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name: induction_head_detector description: Detect induction-head copying behavior on repeated token sequences with explicit mechanism checks.

Induction Head Detector

Use this skill when testing whether an attention pattern or simplified circuit implements the induction-head algorithm [a][b] ... [a] -> [b]. It is especially useful for Transformer Circuits style recovery experiments on repeated random-token sequences. Do not count positions with no previous occurrence as induction successes.

Inputs

• A token sequence as strings or ids.
• Optional policy for selecting the earlier matching source; the default is the most recent previous occurrence.
• Optional expected next tokens or labels.

Outputs

• Applicable positions where a previous occurrence and successor token exist.
• Predicted next tokens copied from the earlier successor.
• Accuracy on applicable positions.
• Mechanism checks: previous-token shift, same-token matching, OV copying assumption, and repeated-token prediction.

Workflow

1. Iterate over destination positions except the final position.
2. Find earlier source positions whose token equals the destination token and that have a successor.
3. Select the most recent matching source.
4. Predict the token after that source.
5. Compare with the actual next token and summarize accuracy.

Validation

Run the included tests or the Paper2Skills skill-tree validator with --run-tests.

Limitations

This skill validates the behavioral core of induction heads. For a trained model, pair it with QK/K-composition and OV-copying evidence from the relevant matrices.