VisCritic: Visual State Comparison as Process Reward for GUI Agents

GUI agents powered by vision-language models show strongpotential for automating digital tasks, yet frequently fail in long-horizonscenarios due to the absence of step-level verification. Existing process re-ward models verify actions through textual reasoning alone, missing thevisual nature of GUI state changes. We introduce VisCritic, a visualprocess reward framework that verifies agent actions by directly com-paring pre-action and post-action screenshots in visual feature space.VisCritic employs a Siamese vision transformer to extract change-awarerepresentations, coupled with an Action-Aware Critic Head that jointlyevaluates action success, task progress, and error type. A critic-trainingdata construction pipeline generates weakly supervised samples from ex-isting trajectories without additional human labels for critic training.Experiments and offline analyses across five benchmarks demonstratethat VisCritic serves as a plug-and-play enhancement for diverse GUIagents, generally improving benchmark metrics while providing visualdiagnostic cues.