Quantitative Clustering in Mean-Field Transformer Models

Shi Chen, Zhengjiang Lin, Yury Polyanskiy, Philippe Rigollet
4/20/2025
cs.LGmath.APmath.DS

No confident code implementation found for this paper.

Abstract

The evolution of tokens through a deep transformer models can be modeled as an interacting particle system that has been shown to exhibit an asymptotic clustering behavior akin to the synchronization phenomenon in Kuramoto models. In this work, we investigate the long-time clustering of mean-field transformer models. More precisely, we establish exponential rates of contraction to a Dirac point mass for any suitably regular initialization under some assumptions on the parameters of transformer models, any suitably regular mean-field initialization synchronizes exponentially fast with some quantitative rates.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{chen2025quantitative,
  title  = {Quantitative Clustering in Mean-Field Transformer Models},
  author = {Shi Chen and Zhengjiang Lin and Yury Polyanskiy and Philippe Rigollet},
  year   = {2025},
  eprint = {2504.14697},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.14697v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2504.14697.svg)](https://codeofpaper.com/paper/2504.14697)

Discussion