taesiri/BlindLoop-Evaluation

BlindLoop Evaluation

The frozen evaluation cohorts for the BlindLoop paper. Each eligible generated task contributes exactly five deterministic, pixel-distinct image instances. The five rows share a task's selected question/prompt family while varying the rendered scene and gold answer as determined by the task's pixel oracle.

Config	Tasks	Rows	Documented exclusions
section1_eval5	1,298	6,490	3
section2_eval5	874	4,370	1
combined_eval5	2,172	10,860	4

Each row exposes a native image, question, answer, answer_options, stable evaluation_id, instance_rank, image hash, generator provenance, profile, and verification metadata. Section 2 rows additionally expose their pixel-diversity cell; those columns are empty for Section 1.

Gold-label safety

The dataset includes answer for scoring. A model request must use only image, question, and answer_options; do not serialize the full row into the prompt. For stricter orchestration, use the gold-free and gold-only CSV ledgers under ledgers/, whose source hashes are frozen in manifests/source-sections.json.

from datasets import load_dataset

ds = load_dataset("taesiri/BlindLoop-Evaluation", "section1_eval5", split="test")
row = ds[0]
row["image"].show()
print(row["question"], row["answer_options"], row["answer"])