Skip to content

CUA-Universe: A Scalable and Dynamic Environment for Hybrid GUI+CLI Agents

Sep 2026 · 5 citations · ⚡ 1 influential · 41 references
Computer Science

TL;DR

CUA-Universe is introduced, a scalable environment-to-data pipeline that turns real desktop software into hybrid GUI+CLI environments and training on this data shifts behavior from inefficient GUI interaction and brittle CLI scripting toward effective GUI+CLI orchestration.

Abstract

Computer-use agents have advanced on benchmarks like OSWorld and AndroidWorld, but still act mostly through the GUI, often producing inefficient trajectories. Real-world computer work is hybrid, combining visual-state inspection with precise, high-throughput command-line operations, so capable agents must coordinate both modalities over shared application state. Yet scalable hybrid environments remain scarce because supporting both GUI and CLI over real applications typically requires substantial manual engineering for each application. Existing agents also struggle to use the two interfaces complementarily: CLI-native agents lack visual perception for tasks involving interface state or layout, while GUI-native agents are inefficient for operations better executed through commands. We introduce CUA-Universe, a scalable environment-to-data pipeline that turns real desktop software into hybrid GUI+CLI environments. App-Forge adapts applications into reproducible VMs and command-line surfaces it discovers, wraps, or generates, scaling to 16 applications; Task-Weave synthesizes diverse hybrid tasks of controllable difficulty from reusable operations over seed files; and Path-Steer steers rollouts along efficient hybrid paths and harvests verified trajectories for post-training. Training on this data shifts behavior from inefficient GUI interaction and brittle CLI scripting toward effective GUI+CLI orchestration. Our 9B model improves both success and efficiency on CUA-Verse (Score +39.3 pts; -37% steps, -60% tokens), OSWorld (SR +16.8 pts; -57% steps, -44% tokens), and OSWorld-MCP (Score +7.84 pts; -27% steps, -30% tokens). CUA-Universe provides a scalable path toward more capable and efficient computer-use agents.

View source

Similar papers

#artificial intelligence Preprint Sep 2026

JarvisGUI: Towards Cross-Device GUI Agents with Dynamic Task Composition

JarvisGUI reveals that state-of-the-art open-source GUI agents struggle with the state-transfer awareness, cross-platform contextual reasoning, and long-horizon dependency management required for real-world workflows, exposing a critical capability gap invisible to existing benchmarks.

Zi-Xiang Chen, Yu-Heng Lu, Zihao Cheng et al. · 1 citation
#artificial intelligence Preprint Aug 2026

UI-Venus-2 Technical Report

UI-Venus-2 is presented, a general-purpose foundation GUI agent designed to operate across mobile, web, and desktop environments through a unified closed-loop reasoning-action framework that integrates safety-aware mechanisms to ensure controlled execution of consequential actions.

Venus Team, Zhuo-Hang Cai, Hao-Xin Chen et al. · 4 citations
#artificial intelligence Preprint Oct 2026

CUAWright: A Minimal Unified Interface for Digital Agents

The prevailing approach to computer-use agents couples a model with a domain-specific harness: a browser or desktop environment equipped with human engineered tools that are fixed before task execution. As models'coding capabilities improve, the GUI native and static harness prevents them from direct programmatic opera...

Ya-Dong Lu, Theo Lee, Yi-Fei Li et al. · 0 citations
#artificial intelligence Preprint Sep 2026

APPSim-Bench: Bridging Real-world Apps and Reproducible Evaluation for Mobile GUI Agents

AppSim-Bench is proposed, which addresses this trade-off through controllable simulated apps that preserve task-relevant interaction logic while supporting deterministic evaluation and removes major sources of environmental stochasticity, enabling reproducible cross-model comparison.

Jin-Tian Feng, Long Chen, Xiao Yu et al. · 0 citations
Preprint Sep 2026

HybridCUA: Learning to Orchestrate GUI and CLI for Computer-Use Agents

Computer use agents (CUAs) have demonstrated strong capabilities in completing digital tasks. However, existing CUAs either rely solely on graphical user interface (GUI) interactions, which are often inefficient and error prone, or augment GUI interactions with application specific APIs or tools, which require substant...

Tong-I Chen, Jun-Bo Niu, Zhengxi Lu et al. · 0 citations

Efficient GUI Agents: A Systems Survey of Observation, Memory, Action, and Runtime Optimization

This survey studies efficient GUI agents through an end-to-end systems lens that preserves the current technical axes of observation efficiency, context and memory efficiency, action efficiency, and planner-side/system efficiency.

Bizhe Bai, Jiakang Yuan, Hong-Zhou Wu et al. · 0 citations

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.