mlsanityData to 7 Oct 2026

Can an MLP Absorb Its Own Skip Connection Exactly?

arXiv 2604.23705 ↗First posted 26 Apr 2026cs.LGcited by 0

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Abstract (arXiv)

The benefits usually attributed to skip connections are optimization-theoretic: a smoother loss landscape and better gradient propagation. We ask a representational question instead: given a residual block x -> x + MLP(x), does a residual-free MLP of the same width compute the same function? The answer is no, unconditionally and at every depth, for every activation used in current frontier language models (ReLU^2, ReGLU, SwiGLU, GeGLU). For ungated ReLU and GELU absorption is possible, but only on a set of weights of measure zero. The two families are therefore generically disjoint: removing a skip connection and retraining cannot recover exactly the same function at equal width.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 26 references that are in the map. First posted on arXiv 26 Apr 2026.

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19 of 26 references.

References · 19

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7 more
  1. 2024Hyper-Connectionscited by 81
  2. 2018ResNet with one-neuron hidden layers is a Universal Approximatorcited by 78
  3. 2025mHC: Manifold-Constrained Hyper-Connectionscited by 76
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  5. 2025Always Skip Attentioncited by 12
  6. 2025Transformers Don't Need LayerNorm at Inference Time: Scaling LayerNorm Removal to GPT-2 XL and the Implications for Mechanistic Interpretabilitycited by 7
  7. 2025Cutting the Skip: Training Residual-Free Transformerscited by 7

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