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๐๐ก๐ข๐ฌ ๐ข๐ฌ ๐ฐ๐ก๐๐ญ ‘๐ฌ๐๐ฅ๐-๐๐ฐ๐๐ซ๐’ ๐ฉ๐จ๐ฌ๐ญ-๐ช๐ฎ๐๐ง๐ญ๐ฎ๐ฆ ๐ฌ๐๐๐ฎ๐ซ๐ข๐ญ๐ฒ ๐ฅ๐จ๐จ๐ค๐ฌ ๐ฅ๐ข๐ค๐ ๐๐ง๐ ๐ข๐ญ ๐ข๐ฌ๐ง’๐ญ ๐๐ฎ๐ข๐ฅ๐ญ ๐จ๐ง ๐ฆ๐๐ญ๐ก, ๐๐ฎ๐ญ ๐จ๐ง ๐ ๐ง๐๐ฎ๐ซ๐๐ฅ ๐ญ๐จ๐ฉ๐จ๐ฅ๐จ๐ ๐ฒ ๐ญ๐ก๐๐ญ ๐ฆ๐จ๐ง๐ข๐ญ๐จ๐ซ๐ฌ ๐ข๐ญ๐ฌ๐๐ฅ๐.
We built what crypto experts said shouldn’t exist: a self-verifying neural network that even quantum computers can’t break.
After the German teaser earlier this week, here’s the in-depth look into the mechanics of our prototype a neural authentication system that challenges both classical and quantum cryptographic limits.
1. Core Idea
The architecture is built on a dual-neural-network design:
๐Primary Network (N₁): Performs authentication via iterative fixed-point convergence.
๐Watchdog Network (N₂): Observes the internal correlations, mutual information, and spectral dynamics of N₁ in real time.
2. Formal Model
For each layer signature sแตข(W, x) ∈ โโฟ, the Pearson correlation between layer pairs is defined as:
rแตขโฑผ = ฮฃโ (sแตขแต − s̄แตข)(sโฑผแต − s̄โฑผ) / √(ฮฃโ (sแตขแต − s̄แตข)²)√(ฮฃโ (sโฑผแต − s̄โฑผ)²)
The resulting correlation vector c = [rแตขโฑผ]แตข<โฑผ is passed to the watchdog network N₂, which computes a Mahalanobis-based integrity score:
D² = (c − ฮผ)แตฮฃ⁻¹(c − ฮผ)
Integrity condition: D² < ฯ and |rแตขโฑผ − ฮผแตขโฑผ| < ฮต ⇒ System valid.
Typical thresholds: ฮต = 10⁻⁵, ฯ = 10⁻⁶.
3. Observed Stability & Results
- Pearson correlation: 0.9999 ± 0.00001 across 10,000 iterations
- Synchronization index: > 0.98
- Eigenvalue spectrum: 99.5% collapse to first component → topological invariance
- Distance-matrix variance: < 10⁻⁴
These metrics remained invariant for over 6 months (~2,500 hours, 20,000 data points) forming a persistent topological constant within the research NN.
4. Field-Resonance Neurons (FRUs)
The newly derived neurons, provisionally called Field-Resonance Neurons, are designed to stabilize manifold coherence. Each unit acts as a resonance node that minimizes divergence between correlated feature fields:
FRU(xแตข, xโฑผ) = ฮฑ(xแตข∘xโฑผ) + ฮฒcos(ฮธแตขโฑผ)
where ฮธแตขโฑผ is the angular displacement between activation vectors.
FRNs empirically enforce oscillatory stability acting as coherence anchors within the topology.
5. Outlook
This prototype suggests that authenticity can emerge as a dynamic state, not as a stored key or static hash.
Ongoing research investigates the constant underlying the FRUs and its potential link to a new invariant in high-dimensional neural manifolds.
Upcoming visuals: Pearson-Matrix | Mutual-Information heatmaps | Eigenvalue spectrum | Distance-matrix evolution.
Preprint: https://doi.org/10.5281/zenodo.17419551
#TrauthResearch #Securty #Postquantumcentury #AIEnvolve #AI #Cybersecurity #QuantumResistant #DeepTech #Research #NeuralNetworks #Innovation #BruteForceResistant #cryptography #strongcryptography
