Modern LLMs: What the FLIP is an Engram!?

Engrams are a proposed LLM component that supplements standard token embeddings by representing hashed sequences of multiple tokens, such as bigrams and trigrams, rather than individual tokens alone. They are described as a form of “conditional memory,” meaning learned, fixed associations for common local sequences rather than flexible or explicit memory. By directly supplying representations for frequent token combinations, engrams can reduce the burden on attention and perceptron layers, which otherwise must infer how separate subword tokens form concepts and phrases. Because the number of possible sequences grows rapidly with vocabulary size, engram systems use fixed-size hash tables that permit collisions, often combining multiple tables and hashes to improve aggregate uniqueness. Hierarchical sequence lengths and confidence gates help balance usefulness against sparse training data, allowing common patterns to contribute strongly while reducing the influence of rare or unreliable sequences.

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