Token Space: A Category Theory Framework for AI Computations

Abstract:We introduce the Token Space, a categorical framework for AI computations. A Token is a finite tuple whose entries are elements of a carrier set or symbols of a fixed core; a Token class is a set together with a heap of such Tokens, and Token maps are the functions preserving every Token. The Token Space is built from the category of sets by adjoining identity set categories, forming products and taking a subsets extension. We prove that the resulting categories have all finite limits, finite coproducts and exponentials, but, unlike Set, are not topoi. We then introduce algebraic tokenization: the constants, relations and graphs of operations of a structured set are recorded as Tokens headed by a core symbol. This gives a full and faithful embedding of every finitary category of structured objects (pointed sets, orders, graphs, rings, vector spaces) into the Token Space which preserves binary products and equalizers; topological spaces embed faithfully. Tree Tokens capture nested structure, and a calculus of operators acts on Token classes. As applications we describe sequence data and self-attention layers of Transformers: permutation equivariant layers are exactly the Token maps between sequence classes, and layers are points of exponential classes, so that architectures are Token maps while parameters are points of bases. Knowledge distillation becomes structure-preserving compression: a student is faithful to a teacher iff it is a Token map from the teacher-induced class, symmetries of the teacher are inherited by the student, and the smallest compression that neither loses nor invents a structural fact is the quotient by an indiscernibility congruence.

General Mathematics (math.GM); Machine Learning (cs.LG)

arXiv:2404.11624 [math.GM]

(or arXiv:2404.11624v2 [math.GM] for this version)

https://doi.org/10.48550/arXiv.2404.11624 Focus to learn more arXiv-issued DOI via DataCite

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