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Author: Tianwei Zhao

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                 To explore the core knowledge representation in MLLMs, we introduce <strong>CoreCognition</strong>, a large-scale benchmark encompassing 12 core knowledge concepts grounded in developmental cognitive science.
-                We evaluate 230 models with 11 different prompts, leading to a total of 1503 data points for analysis. Our experiments uncover four key findings, collectively demonstrating core knowledge deficits in MLLMs: they consistently underperform and show reduced, or even absent, scalability on low-level abilities relative to high-level ones.
+                We evaluate 230 models with 11 different prompts. Our experiments uncover four key findings, collectively demonstrating core knowledge deficits in MLLMs: they consistently underperform and show reduced, or even absent, scalability on low-level abilities relative to high-level ones.
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                 Finally, we propose <strong>Concept Hacking</strong>, a novel controlled evaluation method, that reveals MLLMs fail to progress toward genuine core knowledge understanding, but instead rely on shortcut learning as they scale.