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Verification Before Assembly Dataset

Companion dataset for the paper
Verification Before Assembly: A Low-Cost Verifier for Generated Scientific Video Frames
accepted at the NeurIPS 2026 Workshop on AI for Meta‑Science: Scaling and Organizing Science in the Age of AI.

Contents

Path Description
figures/ Scientific figure PNGs + JSON sidecars (golden, ml_*, juri_*, bt_l2_fail)
router/ Image-domain labels for the ConvNeXt router (byt / video / graph / scheme)
metadata/figures_index.jsonl Flat index with sample_id, label, paths, caption, and lie annotation

See also:

Figure splits

  • golden — held-out evaluation (never used for training or threshold search)
  • ml_orig / ml_l2_fail, juri_orig / juri_l2_fail — paired match / mismatch training figures
  • bt_l2_fail — biotechnology mismatches (no paired originals)

Labels:

  • match — caption and image are consistent (gold.is_corrupted = false)
  • mismatch — locally edited failure (gold.is_corrupted = true)

Indexed figure rows: 985 (golden = 294, train = 691).

Download

from huggingface_hub import hf_hub_download, snapshot_download

root = snapshot_download(
    repo_id="Saintghetto17/verification-before-assembly",
    repo_type="dataset",
)

index = hf_hub_download(
    repo_id="Saintghetto17/verification-before-assembly",
    repo_type="dataset",
    filename="metadata/figures_index.jsonl",
)

In the companion code repository:

python scripts/download_data.py
# installs into data/figures/ and data_router/

Expected local layout:

data/figures/   ← contents of figures/
data_router/    ← contents of router/

Citation

@inproceedings{verification_before_assembly_2026,
  title     = {Verification Before Assembly: A Low-Cost Verifier for Generated Scientific Video Frames},
  author    = {Gromov, Egor},
  booktitle = {NeurIPS 2026 Workshop on AI for Meta-Science: Scaling and Organizing Science in the Age of AI},
  year      = {2026},
  url       = {https://openreview.net/forum?id=cNyEkRsJgC}
}
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