croissant.json

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  "name": "apple-pi",
  "description": "Apple-Pi is an evaluation subset of a larger physics benchmark for evaluating physics reasoning in image and video generation models. Each case provides a video clip (or rendered frames) with ground-truth annotations of physical parameters, instance segmentation, and per-frame velocity. Released for double-blind submission.",
  "conformsTo": "http://mlcommons.org/croissant/1.1",
  "license": "https://creativecommons.org/licenses/by-sa/4.0/",
  "url": "https://huggingface.co/datasets/AnonymousSubmitt/apple-pi",
  "version": "1.0.0",
  "datePublished": "2026-05-06",
  "citeAs": "@misc{anonymous2026applepi, title={Apple-Pi: Physics Infographics Benchmark}, author={Anonymous}, year={2026}}",
  "creator": {
    "@type": "Organization",
    "name": "Anonymous"
  },
  "keywords": [
    "physics reasoning",
    "video generation",
    "image generation",
    "benchmark",
    "evaluation"
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  "rai:dataLimitations": "This subset is intended for evaluation only, not training. Synthetic cases are generated with NVIDIA Isaac Sim (rigid-body dynamics) and do not capture deformation or fluid dynamics. Realworld cases are short controlled clips with limited environmental diversity. Not recommended for safety-critical or clinical applications.",
  "rai:dataBiases": "Task distribution is weighted toward common phenomena (projectile, freefall) and may under-represent rarer tasks (composition, multi-body collision). Synthetic cases use a fixed object/material library; realworld cases are recorded under controlled lighting. No demographic bias because no humans are present.",
  "rai:personalSensitiveInformation": "None. Synthetic cases contain no people; realworld videos do not show identifiable individuals.",
  "rai:dataUseCases": "Evaluating physics reasoning in image- and video-generation models across perception, comprehension, and generation tracks. Validated for zero-shot benchmark evaluation. Not validated for training, clinical decision-making, or robotic control.",
  "rai:dataSocialImpact": "Positive: provides a controlled benchmark to measure physics understanding gaps in current generation models. Risk: scores may be over-interpreted as general physics reasoning ability. Mitigation: released under CC BY-SA 4.0 with explicit research-evaluation framing; contains no humans or sensitive content.",
  "rai:hasSyntheticData": true,
  "prov:wasDerivedFrom": [
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      "@type": "prov:Entity",
      "prov:label": "Original recordings (no upstream source dataset)",
      "sc:description": "This dataset is not derived from any external public dataset. Synthetic cases are originally generated using NVIDIA Isaac Sim (a 3D rigid-body physics simulator). Realworld cases are originally recorded video clips collected under controlled laboratory conditions."
    }
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  "prov:wasGeneratedBy": [
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      "prov:type": {
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      "prov:label": "Synthetic data generation",
      "sc:description": "Simulation cases generated with NVIDIA Isaac Sim (rigid-body 3D physics simulator built on PhysX). For each scene we specified physical parameters (gravity, materials, initial velocities, friction, restitution), ran deterministic simulation, and rendered video frames at 24 fps along with per-frame instance segmentation, depth, mask, velocity, and density buffers. Ground-truth labels are computed directly from physics-engine state.",
      "prov:atTime": "2025/2026",
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      "prov:atTime": "2025/2026",
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      "prov:atTime": "2025/2026",
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      "sha256": "https://github.com/mlcommons/croissant/issues/80"
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