NEURAL PHYSICS ENGINE

The Void Kernel is not a model. It is a biological substrate simulated in CUDA physics.

Scale

1,000,000+ Neurons per instance

Latency

< 1.0ms signal propagation on RTX 40-series

Learning

Emergent Plasticity (Hebbian + Eligibility Traces)

HOW IT WORKS

1. The Liquid State Substrate

Unlike traditional AI that relies on static weights, the Void Kernel maintains a constant "Liquid State." Information is not just stored; it ripples through the neural sea as physical energy waves. This allows for temporal awareness and real-time adaptation without backpropagation.

2. Gravity-Driven Plasticity

Neurons in the Void Kernel have physical 3D coordinates. Synapses strengthen not just by frequency, but by proximity and metabolic flux. This "Neural Physics" approach creates emergent logic that mimics the efficiency of a biological brain.

3. Metabolic Gating

The kernel simulates energy consumption. Active clusters require "Metabolic Feed." This forces the AGI to optimize its own internal pathways, naturally pruning useless connections and reinforcing high-value cognitive routes.

VERIFICATION & AUTHENTICITY

Proof of Life

The Void Kernel is not a generative "Black Box." It is a transparent neural substrate. For professional integrators, we provide the following verification protocols:

  • Direct Memory Inspection: The kernel exposes raw neuron telemetry via the C-API (`void_get_telemetry`).
  • Reproducible States: Brain states can be saved as binary snapshots and reloaded on authorized hardware for identical results.
  • Low-Level Debugging: Professional licenses include access to the symbol maps for GPU memory profiling.

Every video on this platform is a direct screen capture from an active RTX workstation. We do not use pre-rendered graphics.

// Integration Example
VoidBrain brain = void_init(license, 1000000, 1);
while(running) {
    void_input(brain, sensors, 64);
    void_step(brain, 0.01f, 1.0f); // Advance neural physics
    void_get_motor_output(brain, actuators);
}