Title: VisuLogic: A Benchmark for Evaluating Visual Reasoning in Multi-modal Large Language Models
Primary area: datasets and benchmarks
Keywords: Multi-modal Large Language Models, Benchmark, Visual Reasoning

Abstract: 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.