Dataset/cube_net_dataset_3000/README.md

GRIP Cube Net Dataset 3000 — v2

This release contains 3,000 dark-theme diagrams covering all 11 valid cube nets and 15,000 questions under GRIP's unified five-level rubric. Version cube-net-2.0.0 repairs a reference-frame defect in the previous Level 4 labels.

Reference frames and the v2 correction

Two relations are stored explicitly and must not be mixed:

• net_edge_pairs / net_edge_neighbors use the flat-net frame and contain only squares sharing a drawn fold edge.
• opposite_pairs, cube_adjacent_pairs, and cube_adjacent_faces use the folded-cube frame. A folded face is adjacent to every face except itself and its unique opposite.

The previous build stored folded opposition correctly but used flat-net edge neighbors when answering some folded-cube Level 4 questions. Independent recomputation found 452 wrong old Level 4 answers. v2 derives folded adjacency as the complement of opposition and removes the impossible neither option. Level 4 is exactly balanced: 1,500 adjacent and 1,500 opposite.

Level 2 remains intentionally flat-net based. Its valid_answers array contains every face sharing a drawn fold edge with the queried face; the private answer_key.csv preserves both this accepted list and answer_format=letter_any_of_list.

Five questions

1. Count the six visible face squares.
2. Name any valid flat-net fold-edge neighbor.
3. Recover a flat-net degree or a folded opposite face.
4. Classify a folded face pair as adjacent or opposite.
5. Determine whether swapping two face labels preserves a face's original opposite.

Validation

validate_cube_net_dataset.py independently propagates 3D face frames through every net, reconstructs flat and folded relations, re-derives all five answers, verifies every Level 2 accepted list, and checks all six labeled face regions and layouts in every PNG. It reports full distributions, per-level constant baselines, a complete stored-feature leak audit, reference-frame mappings, and violating/boundary guard injections. The final v2 build passes 3,000/3,000 images and 15,000/15,000 questions with zero mismatches.

The previous metadata and reports are preserved under archive/v1/. Cube-net pixels did not require a style change; backgrounds, dimensions, palette, and line-art renderer remain unchanged.

Commands

python generate_cube_net_dataset.py --n 3000 --output-dir .
python flatten_annotations.py annotations.jsonl
python validate_cube_net_dataset.py .

Model-facing evaluation must use question_set.csv, which contains only question_id, task, image, and prompt. annotations.jsonl, answer_key.csv, and dataset_final.* contain ground truth.

Supplementary open-ended questions

Version cube-net-5.0.0 replaces the supplementary free-response set with the exact domain template in OPEN_QUESTION_SPEC.md. It includes 3000 eligible items and excludes 0 under {}. Every prompt uses visible evidence, asks for justification, and ends with a confidence score from 0 to 1.

• open_questions.csv is public and contains exactly question_id,image,prompt.
• open_answer_key.csv is answer-key-side and contains separate partial-credit fields, an exhaustive acceptance_set, deterministic targets, and machine-readable tolerances for every numeric field.
• open_annotations.jsonl is answer-key-side and adds the complete derivation and scoring declaration.
• open_validation_metrics.json contains full target and answer distributions, constant-answer baselines, prompt/schema checks, independent metadata derivation results, and exhaustive PNG recovery results.

Scored fields at or above 60% after template revision: {}. Targeted fields: flat_edge_neighbours, opposite_face. Do not provide open_answer_key.csv, open_annotations.jsonl, or the closed-set annotations.jsonl to a model under evaluation because they expose answer-side scene metadata.