Chinese artificial intelligence start-up DeepSeek has ushered in 2026 with a new technical paper, co-authored by founder Liang Wenfeng, that proposes a rethink of the fundamental architecture used to train foundational AI models.
The method – dubbed Manifold-Constrained Hyper-Connections (mHC) – forms part of the Hangzhou firm’s push to make its models more cost-effective as it strives to keep pace with better-funded US rivals with deeper access to computing power.
It also reflected the…
Source: DeepSeek kicks off 2026 with paper signalling push to train bigger models for less
