arXiv:2606. 09426v1 Announce Type: new Abstract: Computer-use agents (CUAs) increasingly operate in runtimes that combine visual desktop control, command-line execution, code editing, browsers, and external tools.
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.
CUA-Universe is a scalable environment-to-data pipeline that transforms real desktop software into hybrid GUI+CLI environments, enabling agents to coordinate visual inspection with command-line operations. It includes App-Forge for reproducible VMs and CLI surfaces, Task-Weave for generating diverse hybrid tasks, and Path-Steer for efficient rollouts and trajectory harvesting. Training on this data improves agent success and efficiency across multiple benchmarks, demonstrating the value of hybrid interaction.
arXiv:2604. 13072v2 Announce Type: replace-cross Abstract: OpenClaw-style personal assistants extend LLM agents from isolated tool use to open-ended, stateful, and personalized software environments.
arXiv:2606. 07682v1 Announce Type: cross Abstract: AI agents are increasingly expected to complete long-horizon workflows that require sustained progress over hours, millions of tokens, and complex environments.
ProgramDistill is a benchmark that evaluates coding agents by having them infer and implement features discovered through interaction with fully functional reference web applications. The benchmark is built by factorizing applications into features of varying granularity, each linked to replayable behaviors that can be executed via a gold patch. Using a mine‑craft‑patch pipeline, the authors discovered 1,975 replay‑verified behaviors across 26 applications and automatically constructed 4,063 tasks, enabling evaluation of nine frontier coding agents, including GPT‑6 Astra and Claude Opus 5, on both full‑application and partial‑application reconstruction tasks.
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:2608. 02352v1 Announce Type: new Abstract: Native computer use offers a general interface for agents to operate almost any software available to people, but requires long-horizon state tracking, large-scale interactive experience, and learning from sparse yet verifiable outcomes.
arXiv:2609.21293v1 Announce Type: new Abstract: Autonomous software generation (ASG) aims to turn human requirements into executable applications, but delivering these applications does not necessari...
arXiv:2606. 06560v1 Announce Type: cross Abstract: Computer-use agents (CUAs) operate graphical user interfaces (GUIs) through vision and control primitives, and their capabilities have advanced rapidly, driven in part by standardized online evaluation benchmarks such as OSWorld, which serve both as evaluation tools and as training environments for reinforcement learning.
APPSim-Bench is a new benchmark for mobile GUI agents that uses controllable simulated apps to balance realism and reproducibility. It includes 557 tasks across 17 popular Chinese and English apps, with a coding-agent-assisted and human-verified workflow that ensures deterministic evaluation. Evaluation of 19 agents shows that autonomous mobile execution is still far from perfect, with the best model completing only 50.27% of tasks and many failures in longer workflows and numerical reasoning.
arXiv:2605. 25624v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven breakthroughs in domains such as math, tool-use, and software engineering, yet its extension to computer-use agents (CUAs) has been bottlenecked by the scarcity of scalable training data with deterministic rewards.