7 papers · 1 filter
Improving Dynamic Object Interactions in Text-to-Video Generation with AI Feedback
Hiroki Furuta, Heiga Zen, Dale Schuurmans +4
Large text-to-video models hold immense potential for a wide range of downstream applications. However, they struggle to accurately depict dynamic object interactions, often result…
Affordances Enable Partial World Modeling with LLMs
Khimya Khetarpal, Gheorghe Comanici, Jonathan Richens +5
Full models of the world require complex knowledge of immense detail. While pre-trained large models have been hypothesized to contain similar knowledge due to extensive pre-traini…
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…
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…
ElasticTok: Adaptive Tokenization for Image and Video
Wilson Yan, Volodymyr Mnih, Aleksandra Faust +3
Efficient video tokenization remains a key bottleneck in learning general purpose vision models that are capable of processing long video sequences. Prevailing approaches are restr…
Exposing Limitations of Language Model Agents in Sequential-Task Compositions on the Web
Hiroki Furuta, Yutaka Matsuo, Aleksandra Faust +1
Language model agents (LMA) recently emerged as a promising paradigm on muti-step decision making tasks, often outperforming humans and other reinforcement learning agents. Despite…