Make agreement mean something

Two implementations agreeing proves they satisfy a prespecified contract. It does not prove either is correct, and it never validates the method or its assumptions. Both can be wrong in the same way — especially when one was written by reading the other. Design the comparison so that agreement is informative and disagreement is legible.

Rule: freeze before you compare; test the comparator before you trust it.

1. State the claim and the reference

Write down: what is being compared, which side is the reference, and what agreement would and would not establish. "Matches the R package" is a conformance claim, not a correctness claim. Say so explicitly, so nobody later reads parity as validation.

2. Freeze the inputs before inspecting anything

Record and fix: data versions or hashes, code and package versions, random seeds or realized sample splits, options and defaults, the outputs to be compared, and the acceptance thresholds. Freezing after a first look invites tolerance drift toward whatever the run produced.

Do not compare defaults across systems as if only the language changed. Map the choices explicitly — a "default" is a substantive modeling decision that usually differs between implementations.

3. Declare tolerance classes, and make them binding

Do not carry a single fuzzy epsilon. Classify each output:

• EXACT — names, ordering, sample masks, counts, statuses, return/error codes, warning classes, option defaults. Byte-equal after documented normalization.
• Scalar numeric — deterministic estimates, standard errors, p-values, critical values. State absolute and relative tolerances and the justification.
• Matrix/vector — covariance matrices, influence summaries, weight vectors, plot data.
• Stochastic — must meet a prespecified error-rate criterion with uncertainty reported.

A looser tolerance may be used only through a recorded approved divergence with a reason. Silent widening is the most common way a parity gate stops testing anything.

4. Crosswalk and output inventory

Write the mapping between the two implementations, plus an inventory of every expected output with expected row/cell counts. Every declared object must have a live comparison or an explicit out-of-scope reason. Without an inventory, both sides can silently omit the same result and the comparison reports success.

5. Build cases that isolate mechanisms

Not just the happy path:

• analytically solvable or known-truth cases;
• relevant data problems (missing, unbalanced, near-collinear, extreme-but-valid weights, shuffled row order, ties);
• compound cases combining several problems;
• published examples;
• randomized valid designs across the supported surface, not a handful of fixtures.

Fixed fixtures are necessary but not sufficient — they test what the author already thought of.

6. Test the comparator itself

Before trusting a green result, feed the comparison a wrong value, a missing result, a misaligned row, and an empty result. It must fail, not skip. A comparator that silently passes over what it cannot reconcile turns every subsequent green into noise. (See vaccinate.)

7. Compare every declared channel

Not only the headline coefficient: estimates, uncertainty measures, sample counts, labels and ordering, diagnostics, warnings, and failure statuses. Divergent warnings and differing error behavior are real defects — they change what a user does next.

8. Give every divergence an ID and a smallest witness

For each difference: a stable identifier, the smallest reproducible case, and a classification — defect / intentional difference / limitation of the reference / unresolved. Unresolved stays red; it is not averaged away or waived. A fix must turn its witness green and survive a rerun of the full audit, so a local patch does not break something else.

9. Adversarial expansion by someone else

Fixtures written by the implementer test the implementer's mental model. Have an independent reviewer add designs not shared in advance, and preserve any that reveal bugs or materially increase coverage as permanent fixtures. This is the cheapest defense against a suite that passes because it was written to pass.

10. Close with separate reviews and an honest scope statement

End with distinct scientific and implementation sign-off, recording: checks run, open findings, accepted differences, explicit non-claims, and who approved release. State plainly that the audit establishes conformance to the frozen contract — not the truth of the method.

Minimum checklist

1. Name the reference; state what agreement would and would not establish.
2. Freeze versions, hashes, seeds, options, outputs, tolerances.
3. Declare binding tolerance classes; record any approved divergence.
4. Crosswalk + expected-output inventory with counts.
5. Known-truth, dirty, compound, and randomized cases.
6. Seed comparator faults — it must fail, not skip.
7. Compare all channels, including warnings and failures.
8. Stable ID + smallest witness + classification per divergence; unresolved stays red.
9. Independent reviewer adds unseen designs.
10. Separate sign-offs; state the non-claims.

Cross-references

• provenance-and-ground-truth.md — ranked oracles, declared precedence, the divergence taxonomy
• .claude/rules/replication-protocol.md — the tolerance contract