Core Insights - The article highlights that Multi-Modal Language Models (MLLMs) exhibit impressive capabilities in high-level visual understanding and reasoning tasks, yet they frequently fail in seemingly simple tasks that even infants can accomplish [1][2] - It questions whether MLLMs lack "core knowledge," which is essential for early human learning, indicating a potential cognitive blind spot in these models [2][5] Research Findings - A study from UC San Diego titled "Core Knowledge Deficits in Multi-Modal Language Models" systematically analyzes the lack of core cognitive abilities in mainstream MLLMs [3][5] - The research reveals that current MLLMs widely lack core cognitive abilities, which cannot be naturally acquired through model scaling [5][12] CoreCognition Framework - The authors developed an innovative multi-modal assessment system called CoreCognition, along with a unique "Concept Hacking" method to test whether models genuinely understand the core knowledge behind tasks or are merely guessing [6][18] - CoreCognition is a large-scale assessment framework focusing on core knowledge, inspired by Piaget's theories of cognitive development, and aims to bridge the gap between cognitive science and AI testing [9][11] Assessment Design - The CoreCognition dataset includes 1,503 image-question pairs and generates 2,530 evaluation data points across 230 mainstream multi-modal models and 11 prompt designs, effectively covering various model scales and instruction comprehension [11] - The assessment is designed to be discriminative, minimizing confounding factors and avoiding text shortcuts, ensuring that models must engage in multi-modal reasoning to arrive at correct answers [11][12] Key Findings on Model Performance - MLLMs show significant deficiencies in basic cognitive tasks, particularly in areas like boundary perception and spatial awareness, performing poorly compared to their understanding of more complex tasks [12][14] - The study indicates that increasing model size does not significantly enhance basic cognitive abilities, and in some cases, larger models perform worse on foundational tasks [16][20] Concept Hacking Methodology - The Concept Hacking method involves creating control and manipulated groups to test models' understanding of core concepts by reversing key features while keeping other conditions constant [18][29] - Results show that many models perform well on standard tasks but fail dramatically when key features are altered, indicating a reliance on superficial learning rather than true understanding [20][30] Implications and Future Directions - The findings suggest that MLLMs lack the foundational cognitive scaffolding that humans use to build higher-level reasoning, posing a fundamental challenge to the current model development path focused on scaling [22][30] - Future directions may include explicitly injecting physical and spatial common sense into pre-training phases, exploring cognitive-guided training mechanisms, and developing more controlled assessments of cognitive abilities [30]