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EdiTikZ: Scientific Figure Editing from Revision Trajectories

Paper recorded by Signals 4 on 2026-09-01 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-01 on arXiv · recorded by Signals 4 on 2026-09-02

Category: cs.CV · 计算机视觉 · first seen 2026-09-02

Abstract

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 proprietary agentic systems, focus primarily on evaluation, or construct training supervision from s

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#191 most recent of 237 cs.CV papers we have recorded · ↑ newer: MegaStyle++: Scaling Image Style Space through Hierarchical Style Defi · ↓ older: Neuro-Symbolic Geometric Abstraction (NeuSOGA): From Observations to S
Cite this page: EdiTikZ: Scientific Figure Editing from Revision Trajectories: the #191 most recent of 237 cs.CV papers we have recorded (as of 2026-09-01). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/editikz-scientific-figure-editing-from-revision-trajectories.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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