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name: bar_zeroth_order_optimizer description: Train BAR universal-program parameters with query-only one-sided zeroth-order gradient estimates and SGD updates.

BAR Zeroth-order Optimizer

Use this skill when recovery must optimize adversarial-program parameters against a black-box classifier that exposes only prediction outputs. It is the core BAR training loop from the paper.

Do not use it when white-box gradients are available and the goal is ordinary fine-tuning.

Inputs

• Black-box prediction callable that accepts programmed samples and returns source probabilities.
• Embedded target samples, labels, mask, and initial program parameters.
• Label mapping from source to target classes.
• Focal-loss parameters, smoothing beta, random-vector count q, learning rate, iterations, and seed.

Outputs

• Updated parameters W.
• Loss before and after optimization.
• Prediction/accuracy trace.
• Query count and evidence that an optimizer step changed parameters.

Workflow

1. Evaluate focal loss at current parameters.
2. Draw q normalized random directions with a fixed seed when reproducibility matters.
3. Evaluate perturbed losses at W + beta*U_j using black-box calls only.
4. Average one-sided gradient estimates and update W by SGD.
5. Log losses, query counts, and parameters before/after.

Validation

Run python tests/test_bar_optimizer.py or validate_skill_tree.py --run-tests.

Limitations

This skill can run a reduced deterministic proxy. Full paper-scale results require real source models and target datasets supplied by the caller.