MOSCOPT: Mixture-of-Skills Collective Optimization for LLM Agents
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. E...
| Authors | Zhenyu Zhang, Jiudong Yang |
|---|---|
| Primary category | cs.AI |
| Published | 13 September 2026 |
| Last updated at source | 18 September 2026 |
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