LiveVQA: Live Visual Knowledge Seeking
Authors: name: Mingyang Fu, name: Yuyang Peng, name: Benlin Liu, name: Yao Wan, name: Dongping Chen

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.