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name: baseline_path_protocol description: Build and validate absence baselines and straight-line interpolation paths for Integrated Gradients recovery experiments.

Baseline Path Protocol

Use this skill when an Integrated Gradients experiment needs a baseline and interpolation points. Do not use it to choose a task label, evaluate model quality, or replace the attribution computation itself.

Inputs

• input_vector: numeric feature vector for the explained example.
• baseline_vector: same-length vector representing absence of signal.
• steps: positive integer Riemann step count.
• Optional baseline_score and near_zero_tolerance for baseline diagnostics.

Outputs

• Ordered interpolation points for alpha = 1/steps ... 1.
• Baseline metadata with warnings when the baseline score is not near zero.

Workflow

1. Check that the input and baseline vectors are non-empty and equal length.
2. Check that steps is positive.
3. Generate the straight-line path baseline + alpha * (input - baseline).
4. Include the original input as the last point and omit alpha zero for Equation 3 style averaging.
5. Record baseline-score warnings without replacing the caller's baseline.

Validation

Run python tests/test_baseline_path.py from this skill directory or validate through the Distiller skill-tree validator.

Limitations

This skill does not compute gradients or decide whether a baseline is semantically perfect; it provides the auditable protocol and diagnostics required by the paper.