Datasets:

taesiri
/

BlindLoop-Evaluation

configs:
  - config_name: combined_eval5
    default: true
    data_files:
      - split: test
        path:
          - parquet/section1/test-*.parquet
          - parquet/section2/test-*.parquet
  - config_name: section1_eval5
    data_files:
      - split: test
        path: parquet/section1/test-*.parquet
  - config_name: section2_eval5
    data_files:
      - split: test
        path: parquet/section2/test-*.parquet
task_categories:
  - visual-question-answering
tags:
  - synthetic
  - visual-question-answering
  - evaluation
  - parquet

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"])