SciDiagramEdit: Learning to Edit Scientific Diagrams from Paper Revisions
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TL;DR - SciDiagramEdit is a benchmark and "skill-evolution" framework that learns to edit scientific paper figures from natural-language instructions by mining real before/after figure revisions from arXiv version histories. It matters because it targets a routine, time-consuming research task—relabeling, rearranging, and restyling figures—using authors' own revision intent as a training signal.
- Benchmark from arXiv revisions: Mines before/after figure pairs from paper version histories, each grounded in authors' actual editing intent, rather than synthetic edits.
- Operates on editable vector source: Works on the figure's vector primitives (schematics, plots, captions, arrows), enabling users to inspect and co-edit individual elements alongside the agent.
- Agentic skill evolution: An agentic proposer iteratively refines the agent's skill specification from execution traces across multiple epochs, reportedly lifting edit accuracy on a held-out validation set.
- Note: The provided abstract states directional improvement but gives no concrete metrics, baselines, or dataset sizes—so exact performance can't be assessed from this content.
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SciDiagramEdit: Learning to Edit Scientific Diagrams from Paper Revisions
TL;DR - SciDiagramEdit is a benchmark and "skill-evolution" framework that learns to edit scientific paper figures from natural-language instructions by mining real before/after figure revisions from arXiv version histories. It matters because it targets a routine, time-consuming research task—relabeling, rearranging, and restyling figures—using authors' own revision intent as a training signal.
- Benchmark from arXiv revisions: Mines before/after figure pairs from paper version histories, each grounded in authors' actual editing intent, rather than synthetic edits.
- Operates on editable vector source: Works on the figure's vector primitives (schematics, plots, captions, arrows), enabling users to inspect and co-edit individual elements alongside the agent.
- Agentic skill evolution: An agentic proposer iteratively refines the agent's skill specification from execution traces across multiple epochs, reportedly lifting edit accuracy on a held-out validation set.
- Note: The provided abstract states directional improvement but gives no concrete metrics, baselines, or dataset sizes—so exact performance can't be assessed from this content.