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.*

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