DeepSeek又放大招！梁文锋署名新论文引关注

Core Insights - DeepSeek has introduced a new framework called "Manifold-Constrained Hyperconnection" (mHC) aimed at enhancing scalability while reducing the computational power and energy requirements for training advanced AI systems [1][14][19] - The next flagship system, R2, is expected to be launched around the Chinese New Year in February [1][14] Summary of Key Points Introduction of mHC Framework - DeepSeek published a paper detailing the mHC framework, which addresses instability issues in traditional hyperconnections during large-scale model training while maintaining significant performance gains [1][15][16] - The paper lists three primary authors, including DeepSeek's founder Liang Wenfeng [1][17] Performance and Scalability - The mHC framework projects the residual connection space of hyperconnections onto a specific manifold, restoring the identity mapping property and integrating strict infrastructure optimizations for operational efficiency [3][19] - Empirical experiments indicate that mHC effectively supports large-scale training, providing notable performance improvements with better scalability. When the expansion rate is set to 4, it incurs only a 6.7% additional time overhead [3][19][21] Future Research Directions - The paper suggests that mHC serves as a flexible and practical extension of hyperconnection paradigms, potentially deepening the understanding of topological architecture design and guiding the evolution of foundational models [3][21] - It opens up several important research directions, including compatibility with various manifold constraints tailored to specific learning objectives and the exploration of differentiated geometric constraints to better balance plasticity and stability [3][21]