arXiv AI
An Interpretable CF-RL-TOPSIS Fusion Model for Skills-Aware Talent Recommendation
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arXiv AI
Jun 3
SkillDAG: Self-Evolving Typed Skill Graphs for LLM Skill Selection at Scale
arXiv:2606. 03056v1 Announce Type: new Abstract: As LLM agents adopt large skill libraries, selecting the right subset becomes a structural problem rather than a similarity-matching one: skills depend on, conflict with, specialize, or duplicate one another, a structure invisible to both full enumeration and embedding similarity.
arXiv AI
Jul 15
SkillSelect-Serve: QoS-Aware Budgeted Skill Service Recommendation for LLM Agents
arXiv:2607. 00011v2 Announce Type: replace-cross Abstract: Reusable agent skills are emerging as a service-oriented capability layer for Large Language Model (LLM) agents.
arXiv AI
Sep 12
COBRA-Skills: Contextual Bandit-Guided Evolution for Agent Skill Optimization
COBRA‑Skills is a new framework that treats skill optimization for large language model agents as a budgeted sequential problem over a dynamically evolving candidate set. It uses contextual‑bandit prioritization to focus evaluations on promising or informative candidates and refines the skill population based on execution feedback. In experiments across six agent benchmarks and three target models, COBRA‑Skills outperforms existing methods, cuts optimization cost by 55–58 % compared to SkillOpt, and requires only 50 unique optimization examples per benchmark.
arXiv AI
Jul 8
Task Decomposition-Guided Reranking for Adaptive Agent Skill Retrieval
arXiv:2607. 06283v1 Announce Type: new Abstract: Skill usage can significantly enhance the ability of modern agent systems to complete complex tasks.
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
Sep 1
SkillRet: A Large-Scale Benchmark for Skill Retrieval in LLM Agents
arXiv:2605.05726v2 Announce Type: replace Abstract: As LLM agents are increasingly deployed with large libraries of reusable skills, selecting the right skill for a user request has become a critical...