arXiv AI By Jinlin Liu, Wei Chen, Xiaojin Zhang

Lost in Implicit Routing: How LLM Agents Fail to Load Relevant Skills at Scale

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv AI
Sep 28

SkillFlow: Scalable and Efficient Agent Skill Retrieval System

SkillFlow is an open, multi-stage retrieval system that helps AI agents selectively load relevant skills from a large library of community-contributed SKILL.md definitions. The pipeline uses dense retrieval, two rounds of cross-encoder reranking, and LLM-based selection to balance recall and precision. Evaluations on SkillsBench and Terminal-Bench show that SkillFlow improves performance when high-quality skills are available, but retrieval alone does not help if the corpus lacks executable skills for the target domain.

By Fangzhou Li, Pagkratios Tagkopoulos, Ilias Tagkopoulos
arXiv AI
Oct 2

SkillLens: Adaptive Multi-Granularity Skill Reuse for Cost-Efficient LLM Agents

SkillLens introduces a hierarchical skill-evolution framework that organizes skills into a four-layer graph of policies, strategies, procedures, and primitives, allowing retrieval at mixed granularity. The system first retrieves semantically relevant skill seeds, expands them via a degree‑corrected random walk, and uses a verifier to decide whether to accept, decompose, rewrite, or skip each visited unit. This approach enables agents to reuse compatible subskills while locally adapting mismatched components, and theoretical analysis shows sublinear cost under sparse mismatch assumptions, with empirical results on MuLocbench and ALFWorld demonstrating consistent improvements over strong baselines.

By Ziyang Yu, Yongliang Miao, Liang Zhao, Bowen Zhu, Hasibul Haque
arXiv AI
1d ago

SkillBrowser: Selective Skill Exposure for Scalable Skill-Using Agents

SkillBrowser is a Meta‑Skill‑managed framework that allows large language model agents to identify their current capability needs, formulate targeted queries, and selectively load skills on demand. By providing relevant procedural content only when needed, it reduces context pollution and misaligned skill exposure that can disrupt reasoning. Experiments on six SRA‑Bench subsets and five LLMs show that SkillBrowser improves aggregate accuracy over static retrieval and LLM‑based selection, especially for multi‑step tool use, while mitigating accuracy degradation on already‑solvable instances.

By Jin Ye, Yifan Jiang, Lan-zhe Guo