MOCHA: Multi-Objective Chebyshev Annealing for Agent Skill Optimization
MOCHA optimizes LLM agent skills by treating them as multi-objective problems, balancing task performance and platform constraints. It uses Multi-Objective Chebyshev Annealing to navigate complex trade-offs, finding superior skill variants that existing single-objective methods fail to discover. This leads to significant accuracy gains across diverse tasks.