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name: bbvi_proxy_recovery_harness description: Run a bounded mechanism-faithful proxy recovery for Black Box Variational Inference using generated BBVI estimator and optimizer skills.

BBVI Proxy Recovery Harness

When To Use

Use this skill when full BBVI paper reproduction is blocked by unavailable private data or long runtime, and soft-mode recovery permits an explicit proxy. The proxy must still execute score-function gradients, variance reduction, and stochastic optimization.

Do not use it as success evidence in hard recovery mode or when a full real-data result is feasible within budget.

Inputs

• Attempt directory containing module_plan.json and environment/runtime_handoff.json.
• Generated skill root containing bbvi_score_function_gradient, bbvi_variance_reduction, and bbvi_stochastic_optimizer.
• Fixed seed and small synthetic Normal observations.

Outputs

• recovery/recovery_result.json.
• recovery/logs/generated_data_item.json.
• recovery/logs/training_trace.json.
• recovery/logs/generated_skill_invocations.json.
• Numeric variance-reduction and optimizer evidence.

Workflow

1. Load the module plan and runtime handoff.
2. Generate synthetic Normal observations from a fixed seed.
3. Build deterministic BBVI sample arrays and log-density signals.
4. Invoke generated score-gradient, variance-reduction, and optimizer scripts by subprocess.
5. Record invocation evidence and mechanism checks.
6. Write a validation-ready recovery result whose target matches the module plan.

Validation

Run:

python tests/test_proxy_harness.py

Limitations

This is a reduced/proxy recovery. It does not reproduce the medical-data predictive likelihood table or the 20-hour MCMC comparison.