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name: coreset_recovery_harness description: Run a bounded CCS proxy experiment and emit validator-compatible recovery evidence.

Coreset Recovery Harness

Use this skill during Paper2Skills recovery for Coverage-centric Coreset Selection when full CIFAR/ImageNet training is blocked by runtime cost or missing datasets. It builds a deterministic proxy dataset, invokes the generated scoring and selection skills, compares CCS against monotonic top-score selection, and runs a tiny optimizer update on the selected examples.

Do not use this harness to claim full paper accuracy. It produces soft-mode proxy evidence with explicit mechanism checks and source-boundary-safe logs.

Inputs

• Attempt directory containing module_plan.json and environment/runtime_handoff.json.
• Generated skills root containing training_dynamics_scoring and coverage_stratified_selection.
• Output directory under the current attempt's recovery/ tree.

Outputs

• recovery_result.json with numeric coverage_gain_over_monotonic.
• logs/generated_data_item.json describing the synthetic benchmark.
• logs/training_trace.json with params_before and params_after.
• Mechanism checks that prove scoring, mislabel filtering, stratified coverage, and optimizer execution ran.

Workflow

1. Construct a deterministic two-class score-stratified dataset.
2. Generate probability traces and call training_dynamics_scoring.compute_scores.
3. Call coverage_stratified_selection.select_coverage_coreset and monotonic_selection.
4. Measure coverage gain as represented score-bin fraction difference.
5. Run one logistic-regression-style gradient step on CCS-selected examples.
6. Save all validator-compatible artifacts from the executable command.

Validation

Run python tests/test_coreset_recovery_harness.py; during recovery, run the script as python scripts/run_proxy_recovery.py --attempt-dir ... --skills-root ....

Limitations

This harness is intentionally small and deterministic. It verifies the mechanism, not the paper's full dataset accuracy.