# A Unified Geometric Space for Topological Alignment Between Transformer-Based Models and Human Brain Networks

Source: [A Unified Geometric Space for Topological Alignment Between Transformer-Based Models and Human Brain Networks](<https://www.sota2.com/research/paper/69019f7a3ef7ccd74e5e3a18>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-07-08T05:00:04.940Z

Authors: Silin Chen, Yuzhong Chen, Caiwei Wang, Zifan Wang, Junhao Wang, Zifeng Jia, Keith M Kendrick, Tuo Zhang, Lin Zhao, Dezhong Yao, Tianming Liu, Xi Jiang

Published: 2025-10-28T12:09:23.000Z

## Abstract

Whether artificial neural networks organize information comparably to the human brain remains unclear. Prior brain--AI alignment studies are constrained by specific inputs and tasks, limiting cross-modal comparison. Here we introduce a brain--model topological alignment space, mapping Transformer attention topology onto human intrinsic connectivity networks (ICNs) to enable task-free, modality-agnostic comparison. Analyzing 151 Transformer-based models with 62,480 attention head graphs, we observe a continuous arc-shaped distribution reflecting varying alignment. Models optimized for global semantics aligned with higher-order ICNs, while local-detail models aligned with sensory ICNs. Non-intuitive findings include reduced alignment in DINOv2 compared to its predecessors and a counterintuitive scaling inversion in distilled DeiT models, while fine-tuning and instruction tuning had limited effect. Alignment scores showed no significant correlation with ImageNet accuracy (r = 0.266, p = 0.156). This work offers a quantitative framework for comparing the organizational principles of artificial and biological systems.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2510.24342>)

[PDF](<https://arxiv.org/pdf/2510.24342.pdf>)