Neural Activation Patterns Across Language Model Architectures: A Comprehensive Analysis of Cognitive Task Performance

This paper presents a comprehensive analysis of neural activation patterns across six distinct large language model (LLM) architectures, examining their performance on twelve cognitive task categories. The findings reveal fundamental differences in how encoder and decoder architectures process diverse cognitive tasks, with mathematical reasoning consistently producing the highest attention entropy and decoder models exhibiting significantly higher sparsity.