VeriCUA: Verification as Agentic Exploration for Computer-Using Agents
- Published
- Source
- arXiv
- Paper number
- 1387
- Field
- AI Agents
- arXiv ID
- 2610.40578
Key points
- The verifier first inspects the available environment or recording to gather context, then builds a fine-grained rubric for the instruction and selects GUI or code checks for each requirement.
- It labels evidence as attested or asserted sources, so the executing agent's completion claim alone cannot mark a requirement as met.
- In the online AndroidWorld environment it achieves the highest accuracy of 90.5% and the lowest false positive rate of 7.7% among the compared baselines.
- In offline verification it reaches accuracy of 70.4 on OSReward-Hard, ahead of all compared judges, with a larger gap on hard cases.
- Adding code-based state reads raises the decision rate and recall with false positives unchanged, and adding a visual inspection pass lifts both agreement and decision rate to 87%.
- It rejects about 80% of false completion reports across the three online environments, with the largest difference on ScienceBoard.
Paper links
External research summaries. These are not HDATF publications or measured product results.