arXiv:2606. 12817v2 Announce Type: replace Abstract: Understanding the digital world on mobile devices is shifting from static UI perception to dynamic action comprehension.
arXiv:2606. 12817v1 Announce Type: new Abstract: Understanding the digital world on mobile devices is shifting from static UI perception to dynamic action comprehension.
arXiv:2609.39547v1 Announce Type: new Abstract: GUI agents built on large language and vision-language models still struggle on unseen applications and complex multi-step tasks, as completing real GU...
arXiv:2511. 07332v2 Announce Type: replace-cross Abstract: Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen elements.
Vision-Language Models (VLMs) based GUI agents stand to benefit significantly from online reinforcement learning (RL). However, their training is bottlenecked by two fundamental issues: current data s...
arXiv:2608.22847v1 Announce Type: new Abstract: Vision-Language Models (VLMs) based GUI agents stand to benefit significantly from online reinforcement learning (RL). However, their training is bottl...
arXiv:2606. 31410v1 Announce Type: new Abstract: Graphical user interface (GUI) agents build on vision-language models to complete user tasks end-to-end in real applications through interface actions such as tapping, swiping, text entry, and navigation.
The paper introduces VGEBench, a new benchmark for evaluating Vision‑Language Models (VLMs) on generalizable, visually grounded exploration of household devices. Unlike existing datasets that rely on static images or annotated trajectories, VGEBench employs a logic‑driven state machine to simulate multi‑turn interaction loops, requiring agents to actively perceive, act, and refine their actions to achieve goals. Experiments show that current VLMs struggle to translate semantic knowledge into physical execution and to maintain long‑horizon state tracking.
arXiv:2603. 26266v3 Announce Type: replace Abstract: Large vision-language models have endowed GUI agents with strong general capabilities for interface understanding and interaction.
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.
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.
arXiv:2607. 18046v1 Announce Type: new Abstract: Graphical User Interface (GUI) agents powered by vision-language models hold promise for automating real-world mobile tasks.
The paper introduces SEE, a two-stage framework for generating long-horizon GUI agent trajectories. First, an exploration stage builds an explicit UI transition graph over screens and elements. Second, a graph-based synthesis stage composes diverse multi-step trajectories through planning and controlled sampling, preventing spurious cycles and enabling long-horizon composition. Across real-world apps, SEE produces trajectories averaging 14.8 steps and improves agent task success and generalization to unseen screens.