MMMR: Benchmarking Massive Multi-Modal Reasoning Tasks
Guiyao Tie, Xueyang Zhou, Tianhe Gu, Ruihang Zhang, Chaoran Hu, Sizhe Zhang, Mengqu Sun, Yan Zhang, Pan Zhou, Lichao Sun
2025-05-28
Summary
This paper talks about a new test called MMMR that checks how well advanced AI models can think and solve problems using both text and images together.
What's the problem?
The problem is that while AI models are getting better at understanding words and pictures separately, it's still hard to measure how well they can actually reason and put information together from different sources to solve complex tasks.
What's the solution?
The researchers created the MMMR benchmark, which is a set of challenges that tests these AI models on different types of reasoning, and they built a special system to evaluate how well the models do on each part.
Why it matters?
This matters because being able to reason across different types of information is important for making AI more helpful and trustworthy in real-world situations, and this new benchmark will help researchers improve these models even more.
Abstract
The MMMR benchmark evaluates multi-modal reasoning in MLLMs by assessing thinking quality through diverse reasoning types and a modular evaluation pipeline.