rishanthrajendhran/cc2026-deleaked-outlines-1m

CC-2026 De-leaked Outlines (1M)

1,001,145 English documents paired with role-labelled outlines for idea-level AI-text detection. Each row holds the source document, an outline extracted from it, and a de-leaked outline in which surface style has been paraphrased away so only the ideas and their structural roles remain. The intended use is detecting AI authorship from what a document says and how it is organised, rather than from its prose style.

The source label is the Pangram prediction. Documents were selected so that the intended label matches the detector's call, so source and pangram_prediction agree on 100.00% of rows. A model trained here is learning to reproduce Pangram's judgement, not a human-annotated ground truth.

Splits

The split column carries the assignment; all rows ship in one physical train config.

split	rows	% human	purpose
train	842,301	44.03%	model fitting
val	29,975	83.35%	checkpoint selection
calibration	80,000	100.00%	per-format thresholds
test	48,869	50.29%	held-out evaluation

calibration is humans only and disjoint from val, so a threshold is never read off the documents used to pick the checkpoint. It holds 10,000 humans per format, putting 50 documents below a 0.5% quantile — a realised-FPR 95% interval of 0.371%–0.647%. Per-format thresholds are required: the 1%-FPR cut varies ~16× across formats, so a single global threshold realises anywhere from 0.16% to 1.66% FPR depending on format.

test is balanced by format (49.5–52.0% human) and by length (49.1–52.6% human). val is deliberately human-heavy; use AUC or balanced metrics on it, not raw accuracy.

Schema

Identity and labels

column	type	notes
id	string	unique document id
source	string	the label — human or ai. AI is the positive class. Named source for continuity with the 391k release
split	string	train / val / calibration / test

Document metadata

column	type	notes
format	string	8 values: Nonfiction Writing, Knowledge Article, Personal Blog, News Article, Academic Writing, User Reviews, Personal About Page, Creative Writing
role_format	string	role taxonomy used when labelling outline items; same 8 values as format
topic	string	23 values (Finance & Business, Health, Sports & Fitness, …)
url	string	origin URL
date	string	source date
word_count	int64	501–17,809. The corpus floor is 501 words
token_count	int64	529–132,083

Outlines

extracted_outline (stage 1) and deleaked_outline (stage 2) share one struct:

struct<
  document_description: string,
  global_themes:        list<string>,
  items: list<struct<
    id:        int64,
    content:   string,
    verbatim:  bool,     # content lifted from the document rather than abstracted
    role_name: string    # the item's structural role
  >>
>

source_text (string) holds the full original document.

column	type	notes
extractor	string	stage-1 model. gemini-3.7-flash for the 613,132 newly extracted rows; Gemini 3.7 Flash (6-shot) and Gemini 3.1 Pro (6-shot) for the 388,013 carried over. The corpus is deliberately mixed-extractor — the differing labels are meaningful and are what an extractor-invariance test keys on
paraphraser	string	stage-2 model: gemini-3.1-pro-preview (new) or Gemini 3.1 Pro (carried over)

Pangram detector scores

The two Pangram generations populate different fields, so these nulls are structural rather than missing data:

column	type	populated for
pangram_model	string	all rows — pangram-3 (603,132) or pangram-4 (398,013)
pangram_version	string	10,000 rows
pangram_prediction	string	all rows — AI / Human
pangram_score	double	pangram-3 rows only
pangram_confidence	double	pangram-3 rows only
pangram_fraction_ai	double	pangram-4 rows only
pangram_fraction_ai_assisted	double	pangram-4 rows only
pangram_fraction_human	double	pangram-4 rows only
pangram4_prediction	string	pangram-4 rows only — kept so consumers of the 391k release read exactly what they read before
pangram4_fraction_ai	double	pangram-4 rows only
pangram4_fraction_ai_assisted	double	pangram-4 rows only
pangram4_fraction_human	double	pangram-4 rows only
editlens_bucket	int64	5.8% — editlens-derived rows only
editlens_score	double	5.8% — editlens-derived rows only

pangram_* are the generic columns carrying every row's score whatever produced it; pangram4_* are the original narrow columns. editlens_* are a property of one source, not of the document, so they are null elsewhere rather than imputed.

Provenance

Outlines were produced in two stages: extraction with gemini-3.7-flash (6-shot, thinking level HIGH) and de-leaking with gemini-3.1-pro-preview (HIGH). 613,132 rows were newly extracted for this release; 388,013 are carried over from the earlier 391k release with their original outlines.

Known limitations

• Labels are detector-derived, not human-annotated (see above).
• The corpus is mixed-extractor by design; models are expected to be invariant to it.
• Creative Writing and Personal About Page are human-majority because their AI-side pools were exhausted — no allocation could balance them further.
• Format carries a small amount of label information: a format-only classifier beats the majority baseline by 0.8 points (mutual information 0.0038 bits, 0.38% of label entropy). Length carries 0.0000 bits and topic 0.0011 bits.