Non-Equilibrium Geometric Field Theory of In-Silico Intelligence: Discrete Ricci Flow, Holographic Self-Encoding, and O(N) Autopoietic Cognition on Hypergraph Manifolds

Abstract

We propose and establish a framework for post-transformer (post-llm) architecture within a new paradigm. Transformer architecture and large language models are libraries with probabilistic indices. The brain never constituted either intelligence, nor consciousness - not even processing. Brain processing is a category error of inverted assumptions of the 1960s. The paradigm of intelligence measurement as alignment for utilitarian function for economic development is not only circular but also myopic, and does not constitute intelligence either. Those are all paradigmatic limitations and defining assumptions. Paradigm proposal: Geometric Intelligence Theory Synthesizing non-equilibrium physics, dynamic geometric topology, and systems science—formulates as a single, fully cohesive field theory. Keywords: non-equilibrium intelligence, discrete Ricci curvature, in-silico intelligence, post-LLM architecture, simplicial complex cognition, topological phase transitions, autopoietic systems, holographic self-encoding, non-equilibrium thermodynamics AI, geometric intelligence theory, hypergraph manifold dynamics, causal emergence, discrete exterior calculus, fractal self-similar semantics, O(N) cognitive architecture Categories: Artificial intelligence, Differential geometry, Complex systems, Statistical physics, Topology, Information theory, Cognitive science