arXiv AI
GUI-PRA: Process Reward Agent for GUI Tasks
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jul 29
Interactive Reward Agent: GUI Task Evaluation via Environment-State Verification
arXiv:2607. 25904v1 Announce Type: new Abstract: Graphical user interface task evaluation aims to determine whether a GUI agent has successfully completed a user instruction.
arXiv AI
Aug 7
StepReflect: Structured UI Transition Reflection for Mobile GUI Agents
arXiv:2608. 05587v1 Announce Type: new Abstract: Autonomous mobile GUI agents require accurate action reflection for reliable long-horizon execution.
arXiv AI
Jun 10
A History-Aware Visually Grounded Critic for Computer Use Agents
arXiv:2606. 11078v1 Announce Type: new Abstract: Various test-time interventions for Computer Use Agents (CUAs), including critic models, have been developed to improve performance through pre-execution action evaluation in complex Graphical User Interface (GUI) environments.
arXiv AI
Aug 24
Automated Trajectory Evaluation for Mobile Agents via Step-Level Consequence Reasoning and Aggregation
The paper introduces CRATE, a two‑stage vision‑language model framework that evaluates mobile agents by reasoning about each step’s consequences and aggregating this evidence to assess task completion. It also presents CRATE‑S, an extension that evaluates operational safety. Experiments show CRATE and CRATE‑S outperform existing benchmarks, achieving high F1‑scores on AndroidWorld and MobileRisk datasets.
Hugging Face Trending Papers
Aug 6
StepReflect: Structured UI Transition Reflection for Mobile GUI Agents
Autonomous mobile GUI agents require accurate action reflection for reliable long-horizon execution. Existing approaches rely on open-ended multimodal reasoning after each action, which is costly and poorly matched to the structured nature of GUI state transitions.
arXiv AI
Jun 24
Reinforcement Learning for Computer-Use Agents with Autonomous Evaluation
arXiv:2606. 24515v1 Announce Type: new Abstract: Computer-Use Agents (CUAs) execute high-level user goals by perceiving and acting directly within graphical user interfaces.