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Current models struggle with hybrid interface tasks, achieving only a 41.2% success rate, underscoring a critical gap in CUA evaluation.
Draft-OPD accelerates inference by over 5x while improving speculative decoding accuracy, transforming how draft models learn from target feedback.
Decomposing GUI agent trajectories into verifiable milestones and auditing the evidence chain yields a 10% boost in RL training performance, outperforming single-judge reward systems.
