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 languagebased 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. Code, data, and

baselines are available at https://visulogic-benchmark.github.io/VisuLogic.