37 citations · 129 across the 31 of their papers we have counts for
33 papers
CURA: Certified Runtime Alarms for Computer-Use Agents
Divake Kumar, Sina Tayebati, Devashri Naik +5
Self-report is the cheapest oversight channel a deployer has, and on capable computer-use agents (CUAs) it fails precisely where oversight matters. On 361 OSWorld tasks our pipelin…
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…
Synthetic Data Generation for Long-Tail Medical Image Classification: A Case Study in Skin Lesions
Jiaxiang Jiang, Mahesh Subedar, Omesh Tickoo
Long-tailed class distributions are pervasive in multi-class medical datasets and pose significant challenges for deep learning models which typically underperform on tail classes…
Parts-Mamba: Augmenting Joint Context with Part-Level Scanning for Occluded Human Skeleton
Tianyi Shen, Huijuan Xu, Nilesh Ahuja +3
Skeleton action recognition involves recognizing human action from human skeletons. The use of graph convolutional networks (GCNs) has driven major advances in this recognition tas…
PEFA-AI: Advancing Open-source LLMs for RTL generation using Progressive Error Feedback Agentic-AI
Athma Narayanan, Mahesh Subedar, Omesh Tickoo
We present an agentic flow consisting of multiple agents that combine specialized LLMs and hardware simulation tools to collaboratively complete the complex task of Register Transf…
Windsock is Dancing: Adaptive Multimodal Retrieval-Augmented Generation
Shu Zhao, Tianyi Shen, Nilesh Ahuja +2
Multimodal Retrieval-Augmented Generation (MRAG) has emerged as a promising method to generate factual and up-to-date responses of Multimodal Large Language Models (MLLMs) by incor…