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name: axiom_recovery_evaluation description: Evaluate Integrated Gradients experiments against completeness, sensitivity, implementation invariance, and symmetry axioms.

Axiom Recovery Evaluation

Use this skill after attributions have been computed and a recovery run needs auditable metrics. Do not use it to compute attributions; it evaluates whether computed attributions preserve the paper's axiomatic mechanism.

Inputs

• Attribution vector and output_difference.
• Completeness tolerance, using absolute error for small deterministic examples.
• Optional paired attribution vectors from equivalent implementations.
• Optional symmetric feature index groups.
• Optional sensitivity attribution for a single changed feature.

Outputs

• Numeric completeness error.
• Boolean checks for completeness, sensitivity, implementation invariance, and symmetry.
• Overall proxy_accepted boolean when all supplied mechanism checks pass.

Workflow

1. Compare attribution sum with the model output difference.
2. Mark Sensitivity(a) passed when a one-feature output change has nonzero attribution with the expected sign or magnitude.
3. Compare equivalent-implementation attributions within tolerance.
4. Check equal attributions for symmetric features with equal values.
5. Return a compact metric and mechanism-check dictionary for recovery artifacts.

Validation

Run python tests/test_axiom_evaluation.py or the Distiller validator.

Limitations

This skill validates the recovery mechanism. It does not prove visual saliency quality on ImageNet or replace a full benchmark when that runtime is available.