Uncertainty Quantification for Computer-Use Agents: A Benchmark across Vision-Language Models and GUI Grounding Datasets
arXiv:2606.25760v1 Announce Type: new Abstract: Computer-use agents turn vision-language model (VLM) predictions into executable GUI clicks, so reliable uncertainty estimates are essential for rejection, calibration, miss-severity ranking, and spatial safety regions. Yet evidence on post-hoc uncertainty quantification (UQ) for these agents is fragmented across isolated model and dataset pairs, leaving it unclear whether UQ rankings stay stable when the agent, benchmark, or observable interface changes. We present Argus, a cross-regime benchmark for post-hoc UQ in single-step executable GUI gro
The rapid advancement and deployment of AI agents in various applications necessitate robust methods for understanding and managing their reliability, especially as they interact with critical systems.
Reliable uncertainty quantification for AI agents is crucial for their safe and effective integration into complex workflows, enabling better decision-making, error mitigation, and user trust.
The introduction of a standardized benchmark specifically for uncertainty quantification in computer-use agents allows for systematic evaluation and improvement, moving beyond fragmented evidence.
- · AI Agent Developers
- · Enterprise Software
- · Automation Sector
- · Academic Researchers
- · Unreliable AI Agent Startups
- · Companies with Poor UQ Practices
- · Manual Workflow Providers
Improved reliability and trustworthiness of AI agents lead to faster adoption across industries.
Increased demand for specialized tooling and expertise in uncertainty quantification and AI safety.
The benchmark could become a de facto standard, accelerating competitive development and integration of highly reliable autonomous systems.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG