The Flow has not summarised this story yet — read it at arXiv Computation and Language.
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.
OpenGCUA is an open‑stack project that aims to make generalist computer‑use agents (GCUs) fully accessible to the research community. The first release focuses on code execution, allowing agents to use intermediate results and programming skills directly in computer interaction, and introduces harness‑to‑model decision transfer to shift observation and task‑state decisions from fixed rules to the model. On the OSWorld‑v2 benchmark, the full configuration boosts task scores for all eight evaluated models—including GPT‑6 Astra and Claude Opus 5—by an average of +19.6 partial‑score points and cuts steps per task by 53 % compared to batched GUI‑only execution, while also providing a real‑device harness and cross‑device SDK. whyItMatters":"By releasing the full stack, OpenGCUA enables systematic study and broader experimentation with generalist computer‑use agents, addressing the current limitation of closed implementations and demonstrating significant performance gains across diverse models."
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.