Pathway

A post-transformer AI architecture and models that unify memory and reasoning to support in-context adaptation, latent reasoning, and persistent state tracking over long contexts while reducing test-time compute. It is built for developers and data teams in business and enterprise environments that require efficient reasoning and long-context processing. It is positioned as an alternative to transformers that integrates memory, adaptation, and inference in a single fabric using sparse local interactions and continual adjustment.

Key features

• Parametric memory with abstract thought representation
• Latent reasoning without chain-of-thought
• Sparse local neuron interactions
• Persistent state tracking
• In-context adaptation
• Multi-step reasoning over long contexts
• Continual learning and self-improvement
• Recurrent latent reasoning iterations