6 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…
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…
TRIAGE: Type-Routed Interventions via Aleatoric-Epistemic Gated Estimation in Robotic Manipulation and Adaptive Perception -- Don't Treat All Uncertainty the Same
Divake Kumar, Sina Tayebati, Devashri Naik +4
Most uncertainty-aware robotic systems collapse prediction uncertainty into a single scalar score and use it to trigger uniform corrective responses. This aggregation obscures whet…
Uncertainty Quantification in Continual Open-World Learning
Amanda S. Rios, Ibrahima J. Ndiour, Parual Datta +3
AI deployed in the real-world should be capable of autonomously adapting to novelties encountered after deployment. Yet, in the field of continual learning, the reliance on novelty…
CONCLAD: COntinuous Novel CLAss Detector
Amanda Rios, Ibrahima Ndiour, Parual Datta +2
In the field of continual learning, relying on so-called oracles for novelty detection is commonplace albeit unrealistic. This paper introduces CONCLAD ("COntinuous Novel CLAss Det…
CUAL: Continual Uncertainty-aware Active Learner
Amanda Rios, Ibrahima Ndiour, Parual Datta +3
AI deployed in many real-world use cases should be capable of adapting to novelties encountered after deployment. Here, we consider a challenging, under-explored and realistic cont…