title: LiveVQA: Live Visual Knowledge Seeking
summary: We introduce LiveVQA, an automatically collected dataset of latest visual
  knowledge from the Internet with synthesized VQA problems. LiveVQA consists of
  3,602 single- and multi-hop visual questions from 6 news websites across 14
  news categories, featuring high-quality image-text coherence and authentic
  information. Our evaluation across 15 MLLMs (e.g., GPT-4o, Gemma-3, and
  Qwen-2.5-VL family) demonstrates that stronger models perform better overall,
  with advanced visual reasoning capabilities proving crucial for complex
  multi-hop questions. Despite excellent performance on textual problems, models
  with tools like search engines still show significant gaps when addressing
  visual questions requiring latest visual knowledge, highlighting important
  areas for future research.
paper_authors._id: 67f4adf70864398533373364
paper_authors.name: Mingyang Fu
paper_authors._id: 67f4adf70864398533373365
paper_authors.name: Yuyang Peng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-08T10:35:30.731Z
paper_authors._id: 67b945d34535b84d0b7223e8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/67b945d34535b84d0b7223e8/iVviersiF1ijb1ut5LHEz.jpeg
paper_authors.fullname: Yuyang Peng
paper_authors.type: user
paper_authors.user: Ayiirep
paper_authors._id: 67f4adf70864398533373366
paper_authors.name: Benlin Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-08T10:34:51.813Z
paper_authors._id: 6326723c21c98f83d6ad128e
paper_authors.avatarUrl: /avatars/6c208ff815c037d0a6a2ada3198e7fd4.svg
paper_authors.fullname: Benlin Liu
paper_authors.type: user
paper_authors.user: Tim666
paper_authors._id: 67f4adf70864398533373367
paper_authors.name: Yao Wan
paper_authors._id: 67f4adf70864398533373368
paper_authors.name: Dongping Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-08T10:34:42.707Z
paper_authors._id: 643be8879f5d314db2d9ed23
paper_authors.avatarUrl: /avatars/64e9bb2c4e10fbe03e2b81afedf40865.svg
paper_authors.fullname: Chen Dongping
paper_authors.type: user
paper_authors.user: shuaishuaicdp
paper_title: LiveVQA: Live Visual Knowledge Seeking
paper_summary: We introduce LiveVQA, an automatically collected dataset of latest visual
  knowledge from the Internet with synthesized VQA problems. LiveVQA consists of
  3,602 single- and multi-hop visual questions from 6 news websites across 14
  news categories, featuring high-quality image-text coherence and authentic
  information. Our evaluation across 15 MLLMs (e.g., GPT-4o, Gemma-3, and
  Qwen-2.5-VL family) demonstrates that stronger models perform better overall,
  with advanced visual reasoning capabilities proving crucial for complex
  multi-hop questions. Despite excellent performance on textual problems, models
  with tools like search engines still show significant gaps when addressing
  visual questions requiring latest visual knowledge, highlighting important
  areas for future research.
paper_ai_summary: Evaluation of various MLLMs on LiveVQA, a dataset of visual questions with up-to-date visual knowledge, shows that advanced visual reasoning is essential for complex multi-hop questions.
paper_ai_keywords: MLLMs GPT-4o Gemma-3 Qwen-2.5-VL visual reasoning visual questions multi-hop questions visual knowledge