5 papers
Don't Run with Scissors: Pruning Breaks VLA Models but They Can Be Recovered
Jason Jabbour, Dong-Ki Kim, Max Smith +6
Vision-Language-Action (VLA) models have advanced robotic capabilities but remain challenging to deploy on resource-limited hardware. Pruning has enabled efficient compression of l…
Multi-Agent Reinforcement Learning for Sample-Efficient Deep Neural Network Mapping
Srivatsan Krishnan, Jason Jabbour, Dan Zhang +4
Mapping deep neural networks (DNNs) to hardware is critical for optimizing latency, energy consumption, and resource utilization, making it a cornerstone of high-performance accele…
Generative AI in Embodied Systems: System-Level Analysis of Performance, Efficiency and Scalability
Zishen Wan, Jiayi Qian, Yuhang Du +6
Embodied systems, where generative autonomous agents engage with the physical world through integrated perception, cognition, action, and advanced reasoning powered by large langua…
A2Perf: Real-World Autonomous Agents Benchmark
Ikechukwu Uchendu, Jason Jabbour, Korneel Van den Berghe +15
Autonomous agents and systems cover a number of application areas, from robotics and digital assistants to combinatorial optimization, all sharing common, unresolved research chall…
SocratiQ: A Generative AI-Powered Learning Companion for Personalized Education and Broader Accessibility
Jason Jabbour, Kai Kleinbard, Olivia Miller +2
Traditional educational approaches often struggle to provide personalized and interactive learning experiences on a scale. In this paper, we present SocratiQ, an AI-powered educati…