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name: ewc_recovery_evaluation description: Run a bounded EWC retention recovery comparison and emit validator-compatible result, trace, and mechanism-check artifacts.

EWC Recovery Evaluation

Use this skill to execute a reduced or full sequential-training comparison for Elastic Weight Consolidation. It should be used after task protocol, Fisher importance, and EWC penalty skills are available.

Inputs

• Protocol JSON with task order and datasets.
• Generated skill root containing ewc_task_protocol, ewc_fisher_importance, and ewc_penalty.
• Module-plan target metadata.
• Output paths for recovery artifacts.

Outputs

• recovery_result.json with numeric retention metrics and mechanism checks.
• training_trace.json with before/after loss and parameter values.
• Skill invocation evidence proving generated modules were used.

Workflow

1. Fit task A and snapshot parameters.
2. Estimate a diagonal Fisher from task-A gradients.
3. Train task B with plain SGD and with EWC.
4. Compare task-A retention and Fisher-weighted drift.
5. Save command-produced recovery artifacts.

Validation

Run:

python tests/test_recovery_evaluation.py

Limitations

The bundled harness is a reduced synthetic proxy. It does not claim full permuted-MNIST or Atari reproduction.