Hugging Face researchers have unveiled UI-Mate, a foundation GUI agent designed to automate complex digital tasks with greater reliability. The system integrates an environment-grounded training stack with in-context demonstration learning, addressing key challenges such as scarce training data, ambiguous prompts, and unreliable execution.
The training stack uses a closed-loop data engine that automates task generation, environment construction, rollout, filtering, capability balancing, supervised fine-tuning, and online reinforcement learning across massively parallel environments. This is complemented by a mechanism that transforms multimodal demonstrations into flexible subtask-level workflows, allowing the agent to follow relevant steps and re-plan from the live interface.
UI-Mate sets a new open-weight state of the art on general computer-use benchmarks, scoring 77.0% on OSWorld-Verified and 66.2% on WindowsAgentArena. On the new OSWorkerBench benchmark—100 long-horizon office tasks across 41 applications—it achieves 41.0% strict success and 76.9% progress, outperforming its Qwen3.6-27B base by 17.7 and 24.5 points respectively.
Notably, in the 33-task self-demo subset, a single demonstration raises strict success from 17.2% to 35.4% and progress from 67.9% to 81.1%, underscoring the value of in-context demonstrations for long-horizon reliability. The project page is available at https://ui-mate.github.io.