# Methodology and Review Guidance

## Machine checks included

- schema completeness
- ID uniqueness
- exact user-request uniqueness
- normalized user-request uniqueness
- final-response uniqueness
- structural-fingerprint uniqueness
- held-out archetype split policy
- lexical nearest-neighbor audit

## Human review recommended

A human reviewer should sample every family and check:

- whether evidence actually supports the hidden ground truth;
- whether the tool order is sensible;
- whether constraints are preserved;
- whether verification targets the original failure;
- whether bad-behavior examples are genuinely inferior;
- whether language remains natural and sufficiently diverse;
- whether security assumptions are valid.

## Suggested release gates

1. Review at least 10% of records stratified by family and difficulty.
2. Reject or rewrite records with generic final responses.
3. Run an embedding-based semantic audit.
4. Use a separate model as a critic, but do not treat its score as ground truth.
5. Execute a subset against real miniature repositories before calling the dataset a benchmark.