MASkills: Continual Skills Optimization for Multi-Agent LLM Systems
LLM-based multi-agent systems have shown strong performance on complex tasks, yet continual improvement from interaction experience remains challenging. Existing self-reflection methods build experience memories, but memories are mostly hard to invoke, refine, or scale, while agent skills offer a more actionable unit: structured procedural knowledge that specifies when to act, how to act, and which resources or tools to use. We introduce MASkills, a continual learning framework that optimizes multi-agent LLM systems through agent skills. MASkills presents a new agent-optimization pipeline that integrates skill-conditioned credit assignment, hierarchical credit aggregation, and momentum-sm...
| Authors | Huaiyuan Yao, Xiaoou Liu, Charles Fleming et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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