Hugging Face Trending Papers
Lost in Implicit Routing: How LLM Agents Fail to Load Relevant Skills at Scale
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv AI
5d ago
Lost in Implicit Routing: How LLM Agents Fail to Load Relevant Skills at Scale
arXiv:2610.18522v1 Announce Type: new Abstract: Reusable skills let LLM agents load task-specific instructions on demand, but they make execution depend on implicit routing over skill metadata. We te...
arXiv Machine Learning
Jul 21
SkillRouter: Skill Routing for LLM Agents at Scale
arXiv:2603. 22455v5 Announce Type: replace Abstract: Reusable skills let LLM agents package task-specific procedures, tool affordances, and execution guidance into modular building blocks.
arXiv AI
Jun 24
More Skills, Worse Agents? Skill Shadowing Degrades Performance When Expanding Skill Libraries
arXiv:2605. 24050v2 Announce Type: replace-cross Abstract: Skill libraries allow LLM agents to load task-specific instructions on demand, letting non-expert users solve domain-specific tasks through natural language without knowing which skills exist or how they work.
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.
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.
arXiv AI
Jun 9
Skill Retrieval Augmentation for Agentic AI
arXiv:2604. 24594v3 Announce Type: replace-cross Abstract: As large language models (LLMs) evolve into agentic problem solvers, they increasingly rely on external, reusable skills to handle tasks beyond their native parametric capabilities.