No Equivariant Architecture Covers All Equivariant Attention
2608.30417

Authors

Tīkun Ông

Abstract

We give a complete characterization of equivariant multi-head self-attention (MHSA): if an MHSA layer is equivariant to a symmetry group $G$, then $G$ can only act by permuting head-clusters, with QK and OV matrices satisfying an equivariance constraint tied to the group action. As a consequence, we prove that any fixed MHSA architecture that achieves exact equivariance by polynomially parameterizing unconstrained MHSA parameters inevitably leads to expressivity loss within the class of equivariant maps: the equivariance locus of unconstrained MHSA forms a union of extremely many Zariski-irreducible components in a reduced parameter space, and any single architecture covers at most one.

For $G=D_4$ acting on $C$ copies of the regular representation as the token feature space, we show that there are $Ω(C^{64})$ components for eight attention heads.

Resources

Ray graphicRay graphicRay graphicRay graphic

Stay in the loop

Every AI paper that matters, free in your inbox daily.

Details

  • takara.ai
  • Custom AI and machine learning from the Frontier Research Team.
  • © 2026 takara.ai Ltd
  • Content is sourced from third-party publications.
Ray graphicRay graphicRay graphic