Json Zhou, a researcher specializing in robotics and artificial intelligence, has published work spanning algorithms, computer vision, generative AI, and world simulation. His primary focus is on adva…
NVIDIA researcher Yunze Man focuses on robotics as part of the company's AI research division. His work contributes to NVIDIA's broader efforts in spatial intelligence, computer vision, and autonomous…
Researchers introduced Gated DeltaNet-2, a linear attention model that decouples the erase and write operations in recurrent state updates using separate channel-wise gates. The model outperforms Mamb…
Muhammad Khalifa, a research scientist at NVIDIA, is advancing AI agents through his work on test-time techniques, reasoning agents, and LLM post-training. He completed his PhD at the University of Mi…
Shuai Yang, a researcher specializing in artificial intelligence and machine learning, has been identified as a key contributor to recent advancements in the field. Yang's work focuses on developing n…
Xiangyu Chen is a Senior Research Engineer in the Autonomous Vehicle Research Group at NVIDIA, where he focuses on translating heterogeneous multimodal data into learning objectives for robust autonom…
Jason Stock, a researcher specializing in artificial intelligence, machine learning, computer vision, and generative AI, focuses his work on climate simulation. His contributions to the field are docu…
Researchers have developed PiD, a pixel diffusion decoder that directly transforms latent representations into high-resolution images, bypassing the traditional decode-then-super-resolve pipeline. The…
Christian Jacobsen, a researcher specializing in artificial intelligence and machine learning, published a new study on May 20, 2026. The work advances understanding in the field of AI and machine lea…
NVIDIA released Nemotron-Labs-Diffusion, a tri-mode language model that unifies autoregressive, diffusion, and self-speculation decoding within a single architecture. The model, trained with a joint A…
Researchers introduced Iterative Group Relative Policy Optimization (iGRPO), a two-stage reinforcement learning method that improves large language model reasoning by having the model generate and ref…
Researchers have developed RLP, an information-driven reinforcement pretraining objective that integrates exploration and chain-of-thought reasoning into the pretraining phase of large language models…
Researchers have developed Progressive Self-Correction (ProSeCo), a framework that enables masked diffusion models to correct previously generated tokens rather than leaving them fixed. The method reu…