benchmark/
| file | rows | what |
|---|---|---|
inputs.jsonl |
604 | the exact model input for every arm, keyed by id |
golden.jsonl |
604 | the real 3D structure and the expected model output, keyed by id |
images/<id>/ |
11,634 PNGs | every image referenced by inputs.jsonl |
inputs.jsonl
id, n, k, level, held_out
arms: {
arm1: {arm_name: "text", prompt_text, images: []}
arm2: {arm_name: "multiple images", prompt_text, images: [6 faces + 2 renders per piece]}
arm3: {arm_name: "text + multiple images", prompt_text, images: [same as arm2]}
arm4: {arm_name: "single image", prompt_text, images: [merged sheet]}
}
prompt_text is byte-identical to what was sent (prompt_sha is included for
verification). Images are sent in the listed order; paths are relative to
benchmark/. The held-out face image is the PREDICT THIS placeholder, never the
real face — this folder contains no answer-revealing images.
golden.jsonl
| field | |
|---|---|
structure_3d |
the real internal structure, an n×n×n array indexed [z][y][x], z = 0 at the bottom. Each cell is the letter of the piece occupying it, or "." for empty. |
pieces |
the same structure per piece: {letter: [[x, y, z], ...]} in block coordinates |
held_out |
which face is withheld (+X -X +Y -Y +Z -Z); the camera sits on that side and looks inward |
answer_grid |
the withheld face: list of n strings, top row first, leftmost character first. Letter = first piece the ray meets, "." = see-through. |
expected_output |
the output we expect from the model, i.e. the JSON object the prompt asks for. The prompt also asks for a free-text "reason" key, which is recorded but not scored. Scoring is an exact match on answer. |
majority_baseline, opposite_baseline |
cell accuracy from always guessing the most common letter, and from mirroring the opposite face |
Screen axes of each face (right, up): +X(+Y,+Z) -X(-Y,+Z) +Y(-X,+Z)
-Y(+X,+Z) +Z(+X,+Y) -Z(-X,+Y). Ray-casting structure_3d from held_out
with these axes reproduces answer_grid exactly. This was checked on all 604 rows.