Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning
Finetuning methods for foundation models usually change weights, prompts, or hidden states, while leaving the residual topology fixed. We ask whether residual topology itself can become a finetuning object. To study this, we adapt manifold-constrained hyper-connections (mHC), recently introduced for pre-training, to frozen-backbone finetuning. mHC turns a Transformer into an input-dependent multi-stream residual architecture, routing representations through multiple streams at every sub-layer. Across mHC variants, we find that dynamic residual routing can finetune Transformers, but that its role differs from pre-training: by preserving stream mixing and learning only how sub-layers access...
| Authors | Valentijn Oldenburg, Floris de Kam, Bente Zuijdam et al. |
|---|---|
| Primary category | cs.LG |
| Published | 20 July 2026 |
| Last updated at source | 7 October 2026 |
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