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Qualitative export: data-defect check

This is a report-only audit. No dataset, result, prompt, answer key, or image was modified.

L4/L5 exact-text comparison

The table compares the full stored L4 and L5 prompt text for every image in each domain's question_set.csv.

Domain L4/L5 identical? Affected images Images compared
angle_estimation no 0 3,000
clock_reading no 0 3,000
combination3d no 0 3,000
combination no 0 3,000
compass_bearing no 0 3,000
coordinate_geometry no 0 3,000
cube_net no 0 3,000
cube_structure no 0 3,000
depth_height no 0 3,000
embedded_figures no 0 3,000
fbd no 0 3,000
fold_punch no 0 3,000
gauge_reading no 0 3,000
gear_train no 0 3,000
hex_pathfinding no 0 3,000
impossible_object no 0 3,000
laser_mirror no 0 3,000
line_intersection no 0 3,000
nested_hexagons no 0 3,000
nested_squares no 0 3,000
nested_triangles no 0 3,000
occluded_pattern no 0 3,000
optical_illusion no 0 3,000
orthographic no 0 3,000
overlap_circles no 0 3,000
physical_stability no 0 3,000
polyhedron no 0 3,000
projectile_motion no 0 1,000
rotation_matching no 0 3,000
route no 0 3,000
rpm no 0 3,000
shadow_inference yes 627 3,000
surface_topology no 0 3,000
symmetry_pattern no 0 3,000

Physical-stability L5 ground truth and strict scoring

  • Full-corpus L5 ground truths classified as sentence-valued (>5 whitespace-delimited words): 3,000/3,000 (100.00%).
  • Strictly correct stored Test-1 L5 responses across evaluated models: 0/500 (0.00%).
  • The response fraction uses the existing strict comparator and the stored Test-1 evaluation sample; no semantic regrading was applied.
  • Sentence-valued keys can penalize semantically compatible paraphrases under exact comparison; this audit reports the exposure without changing scores.