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Lin-Chao Zhu

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#artificial intelligence Preprint Sep 2026

From Interaction Traces to Persistent Skills: Online Evolution for Computer-Use Agents

Computer-use agents can execute increasingly complex tasks in graphical interfaces, but their interaction experience is typically transient: procedural knowledge acquired from one rollout is not systematically retained, refined, and reused in later tasks. Existing skill libraries provide external procedural knowledge,...

Long-Tao Hu, Xiao Liang, Lin-Chao Zhu · 1 citation
Jul 2026

SeekJudge: A Practical Reward Framework for Reinforcement Learning in Computer-Use Agents

The proposed SeekJudge framework, in which four role-specialized agents, a Condense, a Ground, a Seek and an Analyze agent, reach a verdict through a Seek--Analyze loop over the trajectory, is the first practical model-based reward to match or surpass native rule-based supervision in online RL.

Yang Wan, Zhenhao Zhang, Jie-Rui Wang et al. · 0 citations

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