DeepSeek's mHC: Improving the Untouchable Backbone of Deep Learning
Published:Jan 2, 2026 15:40
•1 min read
•r/singularity
Analysis
The article highlights DeepSeek's innovation in addressing the limitations of residual connections in deep learning models. By introducing Manifold-Constrained Hyper-Connections (mHC), they've tackled the instability issues associated with flexible information routing, leading to significant improvements in stability and performance. The core of their solution lies in constraining the learnable matrices to be double stochastic, ensuring signals are not amplified uncontrollably. This represents a notable advancement in model architecture.
Key Takeaways
Reference
“DeepSeek solved the instability by constraining the learnable matrices to be "Double Stochastic" (all elements ≧ 0, rows/cols sum to 1).”