Alex Wang explains transformer attention and feed-forward layers starting from associative memory that stores keys and values. His experiments show how interference between keys limits linear memory, while nonlinear activation and softmax improve discrimination. Related 2021 research also analyzes feed-forward keys responding to text patterns and values connecting to output-word distributions. These simplified memory experiments and layer interpretations do not directly measure factual storage across a trained model or guarantee accurate recall.