👉 A selection fluid, often used in Liquid Foundation Models (LFMs), is a dynamic, learnable parameter that allows the model to adapt its internal representation based on the input it receives. It enables the model to selectively focus on or de-emphasize certain parts of the input sequence, enhancing its ability to handle complex and variable data patterns. This fluid mechanism is learned during training, allowing the model to optimize its responses for specific tasks or inputs, thus improving performance and flexibility in diverse applications.
