A Unified Geometric Space for Topological Alignment Between Transformer-Based Models and Human Brain Networks
arXiv:2510.24342v2 Announce Type: replace Abstract: Prior brain-AI alignment studies are typically constrained by specific inputs and tasks, limiting their ability to capture organizational properties across models with different modalities. In this work, we focus on Transformer-based models and introduce a brain-model topological alignment space. Rather than inferring alignment from neural mechanisms, we examine it through graph-based organizational properties, mapping the intrinsic spatial attention topology of a model onto canonical human intrinsic connectivity networks (ICNs). This enables
This research addresses limitations in existing brain-AI alignment studies, driven by the increasing complexity and impact of Transformer models in AI development.
It introduces a novel method for understanding the topological alignment between AI models and human brain networks, moving beyond superficial comparisons to structural organizational properties.
The focus shifts from inferring alignment via neural mechanisms to mapping intrinsic spatial attention topology of models onto human intrinsic connectivity networks.
- · AI researchers and developers
- · Neuroscience community
- · Companies developing advanced AI models
- · Prior brain-AI alignment methodologies
- · Developers relying solely on superficial AI-brain analogies
Improved understanding of how AI models process information relative to human cognition.
Development of more 'brain-like' or neuromorphic AI architectures with potentially enhanced capabilities.
Ethical and philosophical debates surrounding AI sentience and consciousness could gain new scientific dimensions.
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Read at arXiv cs.AI