Scaling computer-use agents from individual actions to complex software work requires experience that connects decisions to complete outcomes. We present NorthCUA, an integrated system for turbochargi
NorthCUA scales computer-use agents via an experience flywheel, recursive self-improvement, staged learning, and reinforcement learning to achieve state-of-the-art open-source GUI and web interaction results.
Scaling computer-use agents from individual actions to complex software work requires experience that connects decisions to complete outcomes. We present NorthCUA, an integrated system for turbocharging computer-use agents through experience at scale. Its Experience Flywheel supplies diverse executable tasks to the NorthCUA Harness, which sustains long interactions, records decisions, and checks task outcomes. The Flywheel uses these records to curate verified trajectories. For unsolved tasks, recursive self-improvement evolves reusable skills and preserves verified milestones so later attempts build on earlier progress. Staged Learning combines this experience with grounding data from the…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
NorthCUA Technical Report: Turbocharging Computer-Use Agents through Experience at Scale was first seen on Hugging Face · Daily papers on Nov 3, 2026. Since then it has appeared in 12 checks on 2 different days. Its best position was #7 of 30, reached on Nov 3, 11:28 UTC. It dropped off the list after Nov 4, 03:28 UTC.
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It was first seen on Nov 3, 2026 and has been on the list on 2 days (12 checks) since.