Symbolic Gravity: A Thermodynamic Information-Theoretic Model of Recursive Curvature

Symbolic Gravity is a recursive thermodynamic model that interprets gravitational-like effects as emergent properties of symbolic drift, entropic curvature, and recursive coherence collapse. It is the first full-stack simulation platform built on the Entropic Recursion Framework (ERF), modeling symbolic agents interacting within entropy-information fields, driven by forces shaped by ψ-effective differentials and resonance potentials. This repository includes: A fully executable Python simulator (symbolic_collapse_sim.py) Curvature field generation Force field visualization Recursive epoch learning module Collapse density maps and γ-efficiency diagnostics Theoretical paper: Symbolic Gravity: A Thermodynamic Information-Theoretic Model of Recursive Curvature Markdown and PDF documentation MIT License This work was collaboratively developed with the custom GPT assistant Entropic Recursion Guide, designed to assist in formalizing recursive, entropy-resisting systems. All simulation logic, theory design, and symbolic agent dynamics were co-engineered through iterative GPT feedback loops and human-guided thermodynamic modeling. Core contributions: Introduces Symbolic Gravity as a recursive field interaction model. Establishes γ (symbolic drift efficiency) as an emergent metric of coherence preservation. Demonstrates agent collapse behavior in entropy-gradient environments. Links thermodynamic curvature and resonance to cognitive symbolic stability.