MOSCOPT: Mixture-of-Skills Collective Optimization for LLM Agents

MOSCOPT is a parameter‑free algorithm that jointly optimizes a pool of N skills and a gating skill G, which dynamically selects K skills at each step for LLM agents. It introduces EditAdam, a dual‑state optimizer, and a three‑phase interleaved update scheme that monotonically improves performance without gradient or parameter tuning. Experiments on five benchmarks and three target LLMs show MOSCOPT consistently outperforms baselines, highlighting the importance of mixture‑of‑skills architecture and collective evolution.

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• llms
• agents
• benchmarks