README.md

metadata

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.