This circuit uses proven-optimal XOR decompositions instead of naive OR+NAND+AND:
h1: [-1, +1], bias=0 (fires when b > a)
h2: [+1, -1], bias=0 (fires when a > b)
out: [-1, -1], bias=1 (NOR of hidden)
h1: [0, 0, -1], bias=0 (selector: fires when c=0)
h2: [-1, +1, -1], bias=0 (full: fires when b > a+c)
h3: [-1, -1, +1], bias=0 (full: fires when c > a+b)
out: [+1, -1, -1], bias=0
feedback = C[15] XOR D
C'[0] = feedback (tap at x^0) ← XOR (mag 7)
C'[1] = C[0]
C'[2] = C[1]
C'[3] = C[2]
C'[4] = C[3]
C'[5] = C[4] XOR feedback (tap at x^5) ← XOR3 (mag 10)
C'[6] = C[5]
...
C'[11] = C[10]
C'[12] = C[11] XOR feedback (tap at x^12) ← XOR3 (mag 10)
C'[13] = C[12]
C'[14] = C[13]
C'[15] = C[14]
threshold-crc16-mag53/
├── model.safetensors
├── model.py
├── create_safetensors.py
├── config.json
└── README.md
1from safetensors.torch import load_file
2from model import forward
3
4weights = load_file('model.safetensors')
5
6# Single step: 16-bit CRC state + 1 data bit → new 16-bit state
7inputs = [c0, c1, ..., c15, d] # 17 binary values
8outputs = forward(inputs, weights) # 16 binary values
