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name: program_optimization description: Optimize only universal program parameters through a frozen model for adversarial reprogramming recovery experiments.

Frozen-Model Program Optimization

Use this skill when testing whether a fixed model can be repurposed by training an input program. The target model must remain frozen; only the program parameters may change. This is not ordinary fine-tuning.

Inputs

• Synthetic or real task examples and labels.
• A frozen target model or reduced differentiable proxy.
• Input programming and output remapping functions.
• Learning rate and step count.

Outputs

• Updated program parameters.
• Training trace with loss_before, loss_after, params_before, params_after, and accuracy.
• Mechanism booleans showing universal program reuse, frozen model unchanged, remapping use, and optimizer execution.

Workflow

1. Build programmed inputs for all examples using one shared program.
2. Evaluate the frozen target and output remapper.
3. Compute a classification loss.
4. Update only the program.
5. Log parameter and loss changes.

Validation

Run python tests/test_program_optimization.py.

Limitations

The included script is a standard-library reduced proxy for bounded recovery. Full ImageNet recovery should replace the proxy model with the available frozen classifier while preserving the same contract.