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Existing benchmarks for GUI agents primarily focus on increasing task difficulty by extending task horizons. Besides, enabling agents to explore unfamiliar environments and successfully complete tasks within them is also a critical yet challenging problem. We introduce LatentGUIWorld, a benchmark of six tasks in three...
This work introduces MARCO, an evaluator-grounded reinforcement-learning framework that trains molecular editors on bounded proposal--feedback--revision trajectories and aggregates shaped turn rewards into an undiscounted trajectory return for group-relative policy optimization.
It is found that memory can improve UAQ performance in some settings, but such gains are selective rather than universal and remain fragile under dataset shift, suggesting that reliable UAQ memory depends less on storing larger amounts of experience and more on preserving transferable behavioral guidance.
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