Discover a subquadratic-time replacement for attention using long convolutions and gating, enabling processing of extremely long sequences.

Understand a foundation architecture balancing training parallelism, low-cost inference, and performance through flexible retention mechanisms.

Learn about an architecture combining transformer parallelizable training with RNN efficient inference through linear attention mechanisms.

Explore a selective State Space Model that addresses transformers' quadratic bottleneck with linear scaling and significantly faster inference speeds.

Discover models inspired by control theory that provide an efficient alternative to transformers for handling long-range dependencies in sequential data.

Understand how this architecture uses a single-expert routing mechanism to scale models to trillions of parameters efficiently.

Learn about an architecture that divides neural networks into specialized sub-networks, enabling massive scale without proportional computational cost.

Explore compact AI models with millions to billions of parameters, designed for efficient deployment on edge devices and resource-constrained environments.

Discover multimodal foundation models that integrate vision, language, and action to enable robots to perceive, understand instructions, and execute physical tasks.

Discover how AI enables autonomous systems to perceive, understand, and perform complex actions by incorporating spatial relationships and physical laws.