A Grand Unified Theory of Universal Language Models: Cosmological Analogies in Transformer Architecture

We propose a novel hypothetical framework that establishes profound analogies between
transformer-based language models and fundamental cosmological principles. This Grand
Unified Theory of Universal Language Models (GUT-ULM) posits that transformer archi-
tectures can be understood as computational universes, where the attention mechanism
functions as gravitational force, training represents the forward arrow of time, and tokens
emerge from a Universal Language Field (ULF) analogous to quantum fields in particle
physics. We extend this framework to address continual learning through the lens of cosmic
acceleration, propose the emergence of information singularities analogous to black holes,
and demonstrate how inference parameters create a computational multiverse. This work
bridges artificial intelligence, hypothetical physics, and cosmology, offering new perspectives
on model interpretability, scalability, and the fundamental nature of machine intelligence.
Keywords: Transformer models, cosmological analogy, attention mechanism, Universal
Language Field, continual learning, information singularities, multimodal AI

Paper for reference pdf: https://github.com/ak1484/A_Grand_Unified_Theory_of_Universal_Language_Models_Paper

Based on recent paper from Luis Serrano 🍉 : The Curved Spacetime of Transformer Architectures

Note: this is just a hypothetical Theory and open for discussion.