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  "text": "// Evolving Meta-Skill for Multi-Agent Systems //\n\nCan a multi-agent system get better at orchestration without touching a single weight?\n\nAutomatic MAS generation has been stuck between two bad options. Inference-time methods use frozen frontier models but never learn from past runs. Training-time methods learn but are capped by small-model capability.\n\nSkill-MAS takes a third path.\n\nIt treats the orchestration capability as an evolvable Meta-Skill, refined through a closed loop of multi-trajectory rollout and selective reflection that distills experience into strategy-level principles rather than memorized traces.\n\nAcross four benchmarks and four different LLMs, the evolved Meta-Skills transfer to unseen tasks and other models, because the know-how lives in text at the strategy level, not in any one model's weights.\n\nYou keep the frontier model and still accumulate experience.\n\nPaper: https://t.co/fn4J2Gz33M\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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