The Unreasonable Effectiveness of Scaling Agents for Computer Use
Authors: name: Gonzalo Gonzalez-Pumariega, name: Vincent Tu, name: Chih-Lun Lee, name: Jiachen Yang, name: Ang Li, name: Xin Eric Wang
Organization: Simular

Computer-use agents (CUAs) hold promise for automating everyday digital
tasks, but their unreliability and high variance hinder their application to
long-horizon, complex tasks. We introduce Behavior Best-of-N (bBoN), a method
that scales over agents by generating multiple rollouts and selecting among
them using behavior narratives that describe the agents' rollouts. It enables
both wide exploration and principled trajectory selection, substantially
improving robustness and success rates. On OSWorld, our bBoN scaling method
establishes a new state of the art (SoTA) at 69.9%, significantly outperforming
prior methods and approaching human-level performance at 72%, with
comprehensive ablations validating key design choices. We further demonstrate
strong generalization results to different operating systems on
WindowsAgentArena and AndroidWorld. Crucially, our results highlight the
unreasonable effectiveness of scaling CUAs, when you do it right: effective
scaling requires structured trajectory understanding and selection, and bBoN
provides a practical framework to achieve this.