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    <abstract>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-smoothed optimization, enabling agent skill libraries to evolve through refinement, induction, consolidation, and pruning. Experiments on HotpotQA, LoCoMo, and GAIA demonstrate the effectiveness of MASkills across multiple agentic tasks.</abstract>
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%0 Conference Proceedings
%T MASkills: Continual Skills Optimization for Multi-Agent LLM Systems
%A Yao, Huaiyuan
%A Liu, Xiaoou
%A Fleming, Charles
%A Chen, Tianlong
%A Wei, Hua
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F yao-etal-2026-maskills
%X 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-smoothed optimization, enabling agent skill libraries to evolve through refinement, induction, consolidation, and pruning. Experiments on HotpotQA, LoCoMo, and GAIA demonstrate the effectiveness of MASkills across multiple agentic tasks.
%U https://aclanthology.org/2026.findings-emnlp.1453/
%P 30947-30957

Markdown (Informal)

[MASkills: Continual Skills Optimization for Multi-Agent LLM Systems](https://aclanthology.org/2026.findings-emnlp.1453/) (Yao et al., Findings 2026)

• MASkills: Continual Skills Optimization for Multi-Agent LLM Systems (Yao et al., Findings 2026)

ACL

• Huaiyuan Yao, Xiaoou Liu, Charles Fleming, Tianlong Chen, and Hua Wei. 2026. MASkills: Continual Skills Optimization for Multi-Agent LLM Systems. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 30947–30957, Budapest, Hungary. Association for Computational Linguistics.