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#artificial intelligence Preprint Oct 2026

GUI-HARVEST: Self-Improving GUI Agents through Evidence-Driven Harness Evolution

The executable harness surrounding a GUI model determines how observations are assembled, actions are executed, and verification, recovery, and termination are controlled. Compared with harness optimization for non-GUI agents, automatically optimizing this harness poses three coupled challenges: reconciling model inten...

Ge-Yi Yang, Zi-Kun Qu, Xiang Li et al. · 0 citations
#artificial intelligence Preprint Sep 2026

COBRA-Skills: Contextual Bandit-Guided Evolution for Agent Skill Optimization

COBRA-Skills is introduced, an efficient framework that formulates skill optimization as budgeted sequential optimization over a dynamically evolving candidate space and remains robust to changes in the agent harness and performs effectively when the target model itself is used for skill generation and refinement.

Ping-Chen Lu, Xiang-Yi Wang, Xiang Li et al. · 2 citations

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