👉 Liquid Foundation Models (LFMs) are large neural networks that go beyond the traditional Transformer architecture, instead being built from a custom design space of computational units inspired by Liquid Time-constant Networks (LTCs), deep signal processing layers, and other advanced computational primitives. These models are designed to be more expressive and efficient than conventional neural networks, allowing for better performance on complex tasks like video analysis, audio processing, and natural language understanding. LFMs are trained using a proprietary training process that combines first-principles modeling with deep learning, ensuring they can capture intricate patterns and dynamics in data while maintaining computational efficiency and scalability.
