methods often fail to ensure that evolved skills both cover the knowledge required by the task and remain aligned with the target task. As a result, evolved skills could be incomplete or irrelevant. To address this limitation, we propose AlignEvoSkill, a skill-evolution framework that jointly models knowledge coverage and task alignment. Given failed task trajectories, AlignEvoSkill first identifies task-relevant knowledge tags, retrieves complementary prior skills, and adapts them into candidate skills that address missing knowledge. It then selects high-quality candidates using a joint filtering criterion based on knowledge-coverage and task-alignment scores. Experiments on 3 benchmarks with4 LLM backbones show a 34.7% relative gain of AlignEvoSkill over the non-evolution baseline and achieves a new SOTA in skill evolution with lower cost. |
| title | AlignEvoSkill: Towards Knowledge-Aware and Task-Aligned Agent Skill Evolution |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.23149 |