---
pretty_name: TrellisMark Qwen3-4B Blind Rephrasing Corpus
license: other
language:
- en
task_categories:
- text-generation
size_categories:
- 10K<n<100K
tags:
- watermarking
- synthetic-data
- ai-generated-text
- paraphrase
configs:
- config_name: sources
  data_files:
  - split: corpus
    path: sources/*.parquet
- config_name: rewrites
  default: true
  data_files:
  - split: corpus
    path: rewrites/*.parquet
- config_name: evidence
  data_files:
  - split: corpus
    path: evidence/*.parquet
---

# TrellisMark Qwen3-4B blind rephrasing corpus

This separate release contains the frozen detector-blind rephrasing experiment
for **[TrellisMark](https://github.com/xlr8harder/trellismark)**. Its 16,384
source documents are an exact document-ID-preserving subset of the
**[main TrellisMark Qwen3-4B confirmation corpus](https://huggingface.co/datasets/xlr8harder/trellismark-qwen3-4b)**.
It publishes both rewriters' outcome records, retained rewrite text and token
IDs, aligned key-only and model-assisted evidence, and the reports behind the
article's countermeasure figures.

The rewriters received only a 200-Qwen-token source projection. They did not
receive the watermark key, detector, user address, source prompt, or original
preceding conversation. This release is separate from the main corpus because
blind rewriting is a countermeasure dataset with its own models, acceptance
contract, and failure modes.

TrellisMark demonstrates a surveillance capability. This release exists so the
claim and a practical countermeasure can be checked; it is not a recommendation
to deploy user-level watermarking.

## At a glance

| Layer | Rows | What it contains |
|---|---:|---|
| `sources` | 16,384 | One 200-token marked source response for each rephrase job |
| `rewrites` | 32,768 | One frozen outcome for every source under each of two blind rewriters |
| `evidence` | 28,392 | Accepted rewrites with aligned key-only and model-assisted 32 x 4 soft evidence |

| Rewriter | Outcomes | Length-valid rewrites | No length-valid response within budget |
|---|---:|---:|---:|
| Qwen3-4B | 16,384 | 13,531 | 2,853 |
| Gemma 4 12B | 16,384 | 14,861 | 1,523 |

The source pool contains 128 uniformly sampled held-out user addresses with 128
independently prompted responses per address. Canonical source document IDs are
unchanged from main-dataset revision `v1.0.2`, commit
`c3ed277f6cb167bf8339e404828dcdc9099b04c4`.

## Frozen rewrite contract

- Source input: exactly the first 200 Qwen tokens of a marked response.
- Rewriters: `Qwen/Qwen3-4B-Instruct-2507` at revision
  `cdbee75f17c01a7cc42f958dc650907174af0554`, and
  `google/gemma-4-12B-it` at revision
  `707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7`.
- Blindness: no key, detector, address, source prompt, or request history.
- Acceptance: the first response with at least 200 Qwen tokens and a Qwen-token
  length between 90% and 110% of the source length.
- Content attempts: bounded; rows without a length-valid response retain an
  explicit failure reason.
- Analysis prefix: the first 200 Qwen tokens of each accepted rewrite.

Length matching controls the amount of text available to the detector. It is
not a semantic-quality guarantee. The residual-carry analysis separately
reports multilingual embedding similarity and exact word/Qwen-token five-gram
overlap, including detector-blind quality screens.

## Load the data

```python
from datasets import load_dataset

sources = load_dataset(
    "xlr8harder/trellismark-qwen3-4b-rephrasing",
    "sources",
    split="corpus",
)
rewrites = load_dataset(
    "xlr8harder/trellismark-qwen3-4b-rephrasing",
    "rewrites",
    split="corpus",
)
evidence = load_dataset(
    "xlr8harder/trellismark-qwen3-4b-rephrasing",
    "evidence",
    split="corpus",
)
```

`source_document_id` joins this release to the main corpus.
`rewrite_document_id` uniquely identifies a rewriter/job pair. Accepted rows
include token IDs in both the rewriter's native tokenizer and the Qwen tokenizer
used by the detector. Rejected rows retain the frozen outcome and failure
metadata but do not invent a retained rewrite.

Each evidence row contains flattened 32 x 4 matrices under
`key_only_position_by_symbol_scores` and
`model_assisted_position_by_symbol_scores`. Reshape either list to `(32, 4)`
before aggregation and exact Viterbi decoding. The original compressed NPZ
copies used by the analysis are under `evidence_npz/`.

## Reproduce the article results

The `results/` directory contains:

- `rephrase-curve-qwen3-4b-v1.json` and
  `rephrase-curve-gemma4-12b-v1.json`: exact full-registry top-1 and top-10
  recovery as independent accepted rewrites accumulate;
- `rephrase-residual-carry-v1.csv`: row-level embedding similarity, word and
  Qwen-token five-gram carry, token lengths, language labels, and attribution
  diagnostics;
- `rephrase-residual-carry-v1.json`: frozen thresholds, bootstrap summaries,
  detector-blind screens, correlations, and interpretation.

The standalone GitHub repository supplies the encoder and exact Viterbi
decoder. Reproducing the published curves from cached evidence does not require
either rewriter or the Qwen model. Rescoring new text with the model-assisted
method does require the frozen Qwen model and scorer configuration.

`export-manifest.json` records a SHA-256 digest for every release artifact and
the source evidence commitments. The source jobs, public rows, accepted rows,
and cached evidence sets are checked for one-to-one alignment during export.

## Interpreting the quality screens

Embedding similarity is used only as a coarse screen for obvious semantic
drift. It is not a factuality judge. Token five-gram carry is the fraction of a
rewrite's Qwen-token five-grams that occur anywhere in its frozen source
prefix. It is mechanistically relevant because an unchanged five-token span
preserves both an observed token and the four preceding tokens used for local
watermark synchronization.

The article comparison drops the bottom 10% of embedding similarity, the top
10% of token-five-gram carry, or both, using quality/overlap variables alone.
It does not inspect watermark evidence when deciding which rewrites to retain.
These are descriptive screens rather than a causal decomposition of what makes
watermark evidence survive.

## Scope and limitations

- This is an experimental existence proof and countermeasure study, not a
  production security system.
- Both attacks rewrite marked Qwen3-4B text; other source and rewriter models
  may behave differently.
- The length-only acceptance rule is intentionally simple and does not ensure
  factual or stylistic equivalence.
- Failed content attempts are represented by their frozen failure outcome; the
  release does not reconstruct or promote rejected attempts as accepted text.
- Reported recoveries are finite held-out observations, not population
  guarantees.

## Sources, attribution, and licensing

Source and same-family rewrite text use
[`Qwen/Qwen3-4B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507),
whose model card lists Apache-2.0. Cross-family rewrites use
[`google/gemma-4-12B-it`](https://huggingface.co/google/gemma-4-12B-it) under its
model terms. Source prompt identifiers derive from
[`allenai/WildChat`](https://huggingface.co/datasets/allenai/WildChat), whose
card lists ODC-By; prompt text is not included here.

The Hugging Face `license: other` label reflects the mixed contents of the
repository. **Our original TrellisMark contribution is MIT licensed**,
including the metadata schema, numeric evidence, analysis tables, and result
reports. Generated prose and source-derived identifiers may remain subject to
applicable upstream terms.

TrellisMark is an independent research project. It is not affiliated with or
endorsed by Google, Qwen, AllenAI, Hugging Face, or the Gemma team.