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name: virtual_update_interference description: Rank replay candidates by loss increase under a virtual incoming-batch update for MIR selection.

Virtual Update Interference

Use this skill when a recovery harness or implementation needs the core MIR selector. It computes a one-step virtual update from the incoming batch, evaluates candidate memory examples before and after that virtual update, and selects the examples with the greatest predicted loss increase.

Inputs

• Current linear model parameters or an adapter exposing equivalent loss and gradient behavior.
• Incoming batch examples with features and binary labels.
• Candidate memory examples with features, labels, and optional best historical losses.
• Learning rate, replay budget, and scoring variant smi_1 or smi_2.

Outputs

• Virtual parameters.
• Candidate score records with current loss, virtual loss, and MIR score.
• Top-k selected replay candidates ordered by descending interference.

Workflow

1. Clone the current parameters so the real model is not mutated.
2. Compute the average logistic-loss gradient on the incoming batch.
3. Apply one virtual SGD step to produce theta_v.
4. Score each candidate with loss(theta_v) - loss(theta) for smi_1.
5. For smi_2, subtract min(current_loss, best_loss).
6. Sort by descending score, tie-breaking by stable example id.

Validation

Run the included tests or validate_skill_tree.py --run-tests. They verify non-mutation, ranking of a harmed candidate, empty candidate behavior, and smi_2 historical-loss handling.

Limitations

The bundled script is a deterministic standard-library logistic-regression implementation for small recovery experiments. Larger neural systems should preserve the same contract with framework-specific models.