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name: movement_recovery_harness description: Run a bounded mechanism-faithful recovery experiment for movement pruning without reading the original source repository.

Mechanism-Faithful Movement Pruning Recovery Harness

Use this skill after the movement-pruning module skills have been generated and validated. It creates a deterministic reduced recovery when full BERT fine-tuning is blocked by runtime constraints. Do not import or inspect the original paper repository during recovery.

Inputs

• Attempt directory containing module_plan.json and environment/runtime_handoff.json.
• Generated skill root containing score_masking, movement_scores, and soft_regularization.
• Seed, sample count, keep ratio, and learning rates.

Outputs

The harness writes recovery_result.json, training_trace.json, generated_data_item.json, command evidence, and generated skill invocation evidence.

Workflow

1. Construct a transfer-style proxy with stale high-magnitude pretrained weights and low-magnitude adaptive weights.
2. Run a real logistic-loss gradient step on trainable parameters.
3. Call the movement score update skill, score masking skill, and soft regularization skill.
4. Compare movement top-v masks to magnitude masks for adaptive-feature retention.
5. Record numeric metrics and mechanism checks, then run the recovery experiment validator.

Validation

Run the harness from the attempt directory with python recovery/run_recovery.py. A successful run decreases loss, changes parameters, records all required logs, and passes the Distiller recovery gate.

Limitations

The default experiment is a reduced proxy, not a full BERT/MNLI/SQuAD reproduction. It is acceptable only in soft recovery mode after full runtime blockers are recorded.