The Chinese University of Hong Kong, Shenzhen

The Chinese University of Hong Kong, Shenzhen, together with Tianjin University, The Hong Kong University of Science and Technology (Guangzhou) and National University of Singapore, details COBRA-Skills in a paper titled “COBRA-SKILLS: Contextual Bandit-Guided Evolution for Agent Skill Optimization.” The work models agent skill optimization as a budget-controlled contextual multi-armed bandit problem: a lightweight MLP predicts reward and LinearUCB quantifies exploration to choose candidate skills for real evaluation, while regeneration, trajectory mutation and crossover recombination operators evolve the population on a logarithmic schedule. The authors report that using only 50 optimization samples cuts optimization cost by 55%–58%, outperforming existing mainstream methods on six cross-domain benchmarks. On Qwen3.6-35B-A3B, GPT-5.4-Nano and Gemma-4-26B-A4B-it, improvements over no-skill baselines reach 13.1, 26.9 and 22.5 percentage points.