arXiv AI

Vector-Bench: Can Models Surgically Edit SVG Code?

arXiv:2607. 19056v1 Announce Type: new Abstract: Instruction-based vector editing requires two capabilities: making a requested change and leaving everything else alone.

Hugging Face Trending Papers

Aug 11

VisEditBench: Can Vision-Language Models Edit Visualization Code from Multimodal Feedback?

Vision-language models (VLMs) have shown strong capabilities in generating visualization code from textual or visual specifications. However, real-world visualization authoring is inherently iterative: users frequently revise existing visualizations to repair flawed charts or adapt them to desired styles.

llms nlp multimodal benchmarks

arXiv AI

1d ago

VFIG: Vectorizing Complex Figures in SVG with Vision-Language Models

arXiv:2603. 24575v2 Announce Type: replace-cross Abstract: Scalable Vector Graphics (SVG) are essential for technical illustration and digital design, offering resolution independence and semantic editability.

By Qijia He, Xunmei Liu, Hammaad Memon, Ziang Li, Zixian Ma, Jaemin Cho, Zhongzheng Ren, Daniel S Weld, Ranjay Krishna

llms rag reinforcement-learning fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 2

PaintBench: Deterministic Evaluation of Precise Visual Editing

arXiv:2606. 00188v1 Announce Type: cross Abstract: While current multimodal models are proficient at open-ended visual editing, executing precise single-answer edits remains an important obstacle.

By Kai Xu, Ellis Brown, Shrikar Madhu, Rob Fergus, He He, Saining Xie

robotics multimodal benchmarks safety

arXiv AI

Jun 16

Imperfect Visual Verification for Code Edition : A Case Study on TikZ

arXiv:2606. 15693v1 Announce Type: cross Abstract: LLMs have significantly advanced code generation, enabling the synthesis of functional programs.

By Charly Reux (UR, INSA Rennes, DiverSe), Mathieu Acher (CNRS, IUF, IRISA, UR, DiverSe), Djamel Eddine Khelladi (DiverSe, UR, CNRS, IRISA), Cl\'ement Quinton (SPIRALS, CNRS), Olivier Barais (UR, IRISA, DiverSe)

llms benchmarks

arXiv AI

Jul 22

One Rewrite to Fix Them All? Type-Aware Repair Allocation for Text-to-Image Prompt Optimization

arXiv:2607. 18724v1 Announce Type: new Abstract: Text-to-image (T2I) generators often fail to follow their prompts faithfully, producing wrong counts, swapped attributes, ambiguous relations, and illegible text.

By Haoyue Liu, Xiaoyu Ma, Ye Chen, Shuguang Cui, Xiaoying Tang

diffusion benchmarks

arXiv AI

Jun 2

CV-Arena: An Open Benchmark for Instructional Computer Vision Problem Solving with Human-AI Collaborative Preferences

arXiv:2606. 00931v1 Announce Type: cross Abstract: Instruction-guided image editing is becoming a general interface for visual work, yet existing benchmarks still focus largely on narrow appearance edits and do not fully capture the diversity of real-image tasks in professional workflows.

By Fangzhou Lin, Peiran Li, Lingyu Xu, Wenjing Chen, Qianwen Ge, Shuo Xing, Mingyang Wu, Xiangbo Gao, Siyuan Yang, Kazunori Yamada, Ziming Zhang, Haichong Zhang, Zhen Dong, Ming-Hsuan Yang, Zhengzhong Tu

agents computer-vision multimodal benchmarks

arXiv AI

Jul 14

3D-DefectBench: A Controlled Factorial Study of Vision-Language Model Evaluation Pipelines for Fine-Grained 3D Generation Defects

arXiv:2607. 10826v1 Announce Type: cross Abstract: Automated evaluation is essential for scaling generative 3D systems, where exhaustive human review is costly and slow.

By Zhenyu Zhao, Nanshan Jia, Jihyeon Je, Yifu Tang, Alvin Chan, Michael Spedden, Michael V. Palleschi, Sui Huang, Jingshen Wang, Zeyu Zheng

llms multimodal benchmarks

arXiv AI

Jun 2

TECCI: Tricky Edits of Collected and Curated Images

arXiv:2606. 01213v1 Announce Type: cross Abstract: Despite tremendous recent progress, current text-guided image editing methods still struggle with many aspects of editing involving instruction following, minimally editing the source image, and ensuring high visual quality.

By Aishwarya Agrawal, Roy Hirsch, Yasumasa Onoe, Sherry Ben, Jason Baldridge

llms benchmarks

Hugging Face Trending Papers

Jul 12

3D-DefectBench: A Controlled Factorial Study of Vision-Language Model Evaluation Pipelines for Fine-Grained 3D Generation Defects

Automated evaluation is essential for scaling generative 3D systems, where exhaustive human review is costly and slow. However, the reliability of an automated judge depends on the entire evaluation pipeline, not only the underlying vision-language model (VLM), but also how assets are rendered, what visual evidence is provided, how the task is specified, and how human reference labels are constructed.

llms multimodal benchmarks

arXiv AI

Jul 7

Evaluating and Understanding Model Editing for Medical Vision Language Models

arXiv:2607. 05310v1 Announce Type: new Abstract: Model editing promises a fast, targeted way to correct post-deployment mistakes in medical vision-language models (VLMs) without costly retraining.

By Guli Zhu, Chenwei Wu, Liyue Shen

llms diffusion multimodal benchmarks

arXiv AI

Jul 3

ContraFix: Skill-Enhanced Contrastive Runtime Analysis for Vulnerability Repair

arXiv:2605. 17450v2 Announce Type: replace-cross Abstract: As software systems grow increasingly complex, automated vulnerability repair (AVR) remains difficult because the materials available to a repair system are usually failure artifacts rather than repair guidance.

By Simiao Liu, Fang Liu, Peiding Wang, Taichuan Li, Yinghao Zhu, Xiaoli Lian, Li Zhang

llms agents benchmarks

arXiv AI

Jul 3

PPTArena: A Benchmark for PowerPoint Editing

arXiv:2512. 03042v3 Announce Type: replace-cross Abstract: We introduce PPTArena, a benchmark for PowerPoint editing that evaluates how agents modify real slides from natural-language instructions.

By Michael Ofengenden, Yunze Man, Ziqi Pang, Liang-Yan Gui, Yu-Xiong Wang

llms agents multimodal benchmarks

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