arXiv：cs.AI· Jinlin Liu, Wei Chen, Xiaojin Zhang·· 6 小时前
LLM 智能体为何在技能库扩大时加载不到相关技能：隐式路由失效研究
Lost in Implicit Routing: How LLM Agents Fail to Load Relevant Skills at Scale
AI 导读
LLM 智能体依赖对技能元数据的隐式路由按需加载技能，但在 SkillsBench 任务中加入无关技能后出现"技能路由崩溃"：当无关技能达到 1,000 个时，已加载技能召回率从 73.6% 降至 27.4%，零加载率从 9.3% 升至 51.6%；而同一技能库中显式选择仍保持 73.9% 召回率。
正文
Abstract:Reusable skills let LLM agents load task-specific instructions on demand, but they make execution depend on implicit routing over skill metadata. We test whether this design scales by holding SkillsBench tasks and their golden skills fixed while adding irrelevant skills. We find skill routing collapse: agents increasingly load fewer or no skills, even though the relevant skills remain available. With 1,000 irrelevant skills, loaded-skill recall falls from 73.6% to 27.4% and the zero-load rate rises from 9.3% to 51.6%, whereas explicit selection from the same library retains 73.9% recall. Loading other skills, including plausible alternatives to golden skills, is too rare to explain this decline. Masking only the descriptions of irrelevant skills largely restores skill loading, yet with the metadata intact, explicitly asking agents to plan skill use still yields lower recall than preselection. These findings point to semantic interference from descriptions and instruction interference when skill choice competes with task execution. We also evaluate explicit preselection as a practical mitigation, including its coverage and latency trade-offs. Our analysis informs skill protocols and evaluation for agents with growing skill libraries.
| Comments: | 22 pages, including references and appendices. Code: this https URL |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.18522 [cs.AI] |
| (or arXiv:2610.18522v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18522 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jinlin Liu [view email]
[v1]
Tue, 13 Oct 2026 15:12:12 UTC (1,765 KB)
来源：arXiv：cs.AI · arxiv.org