MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal
  Models
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.
67a1b8afe03dbbbbb51bb5c1
Huanqia Cai
claimed_verified
2025-02-04T09:39:06.531Z
633b99cfc9b44f5c6ac8fe03
https://cdn-avatars.huggingface.co/v1/production/uploads/633b99cfc9b44f5c6ac8fe03/sFmpPlWwo07ttcWWuV1Iw.jpeg
huanqiacai
user
huanqia
67a1b8afe03dbbbbb51bb5c2
Yijun Yang
claimed_verified
2025-02-04T14:48:36.068Z
645e553c3b6d85c65e8b0e54
/avatars/1fffc6499b9d65b21a895ca96f03b781.svg
Steven
user
yijunyang
67a1b8afe03dbbbbb51bb5c3
Winston Hu
MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal
  Models
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.
A multimodal IQ test framework (MM-IQ) is presented to evaluate reasoning capacities in AI, revealing significant performance gaps compared to human baselines.
multimodal systems reasoning paradigms systematic evaluation benchmarking