arXiv:2607. 26041v1 Announce Type: new Abstract: Computer-use agents (CUAs) increasingly act through desktop GUIs to complete long-horizon tasks.
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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.
ComponentBench is a new benchmark that evaluates computer‑use agents at the component level on modern web UIs. It contains 97 canonical UI components and 2,910 programmatically verified tasks, along with cleaned human reference trajectories for measuring task success and interaction efficiency. The benchmark also offers a scalable pipeline for auditing structural difficulty and synthesizing failure analyses across tasks and component families.
arXiv:2609.22000v1 Announce Type: new Abstract: Computer-use agents (CUAs) have advanced along two separate lines: graphical interaction and software development through code and the command line. Re...
arXiv:2606. 17321v1 Announce Type: new Abstract: Training computer-use agents (CUAs) -- models that interact with graphical desktops through screenshots and keyboard/mouse actions -- requires large-scale, diverse trajectory data collected in full desktop environments.
VABench is a benchmark that tests general‑purpose multimodal large language models (MLLMs) on embodied spatial intelligence by requiring them to observe, reason, act, and revise based on visual demonstrations and active perception. The benchmark includes 14 task families, a fixed model‑agnostic controller, and evaluates models on target localization, spatial relations, and long‑horizon composition tracks without providing privileged object poses or learned action heads. Results show that while the best model achieves perfect target localization, overall task success remains modest, and active camera control and geometric transfer significantly influence performance.
arXiv:2609.13458v1 Announce Type: cross Abstract: Embodied language grounding requires more than identifying the referent of an instruction: recovered semantics must also control the action an agent...