MME-CC: A Challenging Multi-Modal Evaluation Benchmark of Cognitive Capacity

Kaiyuan Zhang, Chenghao Yang, Zhoufutu Wen, Sihang Yuan, Qiuyue Wang, Chaoyi Huang, Longxiang Liu, Jianpeng Jiao, Sijin Wu, Lishu Luo, Guosheng Zhu, Huawenyu Lu, Xuanliang Zhang, He Wang, Yu Liu, Xiaolei Zhu, Jianing Wen, Ge Zhang, Chaoyou Fu, Yi Lin et al. (2 additional authors not shown)

Keywords: Visual Reasoning, Chain-of-Thought, Vision-Language Models, Benchmark Evaluation

TL;DR: We introduce MME-CC, a vision-grounded benchmark of 11 tasks spanning spatial, geometric, and knowledge reasoning; models remain weak in spatial/geometric reasoning, show recurring error patterns, and follow a three-stage CoT strategy.

Abstract:

As reasoning models scale rapidly, the essential role of multimodality in human cognition has come into sharp relief, driving a growing need to probe vision-centric cognitive behaviors.
Yet, existing multimodal benchmarks either overemphasize textual reasoning or fall short of systematically capturing vision-centric cognitive behaviors, leaving the cognitive capacity of MLLMs insufficiently assessed.
To address this limitation, we introduce MME-CC (\textbf{M}ulti-\textbf{M}odal \textbf{E}valuation benchmark of \textbf{C}ognitive \textbf{C}apacity), a vision-grounded benchmark that organizes 11 representative reasoning tasks into three fundamental categories of visual information—spatial, geometric, and knowledge-based reasoning—and provides fine-grained analyses of MLLMs’ cognitive capacity across these dimensions.
Based on MME-CC, we conduct extensive experiments over 16 representative MLLMs.
Our study reveals that closed-source models currently lead overall (e.g., 42.66 for Gemini-2.5-Pro vs.\ 30.45 for GLM-4.5V), while spatial and geometric reasoning remain broadly weak ($\leq$30\%).
We further identify common error patterns---including orientation mistakes, fragile cross-view identity persistence, and poor adherence to counterfactual instructions---and observe that Chain-of-Thought typically follows a three-stage process (extract $\rightarrow$ reason $\rightarrow$ verify) with heavy reliance on visual extraction.
We hope this work catalyzes a shift toward treating the cognitive capacity of MLLMs as central to both evaluation and model design.

Primary Area: datasets and benchmarks

Submission Number: 9064