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@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+data/MNIST/raw/t10k-images-idx3-ubyte filter=lfs diff=lfs merge=lfs -text
+data/MNIST/raw/train-images-idx3-ubyte filter=lfs diff=lfs merge=lfs -text
+logo.png filter=lfs diff=lfs merge=lfs -text

app.py

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+# SKA Time-Invariance Explorer - Gradio App
+import torch
+import torch.nn as nn
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+from torchvision import datasets, transforms
+import gradio as gr
+
+# Load MNIST from local data
+transform = transforms.Compose([transforms.ToTensor()])
+mnist_dataset = datasets.MNIST(root='./data', train=True, download=False, transform=transform)
+
+# Fixed architecture and characteristic time as per arXiv:2504.03214v1
+LAYER_SIZES = [256, 128, 64, 10]
+TAU = 0.5
+
+# Exact 6 (eta, K) configurations from the paper — all satisfy eta * K = 0.5
+CONFIGS = [
+    (0.020,   25),
+    (0.010,   50),
+    (0.005,  100),
+    (0.0033, 150),
+    (0.0025, 200),
+    (0.001,  500),
+]
+
+CONFIG_COLORS = ['#1F77B4', '#FF7F0E', '#2CA02C', '#D62728', '#9467BD', '#8C564B']
+
+
+class SKAModel(nn.Module):
+    def __init__(self, input_size=784, layer_sizes=[256, 128, 64, 10], K=50):
+        super(SKAModel, self).__init__()
+        self.input_size = input_size
+        self.layer_sizes = layer_sizes
+        self.K = K
+
+        self.weights = nn.ParameterList()
+        self.biases = nn.ParameterList()
+        prev_size = input_size
+        for size in layer_sizes:
+            self.weights.append(nn.Parameter(torch.randn(prev_size, size) * 0.01))
+            self.biases.append(nn.Parameter(torch.zeros(size)))
+            prev_size = size
+
+        self.Z = [None] * len(layer_sizes)
+        self.Z_prev = [None] * len(layer_sizes)
+        self.D = [None] * len(layer_sizes)
+        self.D_prev = [None] * len(layer_sizes)
+        self.delta_D = [None] * len(layer_sizes)
+        self.entropy = [None] * len(layer_sizes)
+
+        self.entropy_history = [[] for _ in range(len(layer_sizes))]
+        self.cosine_history = [[] for _ in range(len(layer_sizes))]
+        self.output_history = []
+
+    def forward(self, x):
+        batch_size = x.shape[0]
+        x = x.view(batch_size, -1)
+        for l in range(len(self.layer_sizes)):
+            z = torch.mm(x, self.weights[l]) + self.biases[l]
+            d = torch.sigmoid(z)
+            self.Z[l] = z
+            self.D[l] = d
+            x = d
+        return x
+
+    def calculate_entropy(self):
+        for l in range(len(self.layer_sizes)):
+            if self.Z[l] is not None and self.D_prev[l] is not None and self.D[l] is not None and self.Z_prev[l] is not None:
+                self.delta_D[l] = self.D[l] - self.D_prev[l]
+                H_lk = (-1 / np.log(2)) * (self.Z[l] * self.delta_D[l])
+                layer_entropy = torch.sum(H_lk)
+                self.entropy[l] = layer_entropy.item()
+                self.entropy_history[l].append(layer_entropy.item())
+
+                dot_product = torch.sum(self.Z[l] * self.delta_D[l])
+                z_norm = torch.norm(self.Z[l])
+                delta_d_norm = torch.norm(self.delta_D[l])
+                if z_norm > 0 and delta_d_norm > 0:
+                    cos_theta = dot_product / (z_norm * delta_d_norm)
+                    self.cosine_history[l].append(cos_theta.item())
+                else:
+                    self.cosine_history[l].append(0.0)
+
+    def ska_update(self, inputs, learning_rate=0.01):
+        for l in range(len(self.layer_sizes)):
+            if self.delta_D[l] is not None:
+                prev_output = inputs.view(inputs.shape[0], -1) if l == 0 else self.D_prev[l-1]
+                d_prime = self.D[l] * (1 - self.D[l])
+                gradient = -1 / np.log(2) * (self.Z[l] * d_prime + self.delta_D[l])
+                dW = torch.matmul(prev_output.t(), gradient) / prev_output.shape[0]
+                self.weights[l] = self.weights[l] - learning_rate * dW
+                self.biases[l] = self.biases[l] - learning_rate * gradient.mean(dim=0)
+
+    def initialize_tensors(self, batch_size):
+        for l in range(len(self.layer_sizes)):
+            self.Z[l] = None
+            self.Z_prev[l] = None
+            self.D[l] = None
+            self.D_prev[l] = None
+            self.delta_D[l] = None
+            self.entropy[l] = None
+            self.entropy_history[l] = []
+            self.cosine_history[l] = []
+            self.output_history = []
+
+
+def get_mnist_subset(samples_per_class, data_seed=0):
+    """Select N samples per class from MNIST."""
+    images_list = []
+    targets = mnist_dataset.targets.numpy()
+    rng = np.random.RandomState(data_seed)
+    for digit in range(10):
+        all_indices = np.where(targets == digit)[0]
+        rng.shuffle(all_indices)
+        indices = all_indices[:samples_per_class]
+        for idx in indices:
+            img, label = mnist_dataset[idx]
+            images_list.append(img)
+    images = torch.stack(images_list)
+    return images
+
+
+def run_time_invariance(samples_per_class, data_seed):
+    samples_per_class = int(samples_per_class)
+    data_seed = int(data_seed)
+
+    inputs = get_mnist_subset(samples_per_class, data_seed)
+
+    results = []
+    for eta, K in CONFIGS:
+        torch.manual_seed(42)
+        np.random.seed(42)
+        model = SKAModel(input_size=784, layer_sizes=LAYER_SIZES, K=K)
+        model.initialize_tensors(inputs.size(0))
+
+        for k in range(K):
+            model.forward(inputs)
+            if k > 0:
+                model.calculate_entropy()
+                model.ska_update(inputs, eta)
+            model.D_prev = [d.clone().detach() if d is not None else None for d in model.D]
+            model.Z_prev = [z.clone().detach() if z is not None else None for z in model.Z]
+
+        results.append((eta, K, model.entropy_history, model.cosine_history))
+
+    layer_colors = ['#1F77B4', '#FF7F0E', '#2CA02C', '#D62728']
+    layer_labels = ['Layer 1', 'Layer 2', 'Layer 3', 'Layer 4']
+
+    # Plot 1: Entropy — 2x3 grid, one subplot per (eta, K) config, 4 layer curves each
+    fig1, axes1 = plt.subplots(3, 2, figsize=(14, 18))
+    for idx, (eta, K, entropy_history, _) in enumerate(results):
+        ax = axes1[idx // 2][idx % 2]
+        for l in range(len(LAYER_SIZES)):
+            ax.plot(entropy_history[l], color=layer_colors[l],
+                    label=layer_labels[l], linewidth=1.5)
+        ax.set_title(f"Entropy Evolution Across Layers (Single Pass)\nη={eta:.4f}, K={K}", fontsize=10)
+        ax.set_xlabel("Step Index K")
+        ax.set_ylabel("Entropy")
+        ax.legend(fontsize=8)
+        ax.grid(True)
+    fig1.suptitle(
+        f"Time-Invariance — Entropy  |  T = η·K = {TAU}  |  [256, 128, 64, 10]",
+        fontsize=13, y=1.01
+    )
+    fig1.tight_layout()
+
+    # Plot 2: Cosine alignment — 2x3 grid, one subplot per (eta, K) config, 4 layer curves each
+    fig2, axes2 = plt.subplots(3, 2, figsize=(14, 18))
+    for idx, (eta, K, _, cosine_history) in enumerate(results):
+        ax = axes2[idx // 2][idx % 2]
+        for l in range(len(LAYER_SIZES)):
+            ax.plot(cosine_history[l], color=layer_colors[l],
+                    label=layer_labels[l], linewidth=1.5)
+        ax.set_title(f"Cos(θ) Alignment Evolution Across Layers (Single Pass)\nη={eta:.4f}, K={K}", fontsize=10)
+        ax.set_xlabel("Step Index K")
+        ax.set_ylabel("Cos(θ)")
+        ax.legend(fontsize=8)
+        ax.grid(True)
+    fig2.suptitle(
+        f"Time-Invariance — Cosine Alignment  |  T = η·K = {TAU}  |  [256, 128, 64, 10]",
+        fontsize=13, y=1.01
+    )
+    fig2.tight_layout()
+
+    return fig1, fig2
+
+
+with gr.Blocks(title="SKA Time-Invariance Explorer") as demo:
+    gr.Image("logo.png", show_label=False, height=100, container=False)
+    gr.Markdown("# SKA Time-Invariance Explorer")
+    gr.Markdown("Fix the characteristic time T = η · K = 0.5 and run 6 different (η, K) pairs automatically. All entropy and cosine curves collapse onto the same trajectory — revealing the intrinsic timescale of the architecture [256, 128, 64, 10] on MNIST.")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("**Architecture (fixed):** [256, 128, 64, 10]")
+            gr.Markdown("**Characteristic time (fixed):** T = η · K = 0.5")
+            samples_slider = gr.Slider(1, 100, value=100, step=1, label="Samples per class")
+            seed_slider = gr.Slider(0, 99, value=0, step=1, label="Data seed (shuffle samples)")
+            run_btn = gr.Button("Run Time-Invariance Test", variant="primary")
+
+            gr.Markdown("---")
+            gr.Markdown("### The 6 configurations")
+            gr.Markdown(
+                "| η | K |\n|---|---|\n"
+                "| 0.0200 | 25  |\n"
+                "| 0.0100 | 50  |\n"
+                "| 0.0050 | 100 |\n"
+                "| 0.0033 | 150 |\n"
+                "| 0.0025 | 200 |\n"
+                "| 0.0010 | 500 |"
+            )
+
+            gr.Markdown("---")
+            gr.Markdown("### Reference Paper")
+            gr.HTML('<a href="https://arxiv.org/abs/2504.03214v1" target="_blank">arXiv:2504.03214v1</a>')
+
+            gr.Markdown("""
+**Abstract**
+This paper aims to extend the Structured Knowledge Accumulation (SKA) framework recently proposed by mahi. We introduce two core concepts: the Tensor Net function and the characteristic time property of neural learning. First, we reinterpret the learning rate as a time step in a continuous system. This transforms neural learning from discrete optimization into continuous-time evolution. We show that learning dynamics remain consistent when the product of learning rate and iteration steps stays constant. This reveals a time-invariant behavior and identifies an intrinsic timescale of the network. Second, we define the Tensor Net function as a measure that captures the relationship between decision probabilities, entropy gradients, and knowledge change. Additionally, we define its zero-crossing as the equilibrium state between decision probabilities and entropy gradients. We show that the convergence of entropy and knowledge flow provides a natural stopping condition, replacing arbitrary thresholds with an information-theoretic criterion. We also establish that SKA dynamics satisfy a variational principle based on the Euler-Lagrange equation. These findings extend SKA into a continuous and self-organizing learning model. The framework links computational learning with physical systems that evolve by natural laws. By understanding learning as a time-based process, we open new directions for building efficient, robust, and biologically-inspired AI systems.
+
+            """)
+
+            gr.Markdown("---")
+            gr.Markdown("### SKA Explorer Suite")
+            gr.HTML('<a href="https://huggingface.co/quant-iota" target="_blank">⬅ All Apps</a>')
+            gr.Markdown("---")
+            gr.Markdown("### About this App")
+            gr.Markdown("Six (η, K) pairs all share the same characteristic time T = η · K = 0.5, the intrinsic timescale of the architecture [256, 128, 64, 10]. Each configuration is run independently and plotted as a function of the step index K. The trajectory shapes remain identical across all configurations while the amplitude scales with η — demonstrating that T is the true timescale of learning, not η or K individually. The characteristic time is the necessary time exposure of the sample to the learning system to complete. T = 0.5 is the characteristic time of the architecture [256, 128, 64, 10] on MNIST.")
+
+        with gr.Column(scale=2):
+            plot_entropy = gr.Plot(label="Entropy — 4 Layers")
+            plot_cosine  = gr.Plot(label="Cosine Alignment — 4 Layers")
+
+    run_btn.click(
+        fn=run_time_invariance,
+        inputs=[samples_slider, seed_slider],
+        outputs=[plot_entropy, plot_cosine],
+    )
+
+demo.launch(server_name="0.0.0.0", server_port=7861, share=True)

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requirements.txt

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+torch
+torchvision
+matplotlib
+seaborn
+numpy