Core Insights - DeepSeek has introduced a new architecture called mHC (Manifold-Constrained Hyper-Connections), which significantly improves the residual connection component of the Transformer architecture, a foundational element that has seen little change since its inception in 2015 [1][3] Group 1: Historical Context - The evolution of neural network architectures began with ResNet, introduced by Kaiming He in 2015, which addressed the vanishing gradient problem and enabled the training of very deep networks [3] - The Transformer model, released in 2017, adopted residual connections as a standard feature, forming the basis for many leading models today [3] Group 2: Technical Comparisons - Hyper-Connections, proposed by ByteDance in 2024, expanded the single residual flow into multiple parallel streams, enhancing model performance but introducing stability issues during training [5][10] - mHC aims to resolve the stability problems associated with Hyper-Connections by constraining the connection weight matrix within a specific mathematical space, ensuring that signal amplification does not occur [10][12] Group 3: Mathematical Innovation - The core innovation of mHC involves using a Doubly Stochastic Matrix for the connection weights, which guarantees that the output does not exceed the maximum input value, thus preserving energy conservation [10][12] - The implementation of mHC utilizes the Sinkhorn-Knopp algorithm to achieve the desired matrix properties efficiently, allowing for end-to-end training without introducing new hyperparameters [11][12] Group 4: Engineering Excellence - DeepSeek's approach to implementing mHC demonstrates significant engineering capabilities, including the development of custom CUDA kernels and operator fusion techniques to minimize computational delays [16] - The ability to integrate innovative mathematical solutions into practical training environments highlights DeepSeek's competitive advantage in the AI research landscape [16]