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PlotGround: Grounding Plot Digitization in Real Scientific Figures and Their Source Data — arXiv:2610.06825

Source: arXiv
Original: https://arxiv.org/abs/2610.06825
License (as archived): CC0-1.0
Archived: 2026-10-06T05:28:41.462Z
SHA-256 of the archived text: 86221fd22cf6aea6846bf4cb42bcec627477a7ba2897d63fb9ae3405006d60b3
Stable URL of this page: https://metraipolis.com/documento/dc_e259441eb040

How to cite

PlotGround: Grounding Plot Digitization in Real Scientific Figures and Their Source Data — arXiv:2610.06825 — arXiv, https://arxiv.org/abs/2610.06825 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e259441eb040 (Metraipolis archive dc_e259441eb040, sha256 86221fd22cf6aea6846bf4cb42bcec627477a7ba2897d63fb9ae3405006d60b3)

BibTeX

@misc{dce259441eb040,
  title = {PlotGround: Grounding Plot Digitization in Real Scientific Figures and Their Source Data — arXiv:2610.06825},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.06825},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e259441eb040; sha256 86221fd22cf6aea6846bf4cb42bcec627477a7ba2897d63fb9ae3405006d60b3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.06825. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Scientific figures often encode quantitative results that are not readily available in machine-readable form, making accurate plot digitization important for verifying and reusing published findings. Yet it remains unclear how accurately current models recover plotted values from real scientific figures, as existing benchmarks rely largely on synthetic charts or cover only a limited range of chart types. We introduce PlotGround, an automated pipeline for building plot digitization benchmarks from real scientific figures and their author-released source data. PlotGround maps figures to source tables, identifies reconstructable panels, and generates quantitative questions with source-grounded reference values. We use PlotGround to construct PlotGround-1k, a human-verified benchmark of 1,119 questions from 1,066 bioRxiv preprints. Across sixteen multimodal models, the best reaches 87.5% accuracy at a $\pm 5\%$ relative-error tolerance. Tightening the tolerance to $\pm 2\%$ lowers every model's accuracy by 11-24 percentage points, revealing a gap between approximate visual reading and precise quantitative recovery. PlotGround's paired figure-source structure lets us compare how accurately the same values are recovered from figures and from source tables. Providing source tables instead of figures raises a coding agent's accuracy from 90.0% to 97.4% while cutting cost by 72%.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2610.06825 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T05:24:20.397Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).