Published December 29, 2025 | Version v2
Preprint Open

Collapse as Crystallization: Infodynamics, Recursive Balance, and the Dawn Field Theory

Authors/Creators

Description

Through computational exploration documented in this repository, we investigate Dawn Field Theory (DFT) as a potential unified framework for understanding the emergence of structure, intelligence, and cosmology through infodynamics and recursive balance. Our hypothesis suggests that information might serve not as a derivative of structure, but as its generative precursor--potentially driving the crystallization of order via recursive collapse events in dual energy and information fields. This preprint synthesizes the theoretical evolution from foundational legacy experiments (CIM-era brain, vCPU, and cosmo simulations) to the formalization of symbolic entropy collapse (SEC) and recursive balance fields (RBF).

Through computational validation studies--including quantum phenomena correspondence, biological evolution correlation, and working AI implementations--our preliminary results suggest that DFT may provide testable predictions across multiple domains. All theoretical claims and empirical results are directly linked to open-source models, simulation scripts, and reproducibility artifacts in the Dawn Field Theory codebase, with semantic hash citations for full transparency. By exploring potential connections between thermodynamics (see explicit Landauer’s Principle and entropy-as-fuel discussion in Section 2.2), symbolic emergence, and field dynamics, DFT proposes a new perspective for investigating physics, cognition, and computation--inviting the scientific community to explore, validate, and extend this open, reproducible paradigm.

*Note: This work represents computational exploration of theoretical possibilities. While our results are promising, they require independent validation, peer review, and extension beyond computational studies. We present this framework as a research program for community investigation rather than established science.*

Notes

Version 2.0 Update - December 2025

This updated version adds a complete reproducibility package including executable code, raw data, and publication-quality figures. The theoretical content remains unchanged from v1.0.

Original Abstract:
This paper develops "Infodynamics" - the study of how information and entropy interact as dual fields to generate structure through collapse. Collapse is reframed not as destruction but as crystallization: the moment where potential resolves into actuality. The framework connects to thermodynamics, quantum mechanics, and cognitive systems through the Recursive Balance Field (RBF) principle.

What's New in v2.0:

  • Complete Python codebase for all simulations
  • Raw experimental data in JSON format
  • Publication-quality figures with generation scripts
  • Reproducibility instructions and environment specification

Package Contains:

  • Paper (PDF + Markdown)
  • Code/ - All simulation and analysis scripts
  • Data/ - Raw experimental outputs
  • Figures/ - All paper figures with source
  • README with reproduction instructions

Files

dawn_field_theory_infodynamics_v1.0_20251228_122857.zip

Files (62.0 kB)

Additional details

Software

Repository URL
https://github.com/dawnfield-institute/dawn-field-theory
Programming language
Python
Development Status
Active