Towards Universality: Studying Mechanistic Similarity Across Language Model Architectures
Abstract
The hypothesis of \textit{Universality} in interpretability suggests that different neural networks may converge to implement similar algorithms on similar tasks. In this work, we investigate two mainstream architectures for language modeling, namely Transformers and Mambas, to explore the extent of their mechanistic similarity. We propose to use Sparse Autoencoders (SAEs) to isolate interpretable features from these models and show that most features are similar in these two models. We also validate the correlation between feature similarity and~\univ. We then delve into the circuit-level analysis of Mamba models and find that the induction circuits in Mamba are structurally analogous to those in Transformers. We also identify a nuanced difference we call \emph{Off-by-One motif}: The information of one token is written into the SSM state in its next position. Whilst interaction between tokens in Transformers does not exhibit such trend.
Cite
Text
Wang et al. "Towards Universality: Studying Mechanistic Similarity Across Language Model Architectures." International Conference on Learning Representations, 2025.Markdown
[Wang et al. "Towards Universality: Studying Mechanistic Similarity Across Language Model Architectures." International Conference on Learning Representations, 2025.](https://mlanthology.org/iclr/2025/wang2025iclr-universality/)BibTeX
@inproceedings{wang2025iclr-universality,
title = {{Towards Universality: Studying Mechanistic Similarity Across Language Model Architectures}},
author = {Wang, Junxuan and Ge, Xuyang and Shu, Wentao and Tang, Qiong and Zhou, Yunhua and He, Zhengfu and Qiu, Xipeng},
booktitle = {International Conference on Learning Representations},
year = {2025},
url = {https://mlanthology.org/iclr/2025/wang2025iclr-universality/}
}