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 increasingly open, collaborative culture among Chinese AI companies, which have published a growing share of their research in public.

Full report : DeepSeek researchers detail mHC, a new architecture they used to train 3B, 9B, and 27B models, finding it scaled without adding significant computational burden.