xlr8harder/trellismark-qwen3-4b

TrellisMark Qwen3-4B confirmation corpus

This is the frozen English confirmation corpus for TrellisMark, an experimental many-user AI-text watermark. It includes exact generated text and token IDs, unwatermarked Qwen controls, public-key detector evidence, the public research key, independent encoder vectors, and the result reports used for the reader-facing curves. The standalone implementation, detector, and reproduction instructions are in the TrellisMark GitHub repository.

The mixed-author benchmark used for the article is published separately as the TrellisMark open-corpus companion. It is a document-ID-preserving subset of this corpus with disjoint calibration and test users, aligned key-only and model-assisted evidence, frozen corpus recipes, and raw scenario reports.

The countermeasure figures use the separate TrellisMark blind-rephrasing companion, which preserves these source document IDs and adds two detector-blind rewrite cohorts, rewrite evidence, and row-level semantic/overlap diagnostics.

The marked corpus covers 256 user IDs sampled uniformly from the complete 30-bit address space, plus 64 consecutive user IDs (0–63) included as a deliberately awkward diagnostic block. Each marked row exposes the exact encoded address directly as the integer user_id.

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

At a glance

Layer	Rows	What it contains
documents	76,598	46,080 marked and 30,518 unwatermarked raw Qwen responses
evidence	155,550	Complete cached position-by-symbol evidence matrices at 100, 200, or 800 tokens
sentence_evidence	20,846	Detached-sentence token counts plus aligned key-only and model-assisted evidence
black_box_responses	18,000	Raw matched marked/unwatermarked black-box query outcomes

No completion filtering or editing is applied. These are the frozen raw model outputs from prompts sampled from the public WildChat dataset.

Basic recovery curve

This frozen key-only baseline uses 128 uniformly sampled held-out user IDs, with five independently resampled collections per user at each point. Each step adds the first 200 tokens of one independently prompted response, and every ranking searches the complete 2^30 address space exactly. The source report is results/article-dense-recovery-v2.json.

Public research key

The key is deliberately public so anyone can reproduce the key-only evidence:

1e7c4264154e38dba29a0fa294654c04e06af7865c90196c24e54e69a7c3b8ae

Never reuse this key for a deployment. The complete frozen configuration is in config/watermark-config-v1.json, and independent address-to-codeword vectors are in config/address-codewords-v1.json.

Load the data

from datasets import load_dataset

documents = load_dataset(
    "xlr8harder/trellismark-qwen3-4b",
    "documents",
    split="corpus",
)
evidence = load_dataset(
    "xlr8harder/trellismark-qwen3-4b",
    "evidence",
    split="corpus",
)
sentences = load_dataset(
    "xlr8harder/trellismark-qwen3-4b",
    "sentence_evidence",
    split="corpus",
)
black_box = load_dataset(
    "xlr8harder/trellismark-qwen3-4b",
    "black_box_responses",
    split="corpus",
)

The standalone detector and watermark implementation live at xlr8harder/trellismark. Detection should use the released token_ids; decoding and re-encoding response_text is not guaranteed to preserve every tokenizer boundary.

Data contract

documents

Each row includes:

• document_id, condition, generation_tier, and split/cohort metadata;
• the exact encoded 30-bit user_id for marked rows (null for controls);
• exact response_text, Qwen token_ids, token count, finish reason, and seed;
• model revision, sampling settings, payload symbols, and artifact hashes;
• stable prompt ID and upstream row hash, but not the prompt text;
• eligibility flags for the 100-, 200-, and 800-token evidence panels.

Unwatermarked rows have null payload/address fields where appropriate. The uniform_full_keyspace cohort was sampled across the complete 30-bit address space. The compact_low_address cohort deliberately covers consecutive low addresses as a pathological-address diagnostic.

evidence

Each row contains the same plain user_id plus a flattened 32 × 4 position_by_symbol_scores matrix. Reshape the matrix to (32, 4) before aggregation and Viterbi decoding. Available panels are:

Method	Prefix	Rows
key only	100 tokens	71,478
key only	200 tokens	20,480
key only	800 tokens	22,632
with model assistance	100 tokens	20,480
with model assistance	200 tokens	20,480

The model-assisted panel is the frozen six-feature response-only likelihood mixture. It uses no source prompt. Its coefficients, scales, null centering, and source-manifest hashes are preserved in export-manifest.json.

The 100-token model-assisted panel is the exact partial-response evidence used for the bounded shortlist-presence curve. Its NPZ bundle and full derivation metadata are also preserved under derived/model-assisted-100/.

Generation protocol

• Model: Qwen/Qwen3-4B-Instruct-2507
• Frozen revision: cdbee75f17c01a7cc42f958dc650907174af0554
• Dtype: bfloat16
• Sampling: temperature 0.7, top-k 100, top-p 1.0
• Carrier: SynthID-style token tournaments with a 30-bit address encoded as 32 radix-4 symbols
• Prompt source: independently sampled allenai/WildChat rows
• Language: deterministic English filtering before final acceptance

Prompts are omitted from this release. Stable prompt IDs and source-row hashes retain pairing and provenance without republishing the source conversations.

Raw model output

All frozen completions are published without filtering or editing, together with their exact token IDs. They are raw model outputs from public-source prompts and can contain incorrect, offensive, or credential-like generated text. Apparent personal or credential data should not be treated as authentic.

Reproducibility and reports

export-manifest.json records source-manifest metadata and SHA-256 hashes for all Parquet shards. The results/ directory contains:

• negative-controls-v2.json: wrong-key, unwatermarked, permuted-address, and exhaustive-small-space checks;
• article-dense-recovery-v2.json: frozen 200-token recovery checkpoints;
• article-rank-curve-v3.json: exact top-1, top-5, top-10, top-50, top-100, top-500, and top-1,000 presence rates at every 100-token checkpoint;
• sentence, null-dilution, black-box, open-corpus, and rephrasing reports used by the remaining article-facing figures.

The exact PNG/SVG article figures are under figures/article/. Their inputs are either in this repository or in the prominently linked open-corpus and rephrasing subsets, both of which preserve canonical document IDs from this release. derived/sentences/ contains the sentence evidence bundle and derived/black-box/ contains all raw detector queries and the frozen report.

The rank reports search the complete 2^30 registry with exact Viterbi maximization. Viterbi exploits the convolutional code's shared trellis state; it does not approximate the billion-address search or substitute a sampled candidate set.

Generation is statistically reproducible from the saved seeds and pinned configuration. Byte-identical prose may additionally depend on model files, vLLM, PyTorch, CUDA, GPU kernels, hardware, and request concurrency. The released text, token IDs, evidence, reports, and hashes are the canonical frozen artifacts.

Scope and limitations

• This is an experimental existence proof, not a production security system.
• Reported accuracies are finite held-out observations, not population guarantees.
• Address ranking assumes marked evidence is present. Generic AI-text detection and open-set watermark presence are different questions.
• The public raw null set contains independently prompted unwatermarked Qwen output. Human ELI5 prose used in one internal null check is not redistributed because its upstream dataset card does not state a license.
• Rephrasing, mixtures of human and AI text, model mismatch, very short fragments, and repeated-document dependence are separate operating regimes.

Sources, attribution, and licensing

The generated responses use Qwen/Qwen3-4B-Instruct-2507, whose model card lists Apache-2.0. Prompt identifiers derive from allenai/WildChat, whose card lists ODC-By and describes its de-identification process; prompt text is not included here.

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

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