Topos Theory for Generative AI and LLMs

TL;DR

This paper introduces a novel theoretical framework for large language models (LLMs) using topos theory, proposing new categorical architectures that leverage universal properties and compositional structures to enhance LLM design.

Contribution

It develops a topos-theoretic foundation for LLMs, constructing new architectures based on universal categorical properties and validating their theoretical completeness and set-like structure.

Findings

LLMs form a (co)complete category with universal constructions.

The category of LLMs is shown to be a topos, enabling new compositional architectures.

A functorial approach to backpropagation is proposed for implementation.

Abstract

We propose the design of novel categorical generative AI architectures (GAIAs) using topos theory, a type of category that is ``set-like": a topos has all (co)limits, is Cartesian closed, and has a subobject classifier. Previous theoretical results on the Transformer model have shown that it is a universal sequence-to-sequence function approximator, and dense in the space of all continuous functions with compact support on the Euclidean space of embeddings of tokens. Building on this theoretical result, we explore novel architectures for LLMs that exploit the property that the category of LLMs, viewed as functions, forms a topos. Previous studies of large language models (LLMs) have focused on daisy-chained linear architectures or mixture-of-experts. In this paper, we use universal constructions in category theory to construct novel LLM architectures based on new types of compositional structures. In particular, these new compositional structures are derived from universal properties of LLM categories, and include pullback, pushout, (co) equalizers, exponential objects, and subobject classifiers. We theoretically validate these new compositional structures by showing that the category of LLMs is (co)complete, meaning that all diagrams have solutions in the form of (co)limits. Building on this completeness result, we then show that the category of LLMs forms a topos, a ``set-like" category, which requires showing the existence of exponential objects as well as subobject classifiers. We use a functorial characterization of backpropagation to define a potential implementation of an LLM topos architecture.