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name: mir_recovery_evaluation description: Run bounded MIR recovery experiments and emit accuracy, forgetting, traces, and mechanism evidence.

MIR Recovery and Evaluation Harness

Use this skill after MIR module skills exist and a runtime handoff has been produced. It orchestrates a small online continual-learning experiment, invokes or cross-checks the generated memory and scoring skills, and writes recovery artifacts suitable for validation.

Inputs

• Attempt directory containing module_plan.json and environment/runtime_handoff.json.
• Generated skills root containing online memory, virtual-update interference, and optional latent MIR helpers.
• Output directory under recovery/.
• Fixed seed and reduced/proxy declaration when full benchmark recovery is blocked.

Outputs

• recovery_result.json with paper target metadata, numeric metrics, commands, and mechanism checks.
• logs/training_trace.json with updates, selected replay examples, losses, and parameters.
• logs/generated_skill_invocations.json listing generated skill usage.
• source_manifest.json listing allowed sources.

Workflow

1. Load module_plan.json.fast_recovery_target without changing dataset, metric, or target metadata.
2. Build a deterministic non-iid stream with three small binary tasks.
3. Run MIR and a baseline selector using the generated online memory and virtual-update scoring contracts.
4. Evaluate final accuracy and forgetting from task-level histories.
5. Cross-check the latent MIR helper with a tiny probability-vector fixture when no real generator is available.
6. Save all JSON artifacts and report proxy limitations explicitly.

Validation

Run the script directly in a recovery directory, then run the Distiller recovery validator. Included unit tests exercise metric computation and artifact structure without using external packages.

Limitations

This skill provides a reduced proxy harness for bounded soft-mode recovery. It does not claim to reproduce the paper's full MNIST, CIFAR-10, or MiniImagenet multi-seed results.