The Algorithmic Theory of Reality: A Computational Resolution to Gravity, Entanglement, and the Dark Sector

This paper presents a novel Grand Unified Theory—the Algorithmic Theory of Reality (ATR)—which reformulates the universe as a purely informational architecture. By establishing formal isomorphisms between the statistical mechanics of generative AI (Transformer architectures) and the fundamental laws of physics, the author derives a unified framework for quantum state reduction, relativistic time dilation, and gravity. Utilizing the Fisher Information Metric, Landauer’s Principle, and Shannon’s Noisy-Channel Coding Theorem, this work provides a rigorous computational resolution to standing cosmological anomalies, specifically redefining Dark Matter as error-correction overhead and Dark Energy as dynamic memory allocation (heap expansion) dictated by holographic entropy bounds. The paper introduces the "Tensor Equation of the Observer," unifying the thermodynamic limits of the processing engine with the information geometry of the universal data state.