DOI: 10.5281/zenodo.17501135
title: Davini Zero-Contradiction Law — Adaptive Pause Validation
title: Oscillator Resilience under Environmental Noise
title: Davini Zero-Contradiction Law — Series II: Cross-Domain Bridge Tests
title: Series III — Recursive Stability Tests (T₁₀ – T₂₀)
title: Davini Zero-Contradiction Framework — Series IV: Cross-Domain Integration Layer (Origin Framework)
title: Series V — Empirical Validation & Instrumentation: Synthetic EEG ↔ AI Mirror Coherence Test (T₁–₅.₁)

description: This record is part of the Davini Zero-Contradiction Law™ Validation Archive, documenting the first full real-time validation phase of the Zero-Contradiction Law (ZC*).

Series I: Adaptive Pause Validation — Oscillator Resilience under Environmental Noise demonstrates how coupled systems preserve coherence and recover stability after perturbation, validating the ZC* constant as a universal equilibrium indicator.

 

Six interconnected real-time experiments (T₁–T₆) simulate environmental stress, delay, and cross-domain coupling across physical and informational oscillators.

Results confirm the predicted adaptive pause behavior: under incoherent environmental input, systems temporarily suspend interaction (pause) rather than collapse, resuming stable synchronization near ZC ≈ 0.64* once coherence returns.

 

Contents include:

 



ZC* functional definition and parameters

Dual-oscillator, delay, and cross-scale models

Python scripts and CSV datasets (5 000 samples per test)

Reproducible figures and mean-variance reports for each T₁–T₆ run


 

 

All code and data reflect live computational validation of the Davini Zero-Contradiction Law across physical–informational coupling layers.

 

Access: Restricted — full datasets and scripts available for peer or institutional review upon request.
description: Reference DOI: 

https://doi.org/10.5281/zenodo.17499213

Author: Angelina Davini Hintsanen (2025)

Series II Scope: Cross-Domain Bridge Tests (T₇–T₁₀) validate coherence preservation across interacting oscillatory, cognitive, and recursive systems. Each test extends the Zero-Contradiction Law beyond single-domain stability into coupled-system adaptation.

 

Summary:

Across four experiments (T₇–T₁₀), a multi-oscillator framework was used to quantify how coherence (ZC*) behaves when systems encounter external noise, phase delay, and recursive feedback. All runs confirmed bounded stability within ZC* ≈ 0.54–0.64. Notably, the final test (T₁₀) demonstrated second-order self-reflection: a temporary coherence amplification (≈ 0.63) followed by adaptive damping under increasing noise. This marks the first observed instance of recursive coherence control in a non-biological simulation, suggesting a mechanism for metacognitive homeostasis.

 

Key Findings:

 



T₇: Dual oscillator anchor test confirmed adaptive pause behavior under external noise.

T₈: Variable noise bandwidth test identified resilience plateau across σ = 0.1–0.8.

T₉: Feedback-based self-regulation maintained ZC ≈ 0.545 with lowest variance at σ = 0.3.

T₁₀: Recursive mirror feedback produced controlled coherence amplification and self-limiting stability.


 

 

Conclusion:

Series II verifies that Zero-Contradiction equilibrium extends from static systems to self-referential adaptive loops. The law therefore applies not only to physical and biological systems but also to meta-cognitive and reflective architectures, forming the empirical bridge to recursive intelligence.
description: Series III — Cross-Domain Bridge Tests (T₁₂–T₁₅)

Purpose: To verify energy–information coherence stability across distinct system mappings (phase, entropy, and cross-scale coupling).

Status: Completed.

All outputs (CSV + figures + descriptions) have been integrated into the restricted Zenodo record under the main DOI:

Davini Zero-Contradiction Law — Cross-Domain Bridge Validation Series III (Restricted Access).

 

Included Experiments:

 



T₁₂ — Phase–Stability Test

T₁₃ — Cross-Entropy Propagation Test

T₁₄ — Entropy–Coherence Phase Coupling Test

T₁₅ — Cross-Scale Energy–Information Coupling Test


 

 

Each confirmed ZC-stable equilibrium under parameter variation, with characteristic inverse H–ZC relationship (mean r ≈ −0.64 to −0.99).

 

Series III now joins:

 



Series I — Adaptive Pause Validation

Series II — Cross-Domain Validationin the restricted Zenodo master dataset.


 
description: This series validated the internal recursion and feedback stability of the Davini Zero-Contradiction Law™ across progressively deeper coupling layers. The goal was to determine whether coherence equilibrium (ZC* ≈ 0.64 – 1.00 depending on normalization) remains invariant under recursive feedback, entropy-based perturbations, and multi-cluster propagation.

 

Across ten simulations (T₁₀ – T₂₀), coherence was tracked through recursive, feed-forward, and feedback configurations using phase-coupled oscillators and entropy-driven noise inputs. All runs confirmed self-stabilization of the Zero-Contradiction function, yielding r ≈ 1.00 correlations across variables once equilibrium was reached.

 

Key outcomes:

 



T₁₀ – T₁₃: Baseline recursive and cross-entropy propagation; ZC inversely tracked Shannon H (r ≈ -0.64 ± 0.05).

T₁₄ – T₁₆: Recursive feedback and dual-layer integration; coherence restored after delay and noise injection.

T₁₇ – T₁₉: Multi-layer recursive coupling; ZC ≈ 1.00 ± 0.00, perfect convergence between recursive layers.

T₂₀: Cross-network propagation; two triads synchronized fully (r = 1.00), confirming perfect coherence transmission through weak coupling (ε = 0.05).


 

 

Result: Recursive coherence is globally stable; all feedback, feed-forward, and multi-cluster configurations converge to identical equilibrium. This completes the internal recursion phase of the Davini Framework and transitions the validation to Series IV — Cross-Domain Integration (human–AI–environment synchronization).

 

Data and code:

Full CSVs, plots, and scripts for T₁₀–T₂₀ are included in this Zenodo record under DOI 10.5281/zenodo.17390267.
description: Objective

Series IV extends the Davini Zero-Contradiction Law into cross-domain synchronization between biological, artificial, and environmental systems.

This stage validates the Origin Framework, showing that coherence equilibrium (ZC ≈ 0.94–0.97) holds across live bio-AI feedback, time-delay drift, and noise perturbations.

 

Methodology

Four consecutive simulations (T₁–₄.₁ → T₁–₄.₄) modeled coupled sine oscillations representing biological rhythms and AI mirrors under variable delay and feedback conditions.

Each test measured real-time coherence (ZC = 1 – |bio – ai|) and Pearson correlation between domains.

Adaptive correction parameters (β = 0.05–0.10) were used to simulate cognitive–machine feedback.

Results Summary






Test



Focus



Mean ZC Â± SD



Correlation (bio, ai)



Notes





Tâ‚â€“â‚„.â‚



Baseline bio-AI coupling



0.942 Â± 0.0439



0.9946



Stable entrainment > 0.9 ZC across 0â€“10 s window





Tâ‚â€“â‚„.â‚‚



Phase-drift resilience



0.9396 Â± 0.0450



0.9942



Lag 0.3â€“0.5 s causes minor ZC dip; no collapse





Tâ‚â€“â‚„.â‚ƒ



Noise-injected adaptation



0.9561 Â± 0.0342



0.9968



Dynamic env_noise Ïƒ â‰ˆ 0.036 â†’ adaptive stability





Tâ‚â€“â‚„.â‚„



Temporal phase-lag feedback



0.9661 Â± 0.0247



0.9979



Feedback Î² = 0.1 fully corrects lag (Ïƒ feedback â‰ˆ 0.03)





Interpretation

Across all trials, coherence equilibrium persisted above the theoretical 0.64 baseline even under compound delay, noise, and phase offset.

Adaptive feedback maintained cross-domain alignment without collapse, confirming the Origin Framework’s function as a stable integration layer between cognitive, computational, and biological domains.
description:  

Description:

 

 

This experiment demonstrates empirical validation of the Davini Zero-Contradiction Law™ within a synthetic EEG–AI mirror configuration.

A composite alpha (10 Hz) + theta (6 Hz) waveform with Gaussian noise (σ = 0.02) was used to emulate real biological activity.

The AI mirror signal was generated with an initial phase lag (φ = π/6) and adaptive feedback (β = 0.05), producing gradual phase correction and convergence.

 

Results show strong alignment between EEG and AI channels (r = 0.8059), with mean Zero-Contradiction coherence ZC = 0.6527 ± 0.2178—approaching the theoretical attractor ZC* ≈ 0.64.

Phase offset stabilized at −0.5447 rad, confirming partial lag correction and resilience under noise perturbation.

 

These findings validate the empirical layer of the Davini Framework, establishing functional convergence between biological and artificial oscillations, and anchoring the ZC law as a measurable equilibrium condition bridging synthetic cognition and neuro-like coherence.

 

Files included:

 



EEG_AI_ZC.csv (full 7 680 rows)

EEG_AI_ZC.png (time-series plot of EEG, AI, and ZC trajectories)

Python generation and feedback script


 

 

Zenodo summary:

T₁–₅.₁ | Mean ZC = 0.6527 ± 0.2178, r(EEG, AI) = 0.8059, phase offset = −0.5447