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

Abstract:Natural language prompts and skills serve as the strategic backbone of LLM-based agents. Recent advances in prompt and skill optimization have achieved notable gains, yet all existing methods optimize a \emph{single} text template---missing the synergy among multiple complementary strategies. We propose MOSCOPT, a text-native, parameter-free algorithm that jointly optimizes a pool of $N$ skills and a gating skill $G$ that dynamically selects $K$ skills per step. To effectively optimize the skills, we build the EditAdam with internally maintained dual states. Through the three-phase interleaved updates with EditAdam, the system monotonically improves without gradient or parameter tuning. Extensive experiments and detailed ablations across 5 benchmarks and 3 target LLMs demonstrate that MOSCOPT consistently outperforms all baselines, and confirm that both the mixture-of-skills architecture with selective activation and the collective evolution with three-phase interleaving are essential to its superior performance. Code is released this https URL.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2609.14399 [cs.AI]

(or arXiv:2609.14399v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.14399 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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