arXiv AI

MarineEVT: Advancing Event-Centric Marine Video Understanding via Visual Tool Reasoning

arXiv:2607. 24064v1 Announce Type: cross Abstract: Recent Vision-Language Models (VLMs) have achieved remarkable success in visual understanding, driven by the growing availability of high-quality image-text pairs.

arXiv AI

Aug 6

Chain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens

arXiv:2511. 19418v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) excel at reasoning in linguistic space but struggle with perceptual understanding that requires dense visual perception, e.

By Yiming Qin, Bomin Wei, Jiaxin Ge, Konstantinos Kallidromitis, Stephanie Fu, Trevor Darrell, XuDong Wang

llms computer-vision multimodal benchmarks safety

arXiv AI

Jun 10

V-REX: Benchmarking Exploratory Visual Reasoning via Chain-of-Questions

arXiv:2512. 11995v2 Announce Type: replace-cross Abstract: While many vision-language models (VLMs) are developed to answer well-defined, straightforward questions with highly specified targets, as in most benchmarks, they often struggle in practice with complex open-ended tasks, which usually require multiple rounds of exploration and reasoning in the visual space.

By Chenrui Fan, Yijun Liang, Shweta Bhardwaj, Kwesi Cobbina, Ming Li, Tianyi Zhou

llms multimodal benchmarks

arXiv AI

Jun 8

Watch, Remember, Reason: Human-View Video Understanding with MLLMs

arXiv:2606. 07433v1 Announce Type: cross Abstract: Video understanding is being rapidly transformed by multimodal large language models (MLLMs), as research moves from short clips to long, multimodal, and knowledge-intensive video scenarios.

By Jiahao Meng, Yue Tan, Qi Xu, Kuan Gao, Weisong Liu, Yanwei Li, Jason Li, Lingdong Kong, Haochen Wang, Qianyu Zhou, Jiangning Zhang, Guangliang Cheng, Yunhai Tong, Lu Qi, Minghsuan Yang

llms multimodal benchmarks safety

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

Jul 14

Prompting-MammAlps: Fine-Grained Text-to-Video Retrieval for Camera-Trap Data

arXiv:2607. 09876v1 Announce Type: cross Abstract: Automatically retrieving videos from large camera-trap datasets remains challenging.

By Valentin Gabeff, Baptiste Maquignaz, Jennifer Shan, Sepideh Mamooler, Gencer Sumbul, Blair Costelloe, Devis Tuia, Alexander Mathis

llms agents diffusion computer-vision multimodal benchmarks safety

arXiv AI

Jun 19

Vero: An Open RL Recipe for General Visual Reasoning

arXiv:2604. 04917v3 Announce Type: replace-cross Abstract: What does it take to build a visual reasoner that works across charts, science, spatial understanding, and open-ended tasks?

By Gabriel Sarch, Linrong Cai, Qunzhong Wang, Haoyang Wu, Danqi Chen, Zhuang Liu

llms reinforcement-learning efficiency multimodal benchmarks

arXiv AI

Jun 2

Moment-Video: Diagnosing Temporal Fidelity of Video MLLMs on Momentary Visual Events

arXiv:2606. 02522v1 Announce Type: cross Abstract: Video multimodal large language models (MLLMs) have made rapid progress on general and long-form video understanding, yet their ability to preserve brief answer-critical visual evidence remains underexplored.

By Xiaolin Liu, Yilun Zhu, Xiangyu Zhao, Xuehui Wang, Yan Li, Xin Li, Haoyu Cao, Xing Sun, Shaofeng Zhang, Xu Yang, Zhihang Zhong, Xue Yang

llms multimodal benchmarks

arXiv Machine Learning

23h ago

Leveraging existing sparse point annotations for benthic imagery dense segmentation

arXiv:2608. 17561v1 Announce Type: cross Abstract: The health of marine ecosystems is a critical indicator of global environmental change, yet the physical constraints of underwater observation and the intrinsic challenges of processing marine imagery severely limit the scalability of systematic monitoring.

By Cesar Borja, Breck A. McCollum, Jarret E. Byrnes, Kenneth Sebens, Ana C. Murillo

computer-vision benchmarks

arXiv AI

Aug 5

SpatialCLI: Learning to Reason With Spatial Tools, Then Without Them

arXiv:2607. 27703v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly used in embodied agents to interpret visual inputs, reason about spatial relationships, and make task-level decisions based on that reasoning.

By Yang Zhou, Zixuan Huang, Sunzhu Li, Zhuo Yang, Chen Zhang, Shunian Chen, Caijun Yan, Jianyao Xu, Shunyu Liu, Weijie Fu, Peiliang Li, Xiaozhi Chen, Yuxiang Cai

llms agents computer-vision reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv AI

Jul 10

OpenCoF: Learning to Reason Through Video Generation

arXiv:2607. 08763v1 Announce Type: cross Abstract: Reasoning has become a core capability for large models, especially when reliable decisions require understanding logical consequences.

By Xinyan Chen, Ziyu Guo, Renrui Zhang, Dongzhi Jiang, Hongsheng Li

diffusion fine-tuning benchmarks

arXiv AI

Jul 10

MAVEN: A Multi-stage Agentic Annotation Pipeline for Video Reasoning Tasks

arXiv:2605. 21917v2 Announce Type: replace-cross Abstract: Training Vision Language Models (VLMs) for video event reasoning requires high-quality structured annotations capturing not only what happened, but when, where, why, and with what consequence, at a scale manual labelling cannot support.

By Han Zhang, Wanting Jiang, Tomasz Kornuta, Tian Zheng, Vidya Murali

llms agents reinforcement-learning fine-tuning safety data-engineering

arXiv AI

Jun 12

DSAEval: Evaluating Data Science Agents on a Wide Range of Real-World Data Science Problems

arXiv:2601. 13591v2 Announce Type: replace Abstract: Recent LLM-based data agents aim to automate data science tasks ranging from data analysis to deep learning.

By Maojun Sun, Yifei Xie, Yue Wu, Ruijian Han, Binyan Jiang, Defeng Sun, Yancheng Yuan, Jian Huang

llms agents multimodal benchmarks

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