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.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2608. 05587v1 Announce Type: new Abstract: Autonomous mobile GUI agents require accurate action reflection for reliable long-horizon execution.
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:2609.12394v2 Announce Type: replace Abstract: Mobile GUI agents are shifting from multi-module frameworks to native models trained end-to-end, yet industrial deployment faces three persistent g...
arXiv:2605. 25160v2 Announce Type: replace Abstract: GUI agents powered by large language models are advancing rapidly, creating urgent needs for evaluation and training based on realistic environments.
GUI-CC is a benchmark designed to assess the contextual consistency of GUI world models when used as agent environments, rather than just one‑step next‑screen predictors. It includes two tracks: an offline reference‑action track that rolls models along real mobile GUI trajectories, and an online agent‑loop track where fixed probing agents interact with model‑generated UIs. The benchmark evaluates transition fidelity, plausibility, contextual consistency, and task progress across 500 offline trajectory tasks and 200 online tasks spanning 30 mobile apps.
arXiv:2607. 04425v2 Announce Type: replace-cross Abstract: Recent advances in multimodal foundation models and agent systems have driven GUI agents from single-platform task execution toward cross-platform interaction.