arXiv cs.CL· Hanrong Zhang, Minghao Liang, Jiawei Wu, Yankai Chen, Shicheng Fan, Zhiwei Liu, Enze Ma, Junwei Yuan, Tianyi Wang, Dehai Min, Kening Zheng, Xue Liu, Philip S. Yu·· 11 小时前AI 评分38
Skill++：通过路由感知的技能精炼实现终身学习智能体
Skill++: Towards Lifelong Agents via Routing-Aware Skill Refinement
AI 导读
Skill++ 通过只重写新技能的 name 和 description、无需训练 router 或标注数据，解决技能库增长中的路由干扰问题。技能库经十轮扩展到 731 个技能后，Skill++ 在 76.3% 的查询中选全所需技能，原始元数据仅 36.4%。
正文 · 原文
Authors:Hanrong Zhang, Minghao Liang, Jiawei Wu, Yankai Chen, Shicheng Fan, Zhiwei Liu, Enze Ma, Junwei Yuan, Tianyi Wang, Dehai Min, Kening Zheng, Xue Liu, Philip S. Yu
Abstract:Large language model (LLM) agents extend their capabilities with skills, each with a name, a description, and a body. Under progressive disclosure, an LLM router selects skills by name and description alone and loads only the selected bodies. Skill libraries grow continually as users and agents add new skills. Because the router relies on this metadata, a similar new skill can displace an earlier one, breaking previously correct routing even though the earlier skill and the router are unchanged. This routing interference is aggravated for public skills, whose descriptions may not match their bodies. We formulate the problem as lifelong skill routing, a class-incremental continual learning problem. A solution must keep earlier skills selectable without suppressing new ones. Existing approaches fall short, since continually retraining the router risks catastrophic forgetting and rewriting methods either ignore similar skills or require per-skill feedback. We therefore propose Skill++, which rewrites only the name and description of each incoming skill, without router training or labeled data. The new description states what the body supports, how the skill differs from its closest predecessor, and what related work it excludes. As a library grows to 731 skills over ten sessions, Skill++ selects all required skills for 76.3% of queries, versus 36.4% under the original metadata. Across three benchmarks and five LLM routers, it outperforms all compared approaches, and its gains carry over to the Codex CLI agent harness.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2611.00544 [cs.AI] |
| (or arXiv:2611.00544v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.00544 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hanrong Zhang [view email]
[v1]
Sat, 31 Oct 2026 01:58:58 UTC (811 KB)
来源：arXiv cs.CL · arxiv.org