Core Knowledge Deficits in Multi-Modal Language Models

While Multi-modal Large Language Models (MLLMs) demonstrate impressive abilities over high-level perception and reasoning, their robustness in the wild still lags behind humans and exhibits diminished efficacy on simple tasks that are intuitive for humans. We examine the hypothesis that these deficiencies stem from the absence of core knowledge—rudimentary cognitive abilities innate to humans from early childhood. To probe core knowledge representation in MLLMs, we draw from developmental cognitive sciences and develop a large-scale benchmark, CoreCognition dataset, encompassing 12 core cognitive concepts. We evaluate 219 models with 10 different prompts, leading to a total of 2409 data points for analysis. Our findings reveal core knowledge deficits in early-developed core abilities while models demonstrate human-comparable performance in high-level cognition.. Moreover, we find that low-level abilities show little to no scaling, in stack contrast to high-level abilities. Finally, we introduce an evaluation technique “Concept Hacking”, through which we demonstrate that MLLMs do not genuinely advance toward core knowledge but instead rely on superficial patterns and shortcut learning as they scale.