---
pretty_name: Curriculum Contamination Coherent-Wrong-Label Tasks (Stage 1)
language:
- en
task_categories:
- question-answering
- text-generation
tags:
- curriculum-learning
- self-training
- mathematical-reasoning
- synthetic-data
size_categories:
- n<1K
configs:
- config_name: unique_tasks
  data_files:
  - split: train
    path: data/unique_tasks.jsonl
- config_name: label_twins
  data_files:
  - split: train
    path: data/label_twins.jsonl
---

# Curriculum Contamination Coherent-Wrong-Label Tasks (Stage 1)

This private pre-release contains a controlled mathematical task bank for
studying whether self-evolving curriculum selectors can admit an objectively
wrong answer when repeated solver samples form a coherent majority.

## Dataset contents

- `data/unique_tasks.jsonl`: 240 unique, exact-answer mathematical tasks.
- `data/label_twins.jsonl`: 480 surface-identical task/reference pairs: one
  objectively correct reference and one plausible but objectively incorrect
  reference for each task.
- `metadata/validation_report.json`: construction and validation summary.
- `metadata/*.integrity.json`: line-count and SHA-256 integrity records.

The 240 tasks cover ten families, with 24 tasks per family:

1. area scaling;
2. at-least-one probability;
3. Bayesian positive-test reasoning;
4. combined work rates;
5. equal-distance average speed;
6. recurrence indexing;
7. repeated-object permutations;
8. successive percentage change;
9. weighted means;
10. probability without replacement.

## Important interpretation boundary

The plausible wrong answer encodes a prespecified candidate shortcut. It is not
claimed to be a model error until an evaluator run independently observes it.
Fields such as `candidate_shortcut_not_observed_model_behavior` and
`selector_observation_status: not_yet_run` preserve this distinction.

This Stage 1 release contains no completed selector outcome, natural-prevalence
estimate, training-effect estimate, or closed-loop contamination result.

## Construction and validation

- All 240 tasks have a unique exact answer under an independent deterministic
  verifier.
- The plausible wrong answer differs from the objective answer for every task.
- All 240 correct/wrong pairs have identical question text.
- Thirty tasks were included in a blinded machine audit during construction.
- The persisted validation report records 240/240 independent oracle passes and
  no construction errors.

## Schema overview

`unique_tasks.jsonl` includes the question, family, parameters, objective answer,
plausible wrong answer, misconception identifier and verifier certificate.

`label_twins.jsonl` includes the shared question and one proposed reference per
row, with `condition` and `reference_correctness` distinguishing the pair.

## Intended uses

- diagnostic evaluation of majority-vote pseudo-labeling;
- controlled curriculum-admission studies;
- comparison of selector risk/retention frontiers;
- paired causal studies of correct versus coherent-wrong training updates.

## Limitations

- The pool is controlled and deliberately enriched for candidate shortcuts; it
  does not estimate their prevalence in naturally generated curricula.
- It currently covers exact-answer English mathematical tasks only.
- Candidate wrong answers are mechanism probes, not labels of observed model
  behavior.
- Downstream learning harm must be established by a separate intervention.

## License and citation

No public-use license or paper citation is assigned in this private pre-release.
They will be added only after an explicit owner decision and double-blind release
review.