While much of the attention on DeepSeek focuses on cost efficiency, the true engineering breakthrough lies in a single mechanism: Manifold-Constrained Hyper-Connections (mHC).

The core value of this research can be summarized by its impact on stability and its resulting prospects:

1. Stabilizing Training Divergence Unconstrained "Hyper-Connections" diversify connectivity but lose the identity mapping property, causing signals to explode. mHC acts as a mathematical anchor by projecting mixing matrices onto the Birkhoff polytope. In practice, this suppresses the potential divergence factor from a catastrophic 3,000x down to a stable 1.6x. This stability is the prerequisite for everything else.

2. Two Major Prospects

    Breaking the Scaling Law Plateau: By eliminating the "instability wall," mHC allows Scaling Laws to continue progressing even as we increase model depth and complexity.

    Stable Scaling of Low-Bit Models: It provides the necessary foundation for scaling ternary-weight models like BitNet, which were previously considered too volatile to train at massive scale.

I view this mathematical stability not as a radical shift, but as a necessary prerequisite for exploring more efficient, low-precision architectures that were previously considered too unstable for large-scale training.