Are You Thinking What I am Thinking? : Examining Conceptual Separation in Neural Architectures

The paper investigates whether neural networks exhibit conceptual separation, meaning that examples of the same concept cluster together and related concepts are closer in representation space. Using geometric and distributional analyses, the authors find that Convolutional Neural Networks (CNNs) produce coherent, semantically ordered representations for familiar ImageNet concepts, but this coherence weakens for unseen concepts and under domain shift. Large Language Models (LLMs) keep distinct domains well separated, bring related subdomains closer, yet lose distinction between ambiguous topics at both mean and covariance levels.

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