arXiv Artificial Intelligence

CUADebug: Diagnosing and Repairing Computer-Use Agent Failures

CUADebug: Diagnosing and Repairing Computer-Use Agent Failures

Quick summary

arXiv:2608.02643v2 Announce Type: replace-cross Abstract: Computer-use agents (CUAs) interact with graphical interfaces through screenshots and low-level mouse and keyboard actions, yet the causal error may precede the terminal failure. We present CUADebug, a framework for localizing root causes in CUA trajectories and guiding re-execution. CUADebug includes a five-category, 30-subtype taxonomy; CUAErrorBench, a benchmark of 204 failed OSWorld trajectories with human root-cause annotations; and CUADebugger, a ReAct-style agent for root-cause analysis (RCA). CUADebugger iteratively selects traj

Key takeaways

  • arXiv:2608.02643v2 Announce Type: replace-cross Abstract: Computer-use agents (CUAs) interact with graphical interfaces through screenshots and low-level mouse and keyboard actions, yet the causal error may precede the terminal failure.
  • We present CUADebug, a framework for localizing root causes in CUA trajectories and guiding re-execution.
  • CUADebug includes a five-category, 30-subtype taxonomy; CUAErrorBench, a benchmark of 204 failed OSWorld trajectories with human root-cause annotations; and CUADebugger, a ReAct-style agent for root-cause analysis (RCA).

Why it matters

The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗