A new benchmark called SciFigure2Code has been introduced to evaluate AI's ability to reconstruct scientific figures into editable code. The benchmark focuses on recovering the hidden provenance of figures, such as the data and plotting code, from their visual representation. Role-specialized Codex agents were used to generate and refine Python programs that capture the arrangement, hierarchy, encodings, and typography of scientific panels. While current zero-shot models show weak performance in execution and structural fidelity, Claude Opus 4.7 and Claude Opus 4.6 demonstrated leading capabilities in image-only and caption-assisted reconstruction, respectively. AI
IMPACT This benchmark could drive improvements in AI's ability to interpret and reproduce scientific visualizations, potentially accelerating research reproducibility.
RANK_REASON The item describes a new benchmark and associated research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX Code Finder for Papers
- Claude Opus 4.6
- Claude Opus 4.7
- .codex
- DagsHub
- Gotit.pub
- Hong Yi
- Hugging Face
- Python
- ScienceCast
- SciFigure2Code
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