🔥[2025-04-26] VisuLogic has been merged into VLMEvalkit. You can evaluate your model on VisuLogic with it ! Usage see VLMEvalkit ! 🚀
🔥[2025-04-22] Release the paper, training data and training code! 🚀
🔥[2025-04-08] Release the benchmark and the code! 🚀
✅ To-do
Release the benchmark dataset and eval code
Release training code
Release the paper
Release the training dataset
Release model ckpts
📖 Introduction
VisuLogic is a newly designed benchmark aimed at evaluating the visual reasoning capabilities of Multi-modal Large Language Models (MLLMs), independent of textual reasoning processes. It features carefully constructed visual reasoning tasks spanning multiple categories, divided into six types based on required reasoning skills (e.g., Quantitative Reasoning, which involves understanding and deducing changes in the quantity of elements in images). Unlike existing benchmarks, VisuLogic is a challenging visual reasoning benchmark that is inherently difficult to articulate using language, providing a more rigorous evaluation of the visual reasoning capabilities of MLLMs. 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.
Overview
🌟 Key Features
🚀 Visuo-Logical Challenge
The first benchmark to integrate visual perception with logical reasoning, enabling authentic multimodal evaluation. 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.
🛠️ Rigorous Design
Includes 1,000 meticulously curated questions, spanning 6 domains and 24 subcategories, for comprehensive performance evaluation.
📝 Anti-Linguistic Shortcut
Designed to avoid linguistic reasoning, ensuring tasks rely on genuine visual reasoning rather than shortcuts.
💡 RL Exploration
We identify the RL technique as a promising direction for improving the visual reasoning capabilities of MLLMs. Through RL method, models reach SOTA in VisuLogic!
✅ Fully Open-source
We open-source all the evaluation code, training scripts, and datasets associated with this work to promote further research and innovation.
1@article{xu2025visulogic,
2 title={VisuLogic: A Benchmark for Evaluating Visual Reasoning in Multi-modal Large Language Models},
3 author={Xu, Weiye and Wang, Jiahao and Wang, Weiyun and Chen, Zhe and Zhou, Wengang and Yang, Aijun and Lu, Lewei and Li, Houqiang and Wang, Xiaohua and Zhu, Xizhou and Wang, Wenhai and Dai, Jifeng and Zhu, Jinguo},
4 journal={arXiv preprint arXiv:2504.15279},
5 year={2025},
6 url={https://arxiv.org/abs/2504.15279}
7}
🎉 Thank you for your interest in VisuLogic! We hope this benchmark helps drive advancements in multimodal visual reasoning! 🚀
