👉 The fluid response, often associated with Liquid Foundation Models (LFMs), refers to a class of neural network models that generalize the concept of Liquid Time-constant Networks (LTCs) and other computational units beyond traditional transformer architectures. These models are designed to be expressive, efficient, and capable of capturing complex patterns in data, while maintaining a high degree of interpretability. Unlike conventional models that rely on predefined layers and operations, fluid responses use a composition of computational units that can dynamically adapt to the input data, allowing for flexible and efficient processing. This adaptability enables them to handle a wide range of tasks with varying complexities, from natural language understanding to time-series prediction, all while maintaining a compact memory footprint and computational efficiency.
