Uncertainty Quantification for Computer-Use Agents: A Benchmark across Vision-Language Models and GUI Grounding Datasets
📰 ArXiv cs.AI
Learn to quantify uncertainty in computer-use agents using vision-language models and GUI grounding datasets, crucial for reliable executable GUI clicks
Action Steps
- Apply uncertainty quantification techniques to vision-language models using GUI grounding datasets
- Evaluate the performance of different models and datasets using benchmarking metrics
- Analyze the stability of uncertainty quantification rankings across various agent and benchmark configurations
- Implement post-hoc uncertainty quantification methods for computer-use agents
- Compare the results of different uncertainty quantification methods for vision-language models
Who Needs to Know This
AI engineers and researchers working on computer-use agents can benefit from this benchmark to improve the reliability of their models, while data scientists can apply these techniques to other applications
Key Insight
💡 Uncertainty quantification is essential for reliable executable GUI clicks in computer-use agents
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🤖📊 Quantify uncertainty in computer-use agents with vision-language models and GUI grounding datasets 📈
Key Takeaways
Learn to quantify uncertainty in computer-use agents using vision-language models and GUI grounding datasets, crucial for reliable executable GUI clicks
Full Article
Title: Uncertainty Quantification for Computer-Use Agents: A Benchmark across Vision-Language Models and GUI Grounding Datasets
Abstract:
arXiv:2606.25760v1 Announce Type: cross 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
Abstract:
arXiv:2606.25760v1 Announce Type: cross 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
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