model scalability
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Manifold-Constrained Hyper-Connections: Enhancing HC
Read Full Article: Manifold-Constrained Hyper-Connections: Enhancing HC![[R] New paper by DeepSeek: mHC: Manifold-Constrained Hyper-Connections](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-7774-300x171.png)
Manifold-Constrained Hyper-Connections (mHC) is introduced as a novel framework to enhance the Hyper-Connections (HC) paradigm by addressing its limitations in training stability and scalability. By projecting the residual connection space of HC onto a specific manifold, mHC restores the identity mapping property, which is crucial for stable training, and optimizes infrastructure to ensure efficiency. This approach not only improves performance and scalability but also provides insights into topological architecture design, potentially guiding future foundational model developments. Understanding and improving the scalability and stability of neural network architectures is crucial for advancing AI capabilities.
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