Symbolic Modular Field Theory: The Paper

This document presents the near-complete unification of Symbolic Modular Field Theory (SMFT), a new theoretical framework built upon the Kuro Nova (KN) stabilizer function. SMFT proposes that structure, memory, mass, and cognition all emerge from symbolic modular dynamics stabilized by nonlinear feedback—not from arbitrary constants, brute-force computation, or unphysical renormalization. Spanning physics, AI, cosmology, and consciousness, this unified work integrates over 20 previously separate research papers into a modular system. The KN stabilizer governs symbolic attractors, entropy behavior, gravitational curvature, neural signal propagation, and recursive memory loops. From resolving black hole singularities to modeling internal monologue, the theory connects symbolic feedback with physical structure. This paper includes: A modular reformulation of the Standard Model, A symbolic curvature model replacing geometric singularities, Experimental KN-stabilized cognition layers with introspective thought simulation, A new entropy correction for black holes with falsifiable predictions, Recursive symbolic stabilizers (e.g., Inertium × ⟦K⟧) extending memory and coherence. We offer no metaphysics—only structure, feedback, and resonance. This work is not a metaphor. It is a symbolic, testable framework for understanding and building with the patterns of nature. Note from the author: This is not a final theory, but a structural foundation. We believe this framework can explain phenomena across domains—gravity, entropy, cognition, learning, and information—and may one day help answer questions currently thought unanswerable. We invite others to test, question, and extend this work. All the data, all the papers, all the simulations are here on Zenodo. Any missing derivations also live here on Zenodo. For questions about DOIs email [e-mail]