LEADERBOARD_METHODOLOGY.md

# Provisional tokenizer leaderboard methodology

This leaderboard is a diagnostic ranking on the small synthetic suite, not a
claim about downstream model quality.

## Eligibility gates

A row receives a rank only when the benchmark succeeds, adapter fidelity is
`exact`, every evaluation string round-trips exactly, and observed unknown-token
rate is zero. `core_only` artifacts
and round-trip failures remain in both outputs with a reason and no rank.

## Vocabulary-adjusted provisional quality

Raw tokens per word favors large vocabularies. For each of the ten domains, the
script fits a weighted least-squares line across eligible artifacts:

```
ln(domain tokens/word) = intercept + slope * log2(vocabulary size)
```

Each row is weighted by `1 / eligible rows from its author`, so every author has
equal total influence on the fitted baseline. This prevents one author's many
variants from defining expected compression. Residuals are also converted to
author-balanced percentiles, rescaled so the observed best is 100 and worst is
0. The `adjusted_compression_index` is the geometric mean residual ratio across
domains: below 1 is better, but it is diagnostic rather than the score itself.

The quality score gives every domain equal influence:

```
0.80 * mean(domain percentile) + 0.20 * lower-quartile(domain percentiles)
```

The lower-quartile term rewards tokenizers that avoid weak domains. Quality is
shown to one decimal. Adjacent entries within two points of the leading score
in their tier share a competition rank, because this suite does not support
fine-grained distinctions. This score is relative to the current eligible
cohort and changes when submissions change.

## Artifact readiness (reported separately)

Native Hugging Face Tokenizers artifacts receive 100. Exact custom artifacts
requiring the Python reference adapter receive 70. This deliberately small
column describes direct operational loadability. It is **not included** in the
quality score.

## Independent author evidence package (reported separately)

`results/author_evidence_scores.csv` is an independent author-level review of
artifact usability, documentation, evaluation protocol, reproducibility, and
claims discipline. Its total out of 20 is multiplied by five and exposed as
`evidence_package_score` out of 100. Every tokenizer by an author inherits that
author-level score. The `kacperwikiel` reference baseline was not part of the
author evidence review and therefore has a null score. Evidence is **not
included** in tokenizer quality or rank.

Source repository, path, pinned commit, and SHA-256 completeness remain visible
as `traceability_score` and `traceability_checks_json`. These fields describe
artifact provenance only and are not called evidence-package quality.

Encoding and decoding throughput are retained as `info_only` columns. They are
not scored because native Rust and Python reference-adapter runtimes are not
comparable. Unknown-token rate is also retained but is not a differentiator in
the current suite, where every artifact reports zero observed unknowns.

`kacperwikiel` is explicitly labeled as the reference baseline. For authors
with multiple eligible variants, their leading row is labeled
`author_best_of_N_selection_bias`; choosing the best of many trials can inflate
its apparent standing relative to single submissions.

Leave-one-author-out ranks are not reported. With only a tiny synthetic suite,
few authors, uneven vocabulary-size coverage, and some authors occupying unique
size regions, refitting after removing one author can become extrapolation and
would look more authoritative than it is. Author-balanced fitting, explicit
best-of-many labels, one-decimal scores, and two-point rank tiers are the
current sensitivity safeguards. A larger natural held-out corpus and more
authors per size band are needed before meaningful leave-one-author-out claims.

Reproduce after running the benchmark:

```bash
python3 build_leaderboard.py
```