Argus Benchmark Evaluates Uncertainty Quantification Stability Across Vision-Language Models and GUI Grounding Datasets
The authors introduce Argus, a benchmark designed to evaluate post-hoc uncertainty quantification for computer-use agents that translate vision-language model predictions into executable GUI actions. The study assesses 28 open-weight methods across four VLM agents and four datasets, alongside eight closed-source methods from three vendors where internal model states are inaccessible. Key findings reveal selective transfer stability, where uncertainty rankings remain consistent across different datasets for a fixed model but degrade significantly when moving between different model classes or observable interfaces. Among open-weight options, hidden-state and density estimation techniques demonstrated the highest stability, while specific regimes favored sampling-based scores or verbalized self-assessment. Within-model ranking transfer proved strong with Spearman rho values up to 0.969, whereas cross-tier transfer to closed-source vendors averaged only +0.08. The research further indicates that conformal click regions shrink radii by 40-60 percent upon calibration but suffer coverage degradation under interface mismatch. To support regime-aware selection, the authors release per-item records, calibration splits, UQ scores, and analysis scripts.