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

• URL: http://arxiv.org/abs/2609.00764v1
• Date: Tue, 01 Sep 2026 05:53:05 GMT
• Title: Are You Thinking What I am Thinking? : Examining Conceptual Separation in Neural Architectures
• Abstract summary: We examine the conceptual organisation in Convolutional Neural Networks (CNNs) and Large Language Models (LLMs)<n>In CNNs, familiar ImageNet concepts form coherent and semantically ordered representations, while this weakens for unseen concepts.<n>In LLMs, clearly distinct domains remain well separated, related move closer together, and the distinction between ambiguous topics collapses.<n>These results suggest conceptual separation can reveal structure that output accuracy alone cannot, and may serve as a useful diagnostic of how robustly a model represents the concepts it is asked to identify.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Neural networks are increasingly employed to identify both well-defined and ambiguous concepts, yet output-level metrics reveal little about how those concepts are represented internally. Our study asks if these networks exhibit \textit{conceptual separation}: if examples of the same concept form coherent representations, and whether related concepts lie closer together in the representation space. We examine this conceptual organisation in Convolutional Neural Networks (CNNs) and Large Language Models (LLMs) through geometric and distributional analysis of their internal activations. In CNNs, familiar ImageNet concepts form coherent and semantically ordered representations, while this coherence weakens for unseen concepts and suffers within-class domain shift. In LLMs, clearly distinct domains remain well separated, related subdomains move closer together, and the distinction between ambiguous topics collapses at both the mean and covariance level. These results suggest that conceptual separation can reveal structure that output accuracy alone cannot, and may serve as a useful diagnostic of how robustly a model represents the concepts it is asked to identify. Code and data available on \href{https://github.com/JaeeRoshniCapstoneProject/Are-You-Thinking-What-I-m-Thinking-Examining-Conceptual -Separation-in-Neural-Architectures}{GitHub}.

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