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name: generative_latent_mir description: Score latent replay candidates with KL drift, entropy confidence penalties, and diversity filtering for MIR.

Generative and Latent MIR

Use this skill when MIR must operate over generated samples, compressed memories, or a deterministic latent proxy rather than raw replay examples. It captures the paper's generative MIR mechanism without requiring a VAE for small validation tests.

Inputs

• Latent candidate ids and vectors.
• Pre-update and virtual-update class probability distributions for each candidate.
• Entropy weight, diversity distance threshold, and replay budget.
• Optional nearest-neighbor mapping from latent points to stored compressed examples.

Outputs

• KL, entropy, and combined score per candidate.
• A diversity-filtered ranked selection.
• Mechanism diagnostics showing KL drift and entropy confidence were evaluated.

Workflow

1. Normalize candidate probability vectors with small numerical smoothing.
2. Compute KL(y_pre || y_hat) for prediction drift after the virtual classifier update.
3. Compute entropy of y_pre and subtract entropy_weight * entropy.
4. Sort candidates by descending score.
5. Apply greedy diversity filtering by Euclidean distance in latent space.
6. Return selected candidates and diagnostics for recovery logging.

Validation

Run the included tests or validate_skill_tree.py --run-tests. Tests cover KL ranking, entropy penalties, smoothing, and diversity filtering using standard-library numeric fixtures.

Limitations

This helper validates the latent MIR objective and selection contract. It does not train an encoder, decoder, or VAE; a full neural implementation should provide probability vectors and latent candidates from the model stack while preserving the same output schema.

Validation note: the diversity smoke test intentionally verifies that near-duplicate latent candidates can be suppressed while a distant interfered candidate remains selected.