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name: aevb_vae_core description: Run a compact AEVB/VAE training step with encoder statistics, reparameterization, decoder reconstruction, analytic KL, and optimizer evidence.

AEVB VAE Core

Use this skill when a recovery experiment must exercise the core Auto-Encoding Variational Bayes mechanism on image-like data. It is appropriate for reduced/proxy recovery when full dataset training is blocked, provided the proxy is declared.

Inputs

• Proxy image batch JSON with values in [0, 1].
• latent_dim, learning_rate, and seed.
• Optional output path for a training trace.

Outputs

• Training trace JSON with reconstruction loss, KL, total loss before and after one optimizer step; positive loss_delta is desirable recovery evidence but callers should inspect the numeric trace because tiny stochastic steps can vary by seed and learning rate.
• params_before and params_after snapshots.
• Mechanism checks: encoder, reparameterization, decoder, reconstruction loss, KL, optimizer step.

Workflow

1. Load image observations and flatten them.
2. Encode to mu and logvar.
3. Compute z = mu + exp(0.5 * logvar) * epsilon.
4. Decode z to Bernoulli reconstruction logits.
5. Compute BCE plus analytic Gaussian KL.
6. Update trainable parameters and record before/after signals.

Validation

Run:

python scripts/run_vae_step.py --batch-json /tmp/vae_proxy_batch.json --output /tmp/vae_trace.json
python tests/test_vae_step.py

Limitations

A tiny proxy trace is not a full paper reproduction. Keep full_training_executed false unless a real full dataset/model run was executed.