This article presents Manifold-Constrained Hyper-Connections (mHC), a framework designed to improve the stability and scalability of Hyper-Connections in neural networks. By projecting residual connections onto a specific manifold, mHC restores the identity mapping property while optimizing memory access and computational efficiency. Experimental results indicate that mHC enhances performance in large-scale training scenarios.