Tokenized Rules of Inference as Physical Memes: From Metaphor to Mechanism

Since Dawkins introduced the concept of memes as cultural replicators, memetics has struggled

with a fundamental problem: identifying the physical substrate of memes. Unlike genes, which

have clear molecular instantiation, memes have remained frustratingly vague—patterns of neural

activity, behaviors, ideas—without measurable, isolable physical form. This paper argues that

transformer weight matrices in Large Language Models (LLMs) provide the first genuine

candidate for physically instantiated memes. Specifically, when rules of inference—both valid

and invalid—are tokenized, they exist in the exact representational space that transformers

operate on. The weight configurations that reliably regenerate these tokenizable inference

patterns constitute measurable, quantifiable information structures that replicate across training

contexts. This moves memetics from evocative analogy to empirical science, enabling

mechanistic study of how inference patterns compete, propagate, and evolve in computational

substrates.

Mathematical expressions are in marked down format.