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cs.AI2026
SeekJudge: A Practical Reward Framework for Reinforcement Learning in Computer-Use Agents
Yang Wan, Zhenhao Zhang, Jierui Wang +2
Deciding whether a trajectory actually fulfills its instruction determines how we measure computer-use agents on long-horizon graphical-user-interface tasks and how we train them w…
cs.AI2026
Mitigating Conversational Inertia in Multi-Turn Agents
Yang Wan, Zheng Cao, Zhenhao Zhang +4
Large language models excel as few-shot learners when provided with appropriate demonstrations, yet this strength becomes problematic in multiturn agent scenarios, where LLMs erron…