NVIDIA’s open models and infrastructure have become central to AI research, as evidenced by their citations in 145 papers at ICML 2026. The conference revealed a growing emphasis on open frontier models and AI infrastructure, with NVIDIA’s contributions playing a key role in shaping the field. NVIDIA had 74 papers accepted at ICML 2026, underscoring the company’s significant presence in the research community. These models and tools are being used across various domains, including robotics, autonomous vehicles, and biomedical research, to accelerate innovation and reduce development costs. The open infrastructure allows researchers to build upon existing models, datasets, and training recipes, fostering collaboration and reproducibility in AI development. This approach has enabled new advancements in areas such as synthetic data generation and AI for life sciences, where open models are helping researchers better understand complex biological systems and drug discovery processes. The trend toward open models reflects a broader shift in how AI research is conducted, with a focus on accessibility, scalability, and shared progress. The momentum extends beyond NVIDIA, with other organizations and companies adopting these open models to drive their own research and development efforts. This collaborative environment is expected to continue shaping the future of AI research and its applications across industries.
NVIDIA’s open models, including the Nemotron family and Cosmos 3, are being used as foundational tools for AI research. The Nemotron models, in particular, have been cited in multiple papers, with some researchers using them as a research stack that includes open weights, datasets, and training recipes. The open infrastructure also supports synthetic data generation, which is becoming increasingly important as researchers seek to train AI systems without relying solely on human-labeled data. This shift is evident in the growing number of papers that highlight the use of open models for tasks such as protein function prediction and molecular property analysis. The open models are also being integrated into various platforms and tools, making them more accessible to a wider range of researchers and developers. These efforts are helping to lower the barriers to entry for AI research, enabling more scientists and engineers to contribute to the field. The trend toward open models is not just about accessibility but also about fostering innovation through collaboration and shared knowledge. As the field of AI continues to evolve, the role of open models and infrastructure will likely become even more critical in driving progress and addressing complex challenges.
The open research stack, including tools like NeMo Curator and the open datasets it supports, provides researchers with a reproducible foundation for training data curation. This approach enables the creation of high-quality training sets at a scale and speed that would have been impractical just a few years ago. The Cosmos 3 family of open, frontier omnimodels is particularly notable for its ability to help researchers and developers build robots, autonomous vehicles, and vision AI systems that can perceive, reason, plan, and act in the physical world. These models are also being used in the development of autonomous vehicles and robotics, with companies like Humanoid, LG Electronics, and NEURA Robotics adopting NVIDIA Isaac GR00T models to accelerate their industrial deployments. The widespread adoption of these open models underscores their importance in driving innovation across multiple industries and research fields.
Source: nvidia
