---

name: vae_recovery_evaluation description: Convert VAE/AEVB proxy training traces into auditable recovery_result.json artifacts with target matching and mechanism checks.

VAE Recovery Evaluation

Use this skill after a VAE training trace has been produced. It checks that the recovery target matches the module plan and that the trace proves the core paper mechanism ran.

Inputs

• module_plan.json with fast_recovery_target.
• Training trace JSON from aevb_vae_core.
• Runtime handoff path.
• Command and artifact paths.

Outputs

• Recovery result JSON with numeric loss_delta, proxy declaration, paper target metadata, commands, artifacts, and mechanism checks.

Workflow

1. Preserve target metadata from module_plan.json.
2. Read before/after total loss and compute or verify loss_delta.
3. Require encoder, reparameterization, decoder, reconstruction loss, KL, and optimizer checks; fail closed if any core mechanism check is missing or false.
4. Mark the result as proxy when the plan target is proxy.
5. Record limitations and source-boundary notes.

Validation

Run:

python scripts/build_recovery_result.py --module-plan module_plan.json --trace recovery/logs/training_trace.json --output recovery/recovery_result.json
python tests/test_recovery_evaluation.py

Limitations

This skill evaluates recovery evidence; it does not train a VAE by itself and cannot make a reduced proxy equivalent to full dataset training.