11 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…
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…
Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning
Ranganath Krishnan, Piyush Khanna, Omesh Tickoo
Large language models (LLMs) have revolutionized the field of natural language processing with their impressive reasoning and question-answering capabilities. However, these models…
EigenTrack: Spectral Activation Feature Tracking for Hallucination and Out-of-Distribution Detection in LLMs and VLMs
Davide Ettori, Nastaran Darabi, Sina Tayebati +4
Large language models (LLMs) offer broad utility but remain prone to hallucination and out-of-distribution (OOD) errors. We propose EigenTrack, an interpretable real-time detector…
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…