China’s DeepSeek drops papers on stable hyper-connections and ‘Engram’ memory—V4 might just lap Claude and GPT in coding.
Western models dominating headlines, but China’s quietly rewriting the efficiency playbook.
DeepSeek released two arXiv papers signaling V4: ‘Manifold-Constrained Hyper-Connections’ stabilizes advanced training (fixing Hyper-Connections’ scale instability beyond Residual Connections), and ‘Engram,’ a memory system that skips recomputing known facts for better reasoning, coding, and math. Demos are live on GitHub.[3]
Developers get open techniques for leaner, smarter LLMs—perfect for low-power edge, long-context tasks, or cost-sensitive apps. V4 aims to outpace Claude/GPT in advanced coding, reshaping global adoption.[3]
Beats U.S. models in efficiency; Engram boosts knowledge/math without full retraining. Competitive edge for sovereign AI in energy-constrained regions like Australia.[3]
Download GitHub demos, experiment with Engram in your RLHF loops, and prep for V4 benchmarks. Is China about to own the ‘efficient reasoning’ race?
Source: ACI