/*
Mathematical frameworks trained on data that
learn to map inputs to desired outputs, enabling
machines to perceive, reason, and act autonomously.
*/
At Nyrus, our models are designed to combine cutting-edge capability with inherent alignment, integrating advances across embodied, scientific, and foundational domains. We build generalist agents that can reason, reflect, and adapt across tasks, whether operating in the physical world, accelerating scientific discovery, or navigating complex information landscapes. Our architectures go beyond scale for its own sake, focusing instead on modularity, world modeling, and meta-learning to foster genuine understanding. Embedded within every model are robust interpretability tools that make their internal representations and goals transparent, enabling proactive control and human oversight at every level of deployment.