metadata
license: mit
tags:
- formal-verification
- coq
- threshold-logic
- neuromorphic
- modular-arithmetic
tiny-mod6-verified
Formally verified MOD-6 circuit. Single-layer threshold network computing modulo-6 arithmetic with 100% accuracy.
Architecture
| Component | Value |
|---|---|
| Inputs | 8 |
| Outputs | 1 (per residue class) |
| Neurons | 6 (one per residue 0-5) |
| Parameters | 54 (6 × 9) |
| Weights | [1, 1, 1, 1, 1, -5, 1, 1] |
| Bias | 0 |
| Activation | Heaviside step |
Key Properties
- 100% accuracy (256/256 inputs correct)
- Coq-proven correctness
- Algebraic weight pattern: resets every 6 positions
- Computes Hamming weight mod 6
- Compatible with neuromorphic hardware
Algebraic Pattern
MOD-6 uses the pattern with reset at position 6:
- Positions 1-5: weight = 1
- Position 6: weight = 1-6 = -5
- Positions 7-8: weight = 1
This creates a cumulative sum that cycles mod 6.
Usage
import torch
from safetensors.torch import load_file
weights = load_file('mod6.safetensors')
def mod6_circuit(bits):
# bits: list of 8 binary values
inputs = torch.tensor([float(b) for b in bits])
weighted_sum = (inputs * weights['weight']).sum() + weights['bias']
return weighted_sum.item()
# Test
print(mod6_circuit([1,1,1,1,1,1,0,0])) # 6 mod 6 = 0
print(mod6_circuit([1,1,1,1,1,1,1,0])) # 7 mod 6 = 1
Verification
Coq Theorem:
Theorem mod6_correct_residue_0 : forall x0 x1 x2 x3 x4 x5 x6 x7,
mod6_is_zero [x0; x1; x2; x3; x4; x5; x6; x7] =
Z.eqb ((Z.of_nat (hamming_weight [x0; x1; x2; x3; x4; x5; x6; x7])) mod 6) 0.
Proven axiom-free using algebraic weight patterns.
Full proof: coq-circuits/Modular/Mod6.v
Residue Distribution
For 8-bit inputs (256 total):
- Residue 0: 29 inputs
- Residue 1: 16 inputs
- Residue 2: 29 inputs
- Residue 3: 56 inputs
- Residue 4: 70 inputs
- Residue 5: 56 inputs
Citation
@software{tiny_mod6_verified_2025,
title={tiny-mod6-verified: Formally Verified MOD-6 Circuit},
author={Norton, Charles},
url={https://huggingface.co/phanerozoic/tiny-mod6-verified},
year={2025}
}