arXiv AI

SketchVLM: Vision language models can annotate images to explain thoughts and guide users

arXiv AI

Jul 29

Visual prompt engineering for video models

arXiv:2607. 25537v1 Announce Type: cross Abstract: In the age of foundation models, a model is only as good as its prompt.

By Robert Geirhos, Yuxuan Li, Thadd\"aus Wiedemer, Neha Kalibhat, Zi Wang, Mani Malek, Oyvind Tafjord, Kevin Swersky, Been Kim, Priyank Jaini

llms

arXiv Machine Learning

Jul 2

VisReason: A Large-Scale Dataset for Visual Chain-of-Thought Reasoning

arXiv:2511. 17731v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has proven remarkably effective for eliciting complex reasoning in large language models (LLMs).

By Lingxiao Li, Yifan Wang, Xinyan Gao, Chen Tang, Xiangyu Yue, Chenyu You

llms fine-tuning multimodal benchmarks safety

arXiv AI

2d ago

VectorGym: A Multi-Task Benchmark for SVG Code Generation, Sketching and Editing

arXiv:2603.29852v2 Announce Type: replace-cross Abstract: We introduce VectorGym, a comprehensive benchmark suite for Scalable Vector Graphics (SVG) that spans generation from text and sketches, comp...

By Joan Rodriguez, Haotian Zhang, Abhay Puri, Haoran Dai, Tianyang Zhang, Meng Lin, Rishav Pramanik, Xiaoqing Xie, Marco Terral Rodriguez, Darsh Kaushik, Aly Shariff, Perouz Taslakian, Spandana Gella, Sai Rajeswar, David Vazquez, Christopher Pal, Marco Pedersoli

llms reinforcement-learning multimodal benchmarks

arXiv AI

Jun 12

Perceive, Interact, Reason: Building Tool-Augmented Visual Agents for Spatial Reasoning

arXiv:2606. 12830v1 Announce Type: cross Abstract: While recent vision-language models (VLMs) demonstrate strong multimodal understanding, they remain limited in spatial reasoning tasks that require active evidence acquisition and multi-step visual interaction.

By Changye Li, Meng Lu, Yi Wu, Ligeng Zhu

llms agents multimodal benchmarks

arXiv AI

Jun 16

Gen-VCoT: Generative Visual Chain-of-Thought Reasoning via Diffusion-Based RGB Intermediate Representations

arXiv:2606. 16783v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) excel at visual reasoning but rely on text-based chain-of-thought (CoT), lacking interpretable visual intermediates.

By Zhiqiang Zhou, Junliang Dai, Xu ling

llms diffusion computer-vision multimodal

arXiv Computer Vision

Aug 25

When Does Visual Generation Help Visual Understanding in Unified Multimodal Models?

arXiv:2608.22174v1 Announce Type: new Abstract: Unified multimodal models (UMMs) can perform both understanding and generation, raising a central question: can visual generation improve understanding...

By Yubo Zhu, Zhehan Kan, Jingyi Yang, Miaolin Chen, Jinbo Xing, Kai Zhu, Zijian Wang, Sheng Zhong, Wei Tong

multimodal

arXiv Machine Learning

Aug 27

VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning

VBVR-Pro is a closed‑loop testbed that enables native visual reasoning through generation, offering 300 procedurally generated tasks that scale training and allow strong transfer to external benchmarks. It supplies verifiable reward scorers based on deterministic, task‑specific rules, outperforming VLM‑as‑a‑judge approaches and providing reliable signals for reinforcement learning. The suite also facilitates controlled modality studies, revealing that video generation excels at persistent spatiotemporal tracking while interleaved generation offers a compute‑efficient alternative, and highlights the importance of vision‑native trajectories for reasoning.

By Junxiang Xu, Ruisi Wang, Fanyi Pu, Maijunxian Wang, Ran Ji, Tongxi Zhou, Chenyang Gu, Jing Zuo, Hongcan Xiao, Yimeng Geng, Wanqi Yin, Wei Chen, Oscar Qian, Zhengan Yan, Ziqi Huang, Haiwen Diao, Liang Pan, Bo Li, Xiangyu Fan, Dezhi Luo, Fengyuan Yu, Zehong Zhao, Qingying Gao, Tinghui Zhu, Yilan Zhang, Jingqi Tong, Pinyuan Feng, Zhengze Jiang, Letian Wang, Ziyu Guo, Renrui Zhang, Jieneng Chen, Sonia Joseph, Constantin Venhoff, Saman Motamed, Mengyue Yang, Chandra Sripada, Alan Yuille, Philip Torr, Lvmin Zhang, Vikash Kumar, Daniel Khashabi, Nikolaus Kriegeskorte, Rapha\"el Milli\`ere, Vincent C. M\"uller, Anyi Rao, Quan Wang, Ziwei Liu, Dahua Lin, Lei Yang, Hokin Deng, Zhongang Cai

diffusion reinforcement-learning multimodal benchmarks safety

arXiv AI

Jul 7

MentalThink: Shaping Thoughts in Mental SVG World

arXiv:2607. 03530v1 Announce Type: new Abstract: We introduce MentalThink, a visual-symbolic reasoning paradigm that equips Multimodal LLMs (MLLMs) with an executable mechanism for "mental" visualization.

By Kangheng Lin, Jisheng Yin, Dingming Li, En Yu, Yana Wei, Han Zhou, Liang Zhao, Hongyu Zhou, Hongbo Peng, Jianjian Sun, Zheng Ge, Xiangyu Zhang, Daxin Jiang, Jingyu Wang

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jul 14

ABot-N1: Toward a General Visual Language Navigation Foundation Model

arXiv:2607. 10383v1 Announce Type: cross Abstract: Visual Language Navigation foundation models aim to unify deep reasoning for grounded spatial decisions with broad versatility for diverse embodied tasks.

By Ruiyan Gong, Yingnan Guo, Junjun Hu, Jintao Kong, Xiaoxu Leng, Tianlun Li, Weize Li, Fei Liu, Zhicheng Liu, Jia Lu, Minghua Luo, Chenlin Ming, Yanfen Shen, Jiyue Tao, Zhengbo Wang, Mingyang Yin, Minqi Gu, Zihao Guan, Wei Guo, Guoqing Liu, Huachong Pang, Menglin Yang, Zeqian Ye, Xiaoxiao Geng, Zhining Gu, Honglin Han, Di Jing, Hongyu Pan, Mingchao Sun, Kuan Yang, Jianfang Zhang, Yanghong Chen, Ye He, Wei Mei, Jiahao Shi, Xiangpo Yang, Yanqing Zhu, Zedong Chu, Xiaolong Wu, Mu Xu

robotics multimodal benchmarks safety

Hugging Face Trending Papers

Aug 10

SI-Edit: Toward Sketch-Instruction Guided Local Image Editing with Pixel-Level Precision

Despite rapid advances in generative models, achieving pixel-level precision in sketch-based image editing remains a persistent challenge, particularly for fine-grained local deformations. This gap stems primarily from the critical shortage of high-quality, publicly available benchmark datasets that jointly provide geometric constraints and semantic instructions.

llms multimodal benchmarks safety

arXiv AI

Aug 18

MetaReason: Precise Interleaved Multimodal Reasoning via Editing Meta Information for Solving Geometry Problems

arXiv:2608. 15006v1 Announce Type: cross Abstract: Although visual reasoning is crucial for solving complex geometry tasks, existing vision-language models rely heavily on text-only reasoning.

By Penghao Yin, Haomin Wang, Qihong Tang, Xiaoye Qu, Hongjie Zhang, Xiao-Ping Zhang

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jun 30

Dynamo: Dynamic Skill-Tool Evolution for Vision-Language Agents

arXiv:2606. 30185v1 Announce Type: new Abstract: Improving vision-language models (VLMs) on visual reasoning typically requires retraining or hand-designed prompts and tools.

By Yutao Sun, Yanting Miao, Hao-Xuan Ma, Mengyu Zhou, Mingshuai Chen, Tiancheng Zhao, Dexin Wang, Lei Lv, Li Xu, Xiaoxi Jiang, Guanjun Jiang

llms agents reinforcement-learning multimodal benchmarks

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