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Emergence of meta-stable clustering in mean-field transformer models

ICLR 2025 · Architectures and efficiency › Attention and transformers · approach: Transformer expressivity and learnability · 2 citations (OpenAlex)

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Studies the intermediate-time meta-stable clustering of tokens in a mean-field continuous-time transformer (Q=K=V=Id, unit sphere), proving linear, quasi-linear and clustering phases and showing the emerging periodicity is set by the Gegenbauer index maximizing a rescaled coefficient sequence.

Problem

Long-time analyses of token clustering in mean-field transformer dynamics show total collapse to one cluster, but do not explain the intermediate meta-stable partial-clustering phases seen numerically, which matter for finite-depth representations.

Approach

Perturbative analysis of the mean-field PDE around the iid uniform initialization on the sphere: the perturbation grows along the Gegenbauer mode k_max maximizing gamma_k = k(k+d-2) W_k, giving k_max-periodic structure that persists for O(ln N) time, with Dobrushin-type propagation of chaos on finite intervals.

Key results

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Increase inverse temperature β from 5 to 7Mean-field Transformer dynamics · S^1, 10^4 particlesnumber of clusters during the meta-stable phase: 3 -> 4Helped
Initialize tokens independently and uniformly on S^1Mean-field Transformer dynamics · S^1number of clusters during the meta-stable phase: β=5: 3; β=7: 4Helped
Increase the number of particles NMean-field Transformer dynamics · β=2; N=1000 to N=16000exit time from the quasi-linear regimeMixed

Surprising findings

Limitations

Experimental setup

Metrics: number of clusters exit time from quasi-linear regime vs N Wasserstein/total variation distance

Builds on: Geshkovski et al. (2023) mean-field transformer model Dobrushin stability estimate Grenier's iterative scheme

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