MASkills Optimizes Multi-Agent LLM Systems Through Continual Skill Learning

Summary

MASkills introduces a continual learning framework that optimizes multi-agent LLM systems through structured agent skills. Its pipeline combines skill-conditioned credit assignment, hierarchical credit aggregation, and momentum-smoothed optimization, allowing skill libraries to evolve through refinement, induction, consolidation, and pruning. Experiments on HotpotQA, LoCoMo, and GAIA demonstrate effectiveness across several agentic tasks, and the implementation is publicly available.