6 papers
Cosmos 3: Omnimodal World Models for Physical AI
NVIDIA, :, Aditi +293
We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…
SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits
Edward Lin, Sahil Modi, Siva Kumar Sastry Hari +30
As agentic AI systems become increasingly capable of generating and optimizing GPU kernels, progress is constrained by benchmarks that reward speedup over software baselines rather…
InfoMamba: An Attention-Free Hybrid Mamba-Transformer Model
Youjin Wang, Jiaqiao Zhao, Rong Fu +5
Balancing fine-grained local modeling with long-range dependency capture under computational constraints remains a central challenge in sequence modeling. While Transformers provid…
VibeTensor: System Software for Deep Learning, Fully Generated by AI Agents
Bing Xu, Terry Chen, Fengzhe Zhou +12
VIBETENSOR is an open-source research system software stack for deep learning, generated by LLM-powered coding agents under high-level human guidance. In this paper, "fully generat…
PAI-Bench: A Comprehensive Benchmark For Physical AI
Fengzhe Zhou, Jiannan Huang, Jialuo Li +2
Physical AI aims to develop models that can perceive and predict real-world dynamics; yet, the extent to which current multi-modal large language models and video generative models…
Generalized Neighborhood Attention: Multi-dimensional Sparse Attention at the Speed of Light
Ali Hassani, Fengzhe Zhou, Aditya Kane +13
Many sparse attention mechanisms such as Neighborhood Attention have typically failed to consistently deliver speedup over the self attention baseline. This is largely due to the l…