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name: pruning_recovery_harness description: Run a bounded end-to-end proxy recovery for generalization-influence dataset pruning using generated module skills.

Pruning Recovery Harness

Use this skill when the full CIFAR-scale paper experiment is blocked or too expensive and soft-mode recovery permits a declared reduced proxy. The harness must invoke or cross-check the generated influence, pruning, and bound-check skills and produce executable recovery evidence.

Inputs

• Attempt directory with module_plan.json and environment/runtime_handoff.json.
• Generated skills root containing the other module skills.
• Output paths for recovery logs and result JSON.

Outputs

• recovery_result.json-compatible object with metrics and mechanism checks.
• Training trace with params_before and params_after.
• Generated data item and skill invocation logs.

Workflow

1. Build a deterministic tiny binary-classification dataset.
2. Use the influence skill to estimate per-example parameter influences.
3. Use the aggregate pruning skill to remove the largest feasible subset.
4. Train one logistic-regression update on the retained data and log loss/parameter changes.
5. Use the gap-bound skill to validate target metadata, aggregate influence, and observed gap.
6. Write recovery artifacts under the current attempt.

Validation

Run python scripts/run_proxy_recovery.py --help and the recovery experiment validator on the attempt after execution.

Limitations

The proxy is mechanism-faithful but not a full CIFAR reproduction. It must be labeled is_proxy: true and must not claim full model or full dataset execution.