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README.md

@@ -1,105 +1,105 @@
----
-license: mit
-tags:
-- formal-verification
-- coq
-- threshold-logic
-- neuromorphic
-- multi-layer
----
-
-# tiny-XNOR-verified
-
-Formally verified XNOR gate. Two-layer threshold network computing equivalence with 100% accuracy.
-
-## Architecture
-
-| Component | Value |
-|-----------|-------|
-| Inputs | 2 |
-| Outputs | 1 |
-| Neurons | 3 (2 hidden, 1 output) |
-| Layers | 2 |
-| Parameters | 9 |
-| **Layer 1, Neuron 1 (NOR)** | |
-| Weights | [-1, -1] |
-| Bias | 0 |
-| **Layer 1, Neuron 2 (AND)** | |
-| Weights | [1, 1] |
-| Bias | -2 |
-| **Layer 2 (OR)** | |
-| Weights | [1, 1] |
-| Bias | -1 |
-| Activation | Heaviside step (all layers) |
-
-## Key Properties
-
-- 100% accuracy (4/4 inputs correct)
-- Coq-proven correctness
-- Minimal 2-layer architecture (XNOR is not linearly separable)
-- Integer weights
-- Commutative: XNOR(x,y) = XNOR(y,x)
-- Reflexive: XNOR(x,x) = true
-- Equivalence relation (reflexive, symmetric)
-
-## Usage
-
-```python
-import torch
-from safetensors.torch import load_file
-
-weights = load_file('xnor.safetensors')
-
-def xnor_gate(x, y):
-    inputs = torch.tensor([float(x), float(y)])
-
-    # Layer 1: NOR and AND
-    nor_sum = (inputs * weights['layer1.neuron1.weight']).sum() + weights['layer1.neuron1.bias']
-    nor_out = int(nor_sum >= 0)
-
-    and_sum = (inputs * weights['layer1.neuron2.weight']).sum() + weights['layer1.neuron2.bias']
-    and_out = int(and_sum >= 0)
-
-    # Layer 2: OR
-    layer1_outs = torch.tensor([float(nor_out), float(and_out)])
-    or_sum = (layer1_outs * weights['layer2.weight']).sum() + weights['layer2.bias']
-    return int(or_sum >= 0)
-
-# Test
-print(xnor_gate(0, 0))  # 1
-print(xnor_gate(0, 1))  # 0
-print(xnor_gate(1, 0))  # 0
-print(xnor_gate(1, 1))  # 1
-```
-
-## Verification
-
-**Coq Theorem**:
-```coq
-Theorem xnor_correct : forall x y, xnor_circuit x y = negb (xorb x y).
-```
-
-Proven axiom-free with properties:
-- Commutativity
-- Reflexivity
-- Symmetry
-- Equivalence relation properties
-
-Full proof: [coq-circuits/Boolean/XNOR.v](https://github.com/CharlesCNorton/coq-circuits)
-
-## Circuit Operation
-
-XNOR outputs true when inputs are equal (both false or both true).
-
-XNOR(x,y) = OR(NOR(x,y), AND(x,y))
-
-## Citation
-
-```bibtex
-@software{tiny_xnor_prover_2025,
-  title={tiny-XNOR-verified: Formally Verified XNOR Gate},
-  author={Norton, Charles},
-  url={https://huggingface.co/phanerozoic/tiny-XNOR-verified},
-  year={2025}
-}
-```
+---
+license: mit
+tags:
+- formal-verification
+- coq
+- threshold-logic
+- neuromorphic
+- multi-layer
+---
+
+# tiny-XNOR-verified
+
+Formally verified XNOR gate. Two-layer threshold network computing equivalence with 100% accuracy.
+
+## Architecture
+
+| Component | Value |
+|-----------|-------|
+| Inputs | 2 |
+| Outputs | 1 |
+| Neurons | 3 (2 hidden, 1 output) |
+| Layers | 2 |
+| Parameters | 9 |
+| **Layer 1, Neuron 1 (NOR)** | |
+| Weights | [-1, -1] |
+| Bias | 0 |
+| **Layer 1, Neuron 2 (AND)** | |
+| Weights | [1, 1] |
+| Bias | -2 |
+| **Layer 2 (OR)** | |
+| Weights | [1, 1] |
+| Bias | -1 |
+| Activation | Heaviside step (all layers) |
+
+## Key Properties
+
+- 100% accuracy (4/4 inputs correct)
+- Coq-proven correctness
+- Minimal 2-layer architecture (XNOR is not linearly separable)
+- Integer weights
+- Commutative: XNOR(x,y) = XNOR(y,x)
+- Reflexive: XNOR(x,x) = true
+- Equivalence relation (reflexive, symmetric)
+
+## Usage
+
+```python
+import torch
+from safetensors.torch import load_file
+
+weights = load_file('xnor.safetensors')
+
+def xnor_gate(x, y):
+    inputs = torch.tensor([float(x), float(y)])
+
+    # Layer 1: NOR and AND
+    nor_sum = (inputs * weights['layer1.neuron1.weight']).sum() + weights['layer1.neuron1.bias']
+    nor_out = int(nor_sum >= 0)
+
+    and_sum = (inputs * weights['layer1.neuron2.weight']).sum() + weights['layer1.neuron2.bias']
+    and_out = int(and_sum >= 0)
+
+    # Layer 2: OR
+    layer1_outs = torch.tensor([float(nor_out), float(and_out)])
+    or_sum = (layer1_outs * weights['layer2.weight']).sum() + weights['layer2.bias']
+    return int(or_sum >= 0)
+
+# Test
+print(xnor_gate(0, 0))  # 1
+print(xnor_gate(0, 1))  # 0
+print(xnor_gate(1, 0))  # 0
+print(xnor_gate(1, 1))  # 1
+```
+
+## Verification
+
+**Coq Theorem**:
+```coq
+Theorem xnor_correct : forall x y, xnor_circuit x y = negb (xorb x y).
+```
+
+Proven axiom-free with properties:
+- Commutativity
+- Reflexivity
+- Symmetry
+- Equivalence relation properties
+
+Full proof: [coq-circuits/Boolean/XNOR.v](https://github.com/CharlesCNorton/coq-circuits/blob/main/coq/Boolean/XNOR.v)
+
+## Circuit Operation
+
+XNOR outputs true when inputs are equal (both false or both true).
+
+XNOR(x,y) = OR(NOR(x,y), AND(x,y))
+
+## Citation
+
+```bibtex
+@software{tiny_xnor_prover_2025,
+  title={tiny-XNOR-verified: Formally Verified XNOR Gate},
+  author={Norton, Charles},
+  url={https://huggingface.co/phanerozoic/tiny-XNOR-verified},
+  year={2025}
+}
+```