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This paper introduces Best Prefix Selection (BPS), an algorithm for optimal skill selection in LLM agents that provides provable bicriteria guarantees and outperforms existing methods on task success while reducing token usage.
SkillGate addresses selector credit starvation in AI agent skill selection by using separate credit channels for execution and skill-naming tokens, improving success rates and reducing misleading skill exposure in long-horizon tasks.
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.