arXiv:2609.35942v1 Announce Type: new
Abstract: Recent work in visual question answering has shown that vision-language models can exhibit strong reasoning capabilities by translating visual inputs i...
By Ting-Chih Chen, Emile van Krieken, Shujian Yu, Filip Ilievski
arXiv:2608.22429v1 Announce Type: new
Abstract: Multimodal Large Language Models (MLLMs) capable of thinking with images often rely on external tools for fine-grained perception. However, this relian...
By Changjiang Jiang, Qiannian Zhao, Lei Xin, Jinxiang Xie, Preslav Nakov, Zhuohan Xie
arXiv:2608.21796v1 Announce Type: cross
Abstract: Knowledge-based Visual Question Answering (KB-VQA) aims to answer queries that necessitate reasoning over external knowledge sources beyond the visua...
By Long Shu, Shuochen Liu, Wei Chen, Junda Lin, Zhi Zheng, Huijun Hou, Tong Xu
arXiv:2601. 07761v2 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) face a fundamental dilemma in video reasoning: they are caught between the prohibitive computational costs of verbose reasoning and the hallucination risks of efficient, ungrounded approaches.
By Yanxiang Huang, Guohua Gao, Zhaoyang Wei
Recent advances in multimodal generative models have enabled instruction-based image generation to move beyond semantic manipulation to knowledge-driven visual reasoning. However, these methods focus on explicit commonsense reasoning, shallow causal understanding, and direct knowledge recall, failing at knowledge-intensive generation.
arXiv:2505.03654v3 Announce Type: replace-cross
Abstract: Multimodal Large Language Models have shown strong performance across multimodal tasks, and recent personalized MLLMs can recognize user-spec...
By Yifan Xiang, Zhenxi Zhang, Bin Li, Yixuan Weng, Bo Gao, Shoujun Zhou, Yangfan He, Yilin Yuan, Keqin Li