AINeutralarXiv – CS AI · Jun 46/10
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A Unified Geometric Space for Topological Alignment Between Transformer-Based Models and Human Brain Networks
Researchers have developed a novel framework for comparing Transformer-based AI models by mapping their internal attention topology onto human brain networks, analyzing 151 models across vision, language, and multimodal domains. The study reveals an arc-shaped distribution of topological alignment with human cognition, where models trained for semantic abstraction align with higher-order brain networks, while detail-focused models align with low-level networks, though alignment scores show weak correlation with standard performance metrics.