Computer-use agents are increasingly expected to handle long, multi-application workflows. The new arXiv preprint argues that in these settings, undetected errors are especially dangerous because they can silently corrupt persistent state, and that corruption can then cause cascading failures in subsequent actions.
To address this, the authors introduce CUA-Recovery, a benchmark designed to measure how reliably agents detect and recover from errors. The work focuses on the challenge of building agents that can recognise when something has gone wrong and take corrective action before the error propagates.
The benchmark is presented as a step toward more robust long-horizon computer use. The authors emphasise that reliable error detection and recovery are critical as these agents move from single tasks to extended workflows.