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justinlietz93/README.md

Void Dynamics Model (VDM)

"From the impossibility of nothing emerges everything, by mathematical necessity of hierarchical dissipation through logarithmically scaled boundaries to maintain finite energy while processing infinite possibility; concentrated through phase transitions in the complex perimeter evolution of the boundary." - The Void Dynamics Thesis

Real-time, event-driven, Klein-Gordon / Reaction–Diffusion metriplectic dynamics with complex self driven learning and stable real time emergence.

No dense scans. Zero training. Divergent reasoning. Constraint satisfaction in the moment.

DOI ORCID AMD/ROCm only Dual Academic/Commercial License


image

🔭 What I’m building

  • VDM — reaction–diffusion field intelligence + walker ecology + scoreboard/GDSP gating
  • Memory Steering — dynamic knowledge graph with event-driven updates
  • Agency — inherent, minimal energy optimization rules
  • Real-time control — swap massive pretraining for fast constraint satisfaction

Reproducibility: baselines + QA artifacts are archived on Zenodo. Code lives in public GitHub repos with tests and docs.


🧪 Reproducible records (Zenodo)


🗺️ Roadmap (Oct~Dec 2025)

  • Notebook mirrors of each test ✅
  • 100% Reproducible, falsifiable claims ✅
  • Currently working real time model ✅
  • Zero training ✅
  • Online causality based learning
  • Physics driven intelligence model ✅

CURRENT Execution model: All tests live in src/{domain name}/, figures appear in figures/{domain name} if passing and figures/{domain name}/failed_runs/ if failed. The same goes for logs, just replace figures/ with logs/ in the file path to find them.


News

  • October 31, 2025
    • Added a T8 grade proposal for a new Axiom candidate as A8 - Lietz Infinity Conjecture which provides an elegant assumption for how our universe evolved to be how it is from a beginning state to now. It includes the derived rules for how our universe would be structured at hierarchical scales to dissipate away the instabilities of void fluctuations.
  • October 30, 2025
    • Added a historical/ folder including early original work like:
      • A self healing knowledge graph using Topological Data Analysis
      • As well as a Self Improvement Engine that integrates multiple reward components like novelty, self benefit, habituation, and TD error into a single "total reward" signal used to modulate its own neural plasticity for stable self-improvement that avoids weight saturation.
    • Validated the Counterfactual Echo Gain hypothesis by proving the trustworthiness and accuracy of the instrument, and showing that echo assist does modulate and improve the performance of a self aware system with 0 difference in cost compared to baseline.
  • October 28, 2025
  • October 23, 2025:
    • Created a sparsely populated CANON_PROGRESS.md document to post updates on private work to prevent this public repo from going stale.
  • September 29, 2025:
    • First public code release + creation of private Void Dynamics package which can now be imported and run in this repository using workflows and repository secrets.
  • September 28, 2025:
    • Posted two pre-prints to Zenodo. If you've published similar or relevant work on Reaction-Diffusion in the past 3 years on arXiv and want to support this work by endorsing me in a related category, submit an issue, post in the discussion board, or send me an email with Subject: RD Endorsement to get my attention. It would be much appreciated!
  • August 21, 2025: Launched public repo
  • ...
  • March, 2025
    • Released first falsifiable, reproducible simulations that validated initial claims.
  • February, 2025
    • SIE and Emergent KG + Real-Time TDA show very strong statistically significant findings, making feasible the entire model
  • October, 2024
    • Initial realization of the idea.

Provenance & checks (written by 00_RUN_ALL.ipynb)

  • Manifest: artifacts/meta/manifest.json (paths to all outputs).
  • Contradiction report on failure: artifacts/meta/CONTRADICTION_REPORT.json (which gate failed + notebook cell refs).

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