VisuLogic: A Benchmark for Evaluating Visual Reasoning in Multi-modal
  Large Language Models
Authors: name: Weiye Xu, name: Jiahao Wang, name: Weiyun Wang, name: Zhe Chen, name: Wengang Zhou, name: Aijun Yang, name: Lewei Lu, name: Houqiang Li, name: Xiaohua Wang, name: Xizhou Zhu, name: Wenhai Wang, name: Jifeng Dai, name: Jinguo Zhu

Visual reasoning is a core component of human intelligence and a critical
capability for advanced multimodal models. Yet current reasoning evaluations of
multimodal large language models (MLLMs) often rely on text descriptions and
allow language-based reasoning shortcuts, failing to measure genuine
vision-centric reasoning. To address this, we introduce VisuLogic: a benchmark
of 1,000 human-verified problems across six categories (e.g., quantitative
shifts, spatial relations, attribute comparisons). These various types of
questions can be evaluated to assess the visual reasoning capabilities of MLLMs
from multiple perspectives. We evaluate leading MLLMs on this benchmark and
analyze their results to identify common failure modes. Most models score below
30% accuracy-only slightly above the 25% random baseline and far below the
51.4% achieved by humans-revealing significant gaps in visual reasoning.
Furthermore, we provide a supplementary training dataset and a
reinforcement-learning baseline to support further progress.