Tag
Presents SkillSelect-Serve, a framework for budget-controllable and QoS-aware skill service recommendation and composition for small LLM agents, evaluating on a large registry and demonstrating improved recall and utility over top-k retrieval.
SkillCoach introduces a self-evolving rubric framework that evaluates and enhances LLM agent skill-use by analyzing skill selection, following, composition, and reflection, providing process-level supervision beyond outcome-only metrics.
Introduces SkillDAG, a self-evolving typed directed graph for LLM skill selection at scale that models inter-skill relationships and allows agents to query and evolve the graph during execution, outperforming baselines on ALFWorld and SkillsBench.