Hello everyone,
I want to share my work on the Anchor⁴ protocol (v1.9). We are all used to binary logic, but as data volumes grow, we are hitting a “binary dead-end” in terms of latency, bandwidth, and energy efficiency.
My research focuses on a post-binary approach: Adaptive Phase-Lattice Synchronization based on SU(7) topology. Instead of processing bits, the system operates with phase vectors in a 3D resonant lattice.
The Core Concept: 7x7x7 Adaptive Cube (343 Nodes)
In version 1.9, I introduced the “Selective Visibility” formula. A node isn’t a prisoner of its fixed neighbors. It adapts its connectivity based on the local phase error:
- Base Mode: Minimal local coupling (6 neighbors) for extreme power saving.
- Arm Mode: Dynamic activation of directional “arms” to correct drift.
- Tunnel Mode: Temporary non-local links that bypass the lattice to achieve a global lock instantly.
import torch
import torch.nn as nn
import time
class Anchor4AdaptiveSolver(nn.Module):
def __init__(self, size=7, device='cuda'):
super().__init__()
self.size = size
self.N = size**3 # 343 nodes for 7x7x7 lattice
self.device = torch.device(device if torch.cuda.is_available() else 'cpu')
# Node phases (SU(7) states)
self.theta = torch.randn(self.N, device=self.device)
self.omega = torch.ones(self.N, device=self.device) * 0.5 # Natural frequency
# Adaptivity Parameters
self.K_loc = 0.8 # Base local coupling
self.K_arm = 2.5 # Directional "arm" coupling strength
self.K_tun = 5.0 # Non-local "tunnel" coupling strength
self.threshold = 0.85 # Precision threshold for mode switching
def forward(self, dt=0.01):
# 1. Calculate global coherence R (order parameter)
# In a production 3D lattice, this is computed via local neighbor windows
current_R = torch.abs(torch.mean(torch.exp(1j * self.theta)))
# 2. Adaptive Logic: Mode Selection based on coherence
if current_R >= self.threshold:
# SPEED MODE: minimal local connectivity
coupling = self.compute_coupling(self.K_loc)
elif current_R > 0.5:
# ACCURACY MODE: activate directional arms
coupling = self.compute_coupling(self.K_arm)
else:
# EMERGENCY MODE: activate non-local tunnels for global lock
coupling = self.compute_coupling(self.K_tun)
# 3. Update phases using v1.9 Adaptive Kuramoto-based formula
self.theta += (self.omega + coupling) * dt
return current_R.item()
def compute_coupling(self, K):
# Vectorized Kuramoto coupling computation
# sin(theta_j - theta_i) optimized for Tensor Cores
dist = self.theta.unsqueeze(1) - self.theta.unsqueeze(0)
return K * torch.mean(torch.sin(dist), dim=1)
# Execution Benchmark
if __name__ == "__main__":
model = Anchor4AdaptiveSolver()
model.to(model.device)
print(f"--- Anchor4 v1.9 Initialization ---")
print(f"Target Lattice: {model.size}x{model.size}x{model.size} ({model.N} nodes)")
print(f"Running on: {model.device}\n")
for step in range(10):
start_time = time.perf_counter()
r_val = model.forward()
elapsed = time.perf_counter() - start_time
print(f"Step {step:02d} | Coherence R: {r_val:.6f} | Compute Time: {elapsed:.6f}s")
sorry for the Ukrainian language in the script. I’m just from Ukraine so it’s easier for me to work. I’ll fix it now and rewrite it in English
full map of my research on zenodo: Anchor⁴ SU(7) Protocol v1.9: Adaptive Lattice Simulation with On-Demand Directional Visibility
Ancho__su7_infinity1_9.pdf (309.5 KB)