Datasets:
BlindLoop Generations
A flat, general-purpose visual-question-answering dataset generated by the
BlindLoop paper experiments. Each row is one concrete question instance with a
native Hugging Face Image value, question, gold answer, answer choices, and
fully filterable generation provenance.
| Config | Rows | Tasks | Unique source images |
|---|---|---|---|
section1_all |
516,810 | 1,301 | 249,488 |
section2_all |
335,271 | 875 | 166,364 |
combined_all |
852,081 | 2,176 | 415,852 |
Main columns
image: embedded image bytes decoded by Hugging Face as an image.question,answer,answer_options: the VQA instance and gold target.profile,generator_model,generator_harness,reasoning_effort: generation treatment.campaign_id,candidate_id,record_id,example_id,prompt_family: exact provenance.margin,quarantined,mechanically_eligible,human_admission: verification and review state.pixel_support_scale,pixel_support_shape,pixel_diversity_cell: Section 2 pixel-diversity descriptors; empty for Section 1.image_sha256,sample_id: immutable media and row identifiers.
This archive includes all verified replay outputs, including explicitly flagged
quarantined rows for auditability. Mechanical verification is not a substitute
for human admission; human_admission preserves the source status.
from datasets import load_dataset
ds = load_dataset("taesiri/BlindLoop-Generations", "section1_all", split="train")
row = ds[0]
row["image"].show()
print(row["question"], row["answer"])
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