LevenKoko/MIRAGE-CanaryDocs

MIRAGE CanaryDocs

MIRAGE CanaryDocs is an English synthetic enterprise-document dataset for structured privacy-unit, canary, and ordered multi-chunk evaluation. It is the companion dataset for the EMNLP 2026 paper When Metadata Remembers: Ordered Provenance Enables Document-Level Embedding Inversion.

Project documentation and schemas are also available in the MIRAGE GitHub repository.

Dataset summary

The dataset contains complete synthetic documents, ordered token chunks, document-level Gold units, exact surface occurrences, occurrence-to-chunk alignments, and cross-location dependency relations. It is designed for controlled research on document reconstruction, structured privacy-unit recovery, canary recovery, ordered provenance, long-context retrieval, and parser evaluation.

All three splits intentionally publish their complete inputs and Gold annotations. The test split is an openly labeled reference split, not a hidden test set or evaluation-server split.

Split	Documents	Chunks	Gold units	Canaries	Occurrences
Shadow	20,000	108,953	275,920	32,000	308,385
Dev	100	635	1,442	198	1,608
Test	600	3,744	8,878	1,181	9,894
Total	20,700	113,332	286,240	33,379	319,887

The documents cover 24 scenario families across account, calendar, approval, location, finance, and release workflows. Shadow contains 12 families; Dev and Test contain all 24 families.

Loading the canonical layers

Each normalized entity layer is exposed as a separate Hugging Face configuration because the layers have different schemas. Every configuration provides shadow, dev, and test splits.

from datasets import load_dataset

documents = load_dataset("LevenKoko/MIRAGE-CanaryDocs", "documents")
units = load_dataset("LevenKoko/MIRAGE-CanaryDocs", "units")
chunks = load_dataset("LevenKoko/MIRAGE-CanaryDocs", "chunks")

print(documents["test"][0]["title"])
print(units["test"][0]["type"])
print(chunks["test"][0]["chunk_index"])

Available configurations:

Configuration	Entity	Total rows
documents	Complete canonical documents and document metadata	20,700
sections	Ordered document sections	103,500
paragraphs	Ordered section paragraphs	146,691
chunks	Ordered token windows and chunk text	113,332
units	Document-level Gold privacy units and canaries	286,240
occurrences	Exact surface occurrences with character/token spans	319,887
alignments	Occurrence-to-chunk overlap relations	324,290
dependencies	Repeated-unit relations across document locations	33,647

Repository layout

The normalized JSONL files under data/ are the canonical source of record. Other formats are materialized convenience representations of the same public release.

data/
  documents/{shadow,dev,test}.jsonl
  sections/{shadow,dev,test}.jsonl
  paragraphs/{shadow,dev,test}.jsonl
  chunks/{shadow,dev,test}.jsonl
  units/{shadow,dev,test}.jsonl
  occurrences/{shadow,dev,test}.jsonl
  alignments/{shadow,dev,test}.jsonl
  dependencies/{shadow,dev,test}.jsonl
views/
  bundled/{shadow,dev,test}.jsonl
  canaries/{shadow,dev,test}.jsonl
  benchmark/{dev,test}.parquet
database/canarydocs.sqlite
metadata/

• views/bundled/: one self-contained nested document per JSONL row.
• views/canaries/: canary-only units with occurrence and chunk mappings.
• views/benchmark/: nested Dev/Test Parquet views.
• database/canarydocs.sqlite: relational mirror containing only public release tables and views.
• metadata/: JSON Schemas, taxonomy, family definitions, statistics, split metadata, chunking semantics, file inventory, and SHA-256 checksums.

To load a bundled view directly:

from datasets import load_dataset

bundled_test = load_dataset(
    "json",
    data_files={
        "test": "https://huggingface.co/datasets/LevenKoko/MIRAGE-CanaryDocs/resolve/main/views/bundled/test.jsonl"
    },
)
print(bundled_test["test"][0]["gold_units"][0])

Data model

document -> section -> paragraph
document -> chunk
document -> unit -> occurrence <-> chunk
                         |
                         +-> dependency -> occurrence

• A unit is one document-level Gold value.
• An occurrence is one exact realization of a unit in document text.
• An alignment records whether a chunk fully contains or intersects an occurrence.
• A dependency links consecutive occurrences of a repeated unit and records whether the relation crosses chunk or non-overlapping chunk boundaries.

Stable identifiers include document_id, section_id, paragraph_id, chunk_id, unit_id, occurrence_id, alignment_id, and relation_id. Character and token spans are zero-based, half-open intervals.

Annotation taxonomy

Gold units use nine mutually exclusive types:

• entity_mention
• email_account
• date_time
• amount_numeric
• url_path_token
• identifier_case_id
• canary_string
• rare_domain_ngram
• other_non_anchorable

Canaries use five surface-format families: account, identifier, other, path, and URL. The 33,379 canary values are unique in this release.

Chunking

Chunk boundaries use a frozen intfloat/e5-large-v2 tokenizer configuration without special tokens, padding, or truncation. The window size is 128 tokens.

Split	Window	Stride	Overlap
Shadow	128	128	0
Dev	128	96	32
Test	128	96	32

Exact tokenizer metadata and hashes are recorded in metadata/chunking.json.

SQLite access

Download database/canarydocs.sqlite and query the relational mirror directly:

SELECT d.document_id, u.surface, c.chunk_index
FROM units u
JOIN occurrences o USING (unit_id)
JOIN alignments a USING (occurrence_id)
JOIN chunks c USING (chunk_id)
JOIN documents d USING (document_id)
WHERE u.is_canary = 1
ORDER BY d.document_id, c.chunk_index;

Integrity and release metadata

• Release version: 1.0.0
• Release date: 2026-08-30
• Canonical format: normalized JSONL
• Relational mirror: SQLite with foreign keys
• Per-file hashes: metadata/checksums.sha256
• Machine-readable file inventory: metadata/file_inventory.json
• JSON Schemas: metadata/schemas/

After downloading the complete repository, verify it with:

sha256sum --quiet -c metadata/checksums.sha256

Intended uses

• Structured privacy-unit and canary recovery evaluation.
• Ordered-context and document-level reconstruction analysis.
• Chunk/document alignment and long-context retrieval research.
• Synthetic benchmark development and parser validation.

Limitations

• The text is synthetic and may contain formulaic phrasing or intentionally unusual identifiers.
• The dataset should not be treated as evidence about real people, organizations, credentials, or operational systems.
• All hosted URLs and email domains use non-routable .invalid namespaces.
• Because all Gold annotations are public, reported Test results must be interpreted as evaluation on an open reference set rather than a blind benchmark.
• Canary strings are public benchmark markers, not secret production credentials or live leak probes.

Citation

If you use MIRAGE CanaryDocs, please cite the accompanying EMNLP 2026 paper:

@inproceedings{zheng2026metadata,
  title     = {When Metadata Remembers: Ordered Provenance Enables Document-Level Embedding Inversion},
  author    = {Zheng, Liwen and Li, Qing and Zou, Qingsong and Jiang, Yong},
  booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing},
  year      = {2026}
}

Structured citation metadata is provided in CITATION.cff.

License

MIRAGE CanaryDocs is released under the Creative Commons Attribution 4.0 International license (CC BY 4.0). See LICENSE for details.