Datasets:
NMR-Bench test data
manifest.json identifies each task's annotation file, sample count, and annotation-file SHA-256. Each test.jsonl record contains:
| Field | Meaning |
|---|---|
sample_id |
Stable benchmark identifier |
image |
Image path relative to the task directory |
image_sha256 |
SHA-256 of the supplied PNG |
question |
Exact model input question |
answer |
Canonical target answer, used only by the scorer |
difficulty |
easy, medium, or hard |
task_form |
Counting question type, when applicable |
endpoint_type |
Path endpoint-marker category, when applicable |
The corresponding metadata.jsonl in each task directory exposes the same records to the Hugging Face image viewer. It uses file_name instead of image to associate each row with a PNG under images/; test.jsonl remains the evaluator's canonical annotation file.
Burden Counting has 420 images: seven forms × three difficulty levels × 20 images. Path Tracing has 360 images: three difficulty levels × 120 images, balanced across letters, numbers, icons, and shapes.
| Counting form | Question | Boxed answer |
|---|---|---|
| T1a | Category count | Integer |
| T2a | Category and size | Integer |
| T2b | Category and tilt | Integer |
| T2c | Category and occlusion | Integer |
| T2d | Category, size, and tilt | Integer |
| T3a | Compare size-group counts | Group label, count difference |
| T3b | Most frequent listed category | Category label, count |
Path answers list unordered endpoint pairs, for example \boxed{(A, G), (B, M), (L, N)}. Labels are normalized for case, numeric formatting, and backslash escapes.
The last boxed answer is used. Counting also supports the scorer's reasoning-channel fallback when a model provides reasoning separately. In the single tied T3a case, the stored canonical answer is small, 0, applied consistently to all models.
The model adapter receives only the image and question. Answers are reserved for scoring. Do not use target answers to construct prompts or predictions.