24 papers
Uncertainty Quantification for Computer-Use Agents: A Benchmark across Vision-Language Models and GUI Grounding Datasets
Divake Kumar, Sina Tayebati, Devashri Naik +5
Computer-use agents turn vision-language model (VLM) predictions into executable GUI clicks, so reliable uncertainty estimates are essential for rejection, calibration, miss-severi…
GroundControl: Anticipating Navigation Failures in Vision-Language Agents via Trajectory-Consistent Uncertainty Estimates
Nastaran Darabi, Divake Kumar, Sina Tayebati +2
Vision-language navigation agents achieve competitive average success on benchmark tasks, yet failures often arise through predictable trajectory-level breakdowns such as oscillati…
When Robots Sleep: Offline Skill Consolidation for Shared-Policy Robot Learning
Nethmi Jayasinghe, Diana Gontero, Amit Ranjan Trivedi
Robots that learn over long deployments must add new skills without losing the shared policy structure that makes earlier skills reusable. We study sequential robot skill learning,…
VLM Judges Can Rank but Cannot Score: Task-Dependent Uncertainty in Multimodal Evaluation
Divake Kumar, Sina Tayebati, Devashri Naik +2
Vision-language models (VLMs) are increasingly used as automated judges for multimodal systems, yet their scores provide no indication of reliability. We study this problem through…
Structural Verification for Reliable EDA Code Generation without Tool-in-the-Loop Debugging
Dinithi Jayasuriya, Aravind Saravanan, Nilesh Ahuja +2
Large language models (LLMs) have enabled natural-language-driven automation of electronic design automation (EDA) workflows, but reliable execution of generated scripts remains a…
ProGAL-VLA: Grounded Alignment through Prospective Reasoning in Vision-Language-Action Models
Nastaran Darabi, Amit Ranjan Trivedi
Vision language action (VLA) models enable generalist robotic agents but often exhibit language ignorance, relying on visual shortcuts and remaining insensitive to instruction chan…