arXiv (All)
open access
EdiTikZ: Scientific Figure Editing from Revision Trajectories
Vision-language models (VLMs) have shown strong performance in generating scientific figures from text or images. However, producing publication-ready figures requires iterative refinement, making scientific figure editing an important yet largely unexplored task. Existing approaches rely on costly
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- EdiTikZ: Scientific Figure Editing from Revision Trajectories#627733
- Drag as Evidence: Motion-Grounded Latent Recomposition for Drag-Based Editing#1131956
- MixDetect: Word-Level Localization and Quantification of AI Editing#1116956
- AutoFigure-Edit: Generating Editable Scientific Illustration#130773
- Beyond Temporal Smoothing: Spatial Energy Budgets Stabilize One-Step Diffusion Editing#1117172
Record · ID 821344
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive