mHC: Stabilizing and Scaling Hyper-Connections with Manifold Constraints
Published:Dec 31, 2025 14:16
•1 min read
•ArXiv
Analysis
This paper addresses the instability and scalability issues of Hyper-Connections (HC), a recent advancement in neural network architecture. HC, while improving performance, loses the identity mapping property of residual connections, leading to training difficulties. mHC proposes a solution by projecting the HC space onto a manifold, restoring the identity mapping and improving efficiency. This is significant because it offers a practical way to improve and scale HC-based models, potentially impacting the design of future foundational models.
Key Takeaways
- •mHC addresses the instability and scalability problems of Hyper-Connections.
- •The core idea is to project the HC space onto a manifold to restore the identity mapping.
- •The approach includes infrastructure optimization for efficiency.
- •Empirical results show performance improvements and better scalability.
Reference
“mHC restores the identity mapping property while incorporating rigorous infrastructure optimization to ensure efficiency.”