A Hugging Face blog post explains “engrams,” a technique used in modern large language models where embeddings hash sequences of multiple tokens together rather than encoding individual tokens in isolation. This gives the model a fast associative memory for common token sequences, freeing attention and perceptron layers to concentrate on rarer patterns and higher-level linguistic structure. The author frames engrams as a mechanism that complements attention rather than replacing it.
