import torch
import torch.nn as nn
from diffusers import DDPMScheduler, UNet2DModel

# Core meta-model architecture
class ActivationDiffusionModel(nn.Module):
    def __init__(self, activation_dim=5120):
        super().__init__()
        self.unet = UNet2DModel(
            sample_size=32,
            in_channels=1,
            out_channels=1,
            layers_per_block=2,
            block_out_channels=(128, 256, 512),
            down_block_types=("DownBlock2D", "AttnDownBlock2D", "DownBlock2D"),
            up_block_types=("UpBlock2D", "AttnUpBlock2D", "UpBlock2D")
        )
        self.noise_scheduler = DDPMScheduler(num_train_timesteps=1000)
        
    def forward(self, activations, timesteps):
        # activations: [batch_size, activation_dim]
        # Reshape for UNet: treat as 2D structure
        x = activations.view(-1, 1, 32, 160)
        return self.unet(x, timesteps).sample

# Training loop core
for batch in activation_dataloader:
    noise = torch.randn_like(batch)
    timesteps = torch.randint(0, 1000, (batch.shape[0],))
    noisy_activations = noise_scheduler.add_noise(batch, noise, timesteps)
    
    # Predict the noise component
    noise_pred = model(noisy_activations, timesteps)
    loss = F.mse_loss(noise_pred, noise)
    loss.backward()

Why Old AI Analysis Tools Are Failing You

PCA and sparse autoencoders force linear or simple structures onto neural networks. The problem? AI brains aren't linear. They're messy, high-dimensional spaces where traditional assumptions break.

When you use PCA to analyze LLM activations, you're assuming the data lies on straight lines. When you use sparse autoencoders, you're assuming features activate independently. Both assumptions are wrong for modern transformers.

How Diffusion Meta-Models Actually Work

The research trained diffusion models on one billion residual stream activations. That's the internal state flowing between transformer layers. The model learns the actual distribution of how AI thinks.

Here's the breakthrough: diffusion models don't assume structure. They discover it. The training process gradually adds noise to activations, then learns to reverse the process. What emerges is a perfect map of the AI's internal landscape.

The 47% Fidelity Improvement That Changes Everything

Diffusion loss decreased by 47% compared to traditional methods. This isn't just a better number—it's a fundamentally different approach to AI understanding.

Higher fidelity means:

• Better AI behavior editing
• More precise safety interventions
• Accurate feature visualization
• Reliable neural circuit analysis

When you intervene in an AI's activations using a diffusion meta-model as prior, your changes actually work. They don't get distorted by incorrect structural assumptions.

Real-World Impact: From Research to Production

This isn't academic. Meta-models enable:

AI Safety: Precisely modify harmful behaviors without breaking other capabilities. The diffusion prior ensures interventions stay within the AI's natural activation space.

Model Editing: Update facts or behaviors in trained models. Traditional methods often cause catastrophic forgetting or distorted outputs.

Interpretability: Actually understand what neurons are doing, not what we assume they're doing. This unlocks true AI transparency.