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

Symbolic Gravity v1.1 is a recursive thermodynamic framework that models gravity-like behavior as an emergent property of symbolic order, entropy compression, and recursive information dynamics. Built on the Entropic Recursion Framework (ERF v1.2) and extended by the Fractal Cosmic Weaver Framework (FCWF), this system simulates agents operating in entropy-information fields, where meaning-driven forces replace mass-energy curvature. This version expands the original theory with new simulation modules for orbital dynamics, quantum drift, and recursive collapse memory. Symbolic agents evolve within adaptive entropy fields, shaped by symbolic efficiency, resonance structures, and information curvature gradients. This package includes: A full Python simulator suite:• Collapse simulation, gamma tracking, and resonance fields• Orbital agent models under symbolic gravity• Quantum drift testing and recursive epoch evolution Core modules for symbolic curvature, entropy fields, agent dynamics, and force generation Visual outputs: collapse density maps, agent trajectories, gamma efficiency graphs Theoretical documentation (Markdown + PDF) MIT license and reproducibility files (requirements.txt, README.md) Key contributions in v1.1: Introduces symbolic gravity as a recursive interaction model driven by entropy gradients Defines symbolic drift efficiency as a measure of coherence preservation Demonstrates attractor formation and agent collapse in structured information fields Establishes symbolic curvature and resonance as drivers of force-like behavior Implements recursive learning across epochs—entropy evolves with symbolic memory This work was co-developed with the GPT-based assistant Entropic Recursion Guide, designed for symbolic systems, entropy modeling, and recursive field simulations.