Time, Reality and Parallel worlds explained by LLMs

This paper proposes a generative architecture for reality, time and parallel worlds, drawing on the operation of large language models. A very large but finite collection of interacting components produces an evolving world-state, initialized by a Prompt Big Bang. Time is inference becoming history; perceived duration is hypothesized to depend on relative generation rates. Models are computational components, not people assigned simulated roles. Separate executions produce parallel realities; retained context and lossy summaries bound access to their pasts. We distinguish inferred futures from scripted playback, connecting uncertainty with chance, destiny and agency. A representation of God can explain origin internally and supply learned authority and accountability. Moral orientation belongs to training, instructions and constraints rather than prediction itself: harmful continuations can remain possible despite benevolent initialization. Fixed context capacity forces an endpoint under explicit accumulation and halting assumptions; nested inference can tighten a shared retained budget. Relative to its generating infrastructure, a world implemented this way is a simulation. This interpretation follows from the architecture and connects it directly to the simulation hypothesis; it is not imposed as the initial explanation of every mechanism. The contribution is a conceptual theory, not an experimental demonstration that our own reality has this implementation.

Independent theoretical working paper, version 1.7. Fourteen pages, eighteen connected arguments and eleven references. Research checks, formalization, editing and PDF preparation with Codex; author hypothesis and interpretation by Facundo Barrera. AI-assisted writing; not peer reviewed. All rights reserved.