This circuit is functionally identical to
threshold-minority
:
Circuit
Condition
Implementation
Minority
HW ≤ 3
w: all -1, b: +3
AtMost3
HW ≤ 3
w: all -1, b: +3
Both detect "strictly fewer than half." The naming differs by framing:
"Minority" emphasizes the voting interpretation
"AtMost3" emphasizes the counting bound
The Tie Boundary
HW
AtMost3
Interpretation
0-3
1
Minority (< 50%)
4
0
Tie (= 50%)
5-8
0
Majority (> 50%)
AtMost3 excludes ties. Four active inputs is not "at most 3."
Dual of AtLeast5 (Majority)
Circuit
Condition
Meaning
AtLeast5
HW ≥ 5
Majority
AtMost3
HW ≤ 3
Minority
These are not complements - the tie zone (HW = 4) belongs to neither.
Coverage
HW
C(8,k)
AtMost3?
0
1
Yes
1
8
Yes
2
28
Yes
3
56
Yes
4-8
163
No
Fires on 1 + 8 + 28 + 56 =
93
of 256 inputs (36.3%).
Parameters
Component
Value
Weights
all -1
Bias
+3
Total
9 parameters
Usage
from safetensors.torch import load_file
import torch
w = load_file('model.safetensors')
defatmost3(bits):
inp = torch.tensor([float(b) for b in bits])
returnint((inp * w['weight']).sum() + w['bias'] >= 0)
# Three active: minorityprint(atmost3([1,1,1,0,0,0,0,0])) # 1# Four active: tie, not minorityprint(atmost3([1,1,1,1,0,0,0,0])) # 0
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