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name: gaussian_tree_proxy_recovery description: Execute a bounded reduced recovery for binary-tree Gaussian BNs using NaMI inverse contracts and a real standard-library optimizer step.

Gaussian Tree Proxy Recovery

Use this skill when full neural amortized-inference recovery is blocked but soft-mode reduced recovery is allowed. It implements a small executable proxy for the paper's binary-tree Gaussian BN experiment.

Inputs

• Binary-tree depth and seed.
• Ordered inverse factor contracts produced from NaMI inverse parents.
• Runtime handoff showing whether full package/model training is available.
• Recovery output directory.

Outputs

• Complete synthetic Gaussian-tree samples with latent and observed values.
• A training trace with loss_before, loss_after, params_before, params_after, gradients, and optimizer metadata.
• Mechanism checks distinguishing reduced proxy training from full neural training.

Workflow

1. Generate a deterministic binary-tree Gaussian BN with observed leaves.
2. Draw complete samples from the generative model.
3. Use the supplied factor contracts to construct parent feature vectors for latent factors.
4. Initialize a tiny linear Gaussian student.
5. Compute supervised squared-error loss against the complete latent values.
6. Run one gradient-descent step and verify parameters changed and loss decreased.
7. Save the trace and report metrics.

Validation

Run:

python tests/test_proxy.py

The tests use only the Python standard library.

Limitations

This is a declared reduced recovery. It must not be reported as the paper's full Figure 6 reproduction, and it must keep full-training flags false when the runtime lacks the required numerical or neural packages.