Proxy Training Recovery Harness

Use this skill when full image-dataset retraining is blocked but soft-mode recovery permits a declared mechanism-faithful proxy. The harness must import or call the scoring, median-selection, and ablation skills, produce executable logs, and write validator-compatible recovery artifacts.

Inputs

• Attempt directory containing module_plan.json and environment/runtime_handoff.json.
• Generated skills root containing the Moderate-DS module skills.
• Optional synthetic dataset parameters for stress tests.

Outputs

• recovery/recovery_result.json with numeric metrics and mechanism checks.
• recovery/logs/generated_data_item.json describing the proxy data.
• recovery/logs/training_trace.json showing loss before and after a real optimizer update.
• recovery/logs/generated_skill_invocations.json proving all core skills were exercised.

Workflow

1. Create a deterministic class-cluster representation dataset with central, moderate, and far points.
2. Import the generated scoring, median-selection, and ablation scripts from the generated skills root.
3. Select the moderate coreset and compare it against extreme policies.
4. Run a simple trainable linear proxy update on the selected coreset and log parameter changes.
5. Write recovery artifacts and source boundaries before invoking validation.

Validation

Run python scripts/run_proxy_recovery.py --attempt-dir <attempt_dir> --skills-root <skills_root>. Then run the Distiller recovery validator on the attempt directory.

Limitations

This is not a full CIFAR-100/ImageNet reproduction. It is a soft-mode proxy that validates the mechanism under bounded runtime constraints.