MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal
  Models
Authors: name: Huanqia Cai, name: Yijun Yang, name: Winston Hu

IQ testing has served as a foundational methodology for evaluating human
cognitive capabilities, deliberately decoupling assessment from linguistic
background, language proficiency, or domain-specific knowledge to isolate core
competencies in abstraction and reasoning. Yet, artificial intelligence
research currently lacks systematic benchmarks to quantify these critical
cognitive dimensions in multimodal systems. To address this critical gap, we
propose MM-IQ, a comprehensive evaluation framework comprising 2,710
meticulously curated test items spanning 8 distinct reasoning paradigms.
  Through systematic evaluation of leading open-source and proprietary
multimodal models, our benchmark reveals striking limitations: even
state-of-the-art architectures achieve only marginally superior performance to
random chance (27.49% vs. 25% baseline accuracy). This substantial performance
chasm highlights the inadequacy of current multimodal systems in approximating
fundamental human reasoning capacities, underscoring the need for
paradigm-shifting advancements to bridge this cognitive divide.