Inference-time scaling helps frontier models but doesn't reliably improve local computer-use agents; success requires failure-aware design and selective compute allocation rather than just adding more computation.
This paper studies how to make computer-use agents (AI systems that control computers) work better on local devices with limited hardware. The researchers test different ways to use extra computation during execution—like remembering more history, planning longer, or trying multiple approaches in parallel—on smaller AI models.