README.md

metadata

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

PixelProof Evaluation

The frozen evaluation sets for the PixelProof paper. Each eligible replay-verified world contributes exactly five deterministic, pixel-distinct instances. The five instances of a world share its selected question text but show different rendered scenes; the answer can differ between instances, and the world's inverse program recovers it from the image alone.

Config	Experiment	Worlds	Instances	Documented exclusions
section1_eval5	Profile-steered generation	1,298	6,490	3
section2_eval5	Spatially-steered generation	874	4,370	1
combined_eval5	Both experiments	2,172	10,860	4

The config names, the section column, and the ledgers/section* files are stable identifiers: section1 holds profile-steered worlds and section2 holds spatially-steered worlds. The exclusions are the worlds that passed the 200-scene replay (1,301 profile-steered and 875 spatially-steered) but produced fewer than five pixel-distinct scenes; each one is documented in the corresponding ledgers/section*_exclusions.csv file.

Each row exposes a native image, question, answer, answer_options, a stable evaluation_id, instance_rank, an image hash, coding-agent provenance, the discovery profile, and verification metadata. Spatially-steered rows also record their spatial pattern (pixel_support_scale, pixel_support_shape, and pixel_diversity_cell), one of nine patterns that combine a scale (focal, regional, distributed) with a shape (compact, pathlike, multipart). These columns are empty for profile-steered rows.

Keeping answers out of prompts

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 answer-free and answer-only CSV ledgers under ledgers/, whose source hashes are frozen in manifests/source-sections.json.

from datasets import load_dataset

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

Release integrity

This release preserves the questions, answers, images, and dataset configurations used in the paper. Local filesystem paths in audit diagnostics are redacted, and CHECKSUMS.sha256 lists SHA-256 checksums for the files distributed here.

Project resources

• PixelProof website
• Source code