English

Can an MLP Absorb Its Own Skip Connection?

Machine Learning 2026-04-28 v1

Abstract

We study when a skip connection around a single-hidden-layer MLP can be absorbed into a residual-free MLP of the same width. We first show that for any architecture whose skip branch is an invertible linear map (including Hyper-Connections and their manifold-constrained variants), the problem reduces to the identity skip case. For homogeneous activations of degree k1k \neq 1, such as ReLU2^2 and ReGLU, absorption is unconditionally impossible by a degree argument. For gated activations whose gate is differentiable at the origin with g(0)=0g(0) = 0, including SwiGLU and GeGLU, a linearization argument gives the same conclusion. These impossibility results extend to arbitrary depth: a composition of LL residual blocks using such activations cannot be replicated by any composition of LL residual-free blocks of the same width. For ungated ReLU and GELU, the situation is richer. For generic weight matrices, absorption holds at the single-block level if and only if there exists an index set SS of size at least dd such that Wdown[:,S]Wup[S,:]=IdW_{\mathrm{down}}[:,S]\,W_{\mathrm{up}}[S,:] = -I_d. This condition is non-generic (it fails with probability one under continuous weight distributions), so skip-connected and residual-free MLPs of the same width represent generically disjoint function classes. Whether this disjointness persists for deep compositions of ReLU or GELU blocks remains open.

Cite

@article{arxiv.2604.23705,
  title  = {Can an MLP Absorb Its Own Skip Connection?},
  author = {Antonij Mijoski and Marko Karbevski},
  journal= {arXiv preprint arXiv:2604.23705},
  year   = {2026}
}
R2 v1 2026-07-01T12:35:46.165Z