DeepSeek新年炸场！梁文锋署名论文发布

Core Viewpoint - DeepSeek has introduced a new network architecture called mHC (Manifold-Constrained Hyper-Connections) aimed at addressing instability issues in large-scale model training, potentially guiding the evolution of next-generation infrastructure [1][3][4]. Group 1: Technical Innovations - The mHC architecture improves upon traditional hyper-connection frameworks by balancing performance and efficiency, akin to adding "traffic rules" to information channels, ensuring stable information flow during model training [4]. - The research highlights that mHC can enhance the stability and scalability of large models, making it easier to implement in complex scenarios, such as multi-modal models and industrial decision-making systems [5]. Group 2: Industry Implications - mHC may reduce hardware investment and training time for companies developing larger foundational models, thus lowering the barriers for small and medium AI enterprises to create more complex models [5]. - The innovation is seen as a fundamental advancement in addressing core issues within the Transformer architecture, with expectations for significant updates in DeepSeek's upcoming V4 version [5]. Group 3: Recent Developments - Despite not launching major versions like R2 or V4 in 2023, DeepSeek has continued to innovate, releasing DeepSeek-V3.2 and DeepSeek-Math-V2, the latter being the first math model to reach international Olympiad gold medal standards [6].