# Independent submission evidence review

Scope: the seven contributor folders selected for the normalized dataset at source commit
`1a5cd2c2e4df2287b4c19b3dbf5051f5d460fdc1`. This is a review of submitted evidence,
documentation, and reproducibility—not a ranking of tokenizer quality. Author-reported
compression values use different corpora, held-outs, vocabulary sizes, and word definitions
and are therefore not compared across authors.

## Rubric

Each dimension is scored 0–4 (maximum 20):

- **Artifact usability:** complete runtime artifact, loadability, round-trip/special-token readiness.
- **Documentation:** design, data, configuration, outputs, and limitations are explained.
- **Evaluation protocol:** held-out construction, denominators, controls, stress tests, and baselines.
- **Reproducibility:** committed code, pinned inputs, seeds/environment, and runnable evaluation.
- **Claims discipline:** conclusions match evidence; confounds and non-comparability are acknowledged.

The score measures strength of the evidence package only. It must not be combined with future
common-corpus benchmark results as if it were a tokenizer-performance score.

## Results

| GitHub author | Artifact | Docs | Protocol | Repro | Claims | Total | Evidence judgment |
|---|---:|---:|---:|---:|---:|---:|---|
| `KateMajzel` | 4 | 4 | 4 | 2 | 4 | **18** | Strongest controlled-methodology package; excellent failure analysis and explicit limits. |
| `janbanot` | 4 | 4 | 3 | 1 | 4 | **16** | Broadest qualitative/stress analysis and a directly loadable final artifact; exact split/code absent. |
| `Maggio333` | 2 | 4 | 4 | 1 | 4 | **15** | Deepest research narrative and unusually good caveats; custom runtime and missing scripts prevent replay. |
| `olajachymiak` | 2 | 4 | 4 | 1 | 4 | **15** | Strong progression of controlled experiments and candid overfitting analysis; referenced scripts absent. |
| `dawidmajewski` | 4 | 3 | 2 | 1 | 3 | **13** | Seven loadable artifacts and concrete corpus/source tables; mostly exploratory, with no fixed replay harness. |
| `ktalik` | 4 | 2 | 1 | 1 | 2 | **10** | Loadable minimal tokenizer plus interactive plots; sparse protocol and referenced training/report code absent. |
| `p4pryk` | 1 | 3 | 2 | 1 | 2 | **9** | Useful design explanation and corpus accounting, but only a custom vocab/merge map was submitted. |

## Per-author findings

### `KateMajzel` (source folder `KasiaMP`)

- Best evidence of experimental hygiene: identical stated corpus size (9,363,020 chars), matched
  total vocab (6,756), shared 2,696-character/371-word held-out, and the same word denominator.
- Documents and repairs serialization loss, boilerplate contamination, corpus reconstruction drift,
  and the 6,500-merges versus 6,756-total-vocab convention. Four HF artifacts load directly.
- Excellent claims discipline: calls the 1.3% spread inconclusive and explicitly lists limitations.
- Reproduction gap: no training/evaluation code, held-out text, corpus manifest/hash, seed, lockfile,
  or one-command replay is present. Character count is a useful check but not a cryptographic identity.

### `janbanot` (source folder `Janek`)

- Directly loadable HF ByteLevel BPE (8,192); comprehensive discussion of corpus balancing,
  vocabulary sweep, utilization, morphology, multilingual/emoji/numeric/code stress cases, and limits.
- Small peer-baseline suite is clearly described as contextual rather than a definitive benchmark.
- Protocol gap: the exact held-out composition/identity and split procedure are not committed, and
  neither training nor evaluation code is present. Reported tables therefore cannot be replayed.

### `Maggio333` (source folder `Arek`)

- Most ambitious research account: matched-vocab pre-tokenizer comparisons, vocabulary curve,
  distribution-shift matrix, compute-head trade-off, Renyi efficiency, and morphology caveats.
- Explicitly warns that different held-outs cannot be compared and that compression is not downstream
  model quality. That restraint is exemplary.
- All 17 artifacts use a custom BPE serialization and fail direct HF `Tokenizer.from_file` loading.
  A loading sketch is documented, but the actual encoder/pre-tokenizer implementation is absent.
- Major replay gap: referenced `vocab_cost.py`, training/evaluation code, exact data manifests,
  held-out artifacts, and environment are not in the repository. Several broad empirical claims are
  supported only by prose/tables embedded in the README.

### `olajachymiak` (source folder `ola`)

- Strong pedagogical chain: controls corpus at vocab 512, controls vocab on one corpus, exposes the
  in-domain `Quo Vadis` illusion, then builds an 8k diverse/pretokenized version.
- Clearly reports train/held-out boundary for the book experiment, exact held-out counts, round-trip,
  overfitting mechanisms, and remaining weaknesses.
- All four JSONs are custom experiment bundles rather than directly loadable HF serializations.
- Referenced `homework_diverse.py` and `homework_vocab_sweep.py` are absent, as are the exact corpora,
  corpus hashes, environment, and executable evaluator. The final Pan Tadeusz result is not a fully
  out-of-domain test because the training mix is still majority Polish literature.

### `dawidmajewski` (source folder `dawidm`)

- Seven submitted tokenizer files load directly. The writeup gives source URLs/revisions for test
  snippets, corpus sizes, regex, vocab variants, round-trip claim, and raw token-count tables.
- The author accurately labels the work exploratory rather than research, which appropriately limits claims.
- Training-corpus fertility appears to be reported alongside short out-of-corpus token counts; there is
  no fixed held-out benchmark across every model, no vocabulary-utilization analysis, and no statistical
  treatment. Code, pinned training inputs, preprocessing, seeds, and evaluator are absent.

### `ktalik` (source folder `Konrad`)

- Submitted 456-vocab HF ByteLevel BPE loads directly; three HTML plots preserve some experimental output.
- README states SJP scale, merge sweep, and an aggregate token-count trend.
- The claimed `bpe.py` and `report.py` are not committed. No train/eval split, exact word list,
  preprocessing, denominator, round-trip suite, corpus version/license, or reproducible command is given.

### `p4pryk` (source folder `patryk`)

- Explains ByteLevel motivation, a Polish regex, balanced DynaWord/Wikipedia sampling, length/newline
  constraints, exact stated training character count, and a Pan Tadeusz evaluation.
- Submitted JSON is only a custom vocabulary/merge mapping, not a complete runtime tokenizer; it lacks
  normalizer, pre-tokenizer, decoder, added-token policy, and a runnable loader.
- No code, exact data manifest/hash, held-out artifact, environment, or evaluation command is present.
  Phrases such as "commercial standard", "ideal", and character compression as taking less storage
  overstate what round-trip and token-count results establish. The held-out may also be adjacent in
  literary domain to some training sources.

## Cross-submission conclusions

1. **Do not select a winner from reported metrics.** Vocabulary ranges from 456 to 512,000 and evaluation
   domains range from the training corpus to held-out book tails, Pan Tadeusz, SpeakLeash, and tiny probe suites.
2. **Artifact readiness differs sharply.** `KateMajzel`, `janbanot`, `dawidmajewski`, and `ktalik` have at
   least one directly loadable HF artifact. The others need author-specific adapters or reconstruction.
3. **No submission is fully reproducible from this repository.** The repository contains no contributor
   training/evaluation scripts; exact evaluation texts and dependency environments are also absent.
4. **Documentation distinction:** strongest controlled-methodology evidence is `KateMajzel`; broadest
   research analysis is `Maggio333`; broadest practical stress analysis is `janbanot`; clearest controlled
   learning progression is `olajachymiak`.
5. **Next judging step:** run loadable artifacts (and validated adapters for custom formats) through one
   versioned, multi-domain Polish test manifest. Publish per-domain metrics and Pareto fronts within vocab
   bands; keep this evidence score as a separate reproducibility/documentation axis.

## Minimum evidence upgrade requested from every author

- `train.py` and `evaluate.py` (or notebook exported with deterministic cells), dependency lock, and commands.
- Corpus source/version/license, preprocessing config, byte count plus SHA-256, and deterministic split rule.
- Committed held-out manifest or hashes, explicit word-count definition, round-trip/stress test corpus.
- Standard `tokenizer.json` plus special-token configuration, or a versioned adapter with parity tests.
- Machine-readable result JSON containing tokenizer hash, dataset hash, code commit, environment, and timings.