Published on: Jan 5, 2026
mHC: Manifold-Constrained Hyper-Connections
mHC stabilizes Hyper-Connections by constraining residual mixing onto a manifold that restores identity-like signal flow, enabling more reliable large-scale training with modest overhead.
Large Language Models
Large Language Models
Neural Network Architecture
Neural Network Architecture
Deep Learning
Deep Learning
AI
AI
Hyper-Connections widen and diversify residual pathways to improve performance, but this can break the "identity mapping" behavior that makes residual networks stable at scale—leading to instability and higher system overhead. The authors propose Manifold-Constrained Hyper-Connections (mHC), which constrains the residual-mixing space by projecting it onto a chosen manifold to recover identity-like propagation while keeping the benefits of multi-stream connectivity. Experiments indicate improved stability and scalability during large-scale training with practical engineering optimizations for efficiency.
DOI: 10.48550/arXiv.2512.24880
Key Takeaways
This paper targets a specific scaling failure mode: widening/diversifying residual streams boosts performance, but can destroy the identity mapping that keeps deep nets stable.
Core idea: project HC residual mixing onto a constrained manifold to recover stable identity-like propagation.
Why it matters: improves training stability + scalability in large runs where HC can become unstable.
Practical angle: pairs the math constraint with systems optimizations to keep implementation efficient.
Institutions
DeepSeek-AI
Hangzhou, Zhejiang, China
Journal
Cornell University
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