DeepSeek Researchers Apply a 1967 Matrix Normalization Algorithm to Fix Instability in Hyper Connections

Here is your today’s AI Dev Brief from Marktechpost, covering core research, models, infrastructure tools, and applied updates for AI developers and researchers.

DeepSeek’s new mHC, Manifold Constrained Hyper Connections, takes the Hyper Connections idea of widening residual streams and makes it trainable at scale by constraining the residual mixing matrices to be doubly stochastic using the classic Sinkhorn Knopp algorithm, which keeps long range signal gain bounded around 1.6 instead of spiking near 3000 in a 27B MoE model while adding about 6.7 percent training overhead and delivering consistent accuracy gains on benchmarks like BBH and DROP, giving LLM designers a new stable scaling axis based on residual topology rather than only parameter count or context length. Read the full analysis/article here.

Tencent Hunyuan researchers open sourced HY MT1.5, a 2 model translation stack, HY MT1.5 1.8B and HY MT1.5 7B, that supports mutual translation across 33 languages with 5 dialect variants, uses a translation specific pipeline with MT oriented pre training, supervised fine tuning, on policy distillation and RL, delivers benchmark performance close to or above Gemini 3.0 Pro on Flores 200, WMT25 and Mandarin minority tests, and ships FP8, Int4 and GGUF variants so teams can deploy a terminology aware, context aware and format preserving translation system on both 1 GB class edge devices and standard cloud LLM infra. Read the full analysis/article here.

LLM-Pruning Collection is a JAX based Apache 2.0 repository from zlab princeton that unifies modern LLM pruning methods such as Minitron, ShortGPT, Wanda, SparseGPT, Sheared LLaMA, Magnitude and LLM Pruner into a single repo, with shared pruning, FMS FSDP and MaxText training pipelines and JAX compatible lm eval harness evaluation, plus side by side paper versus reproduced tables that let engineers systematically compare sparsity patterns and accuracy tradeoffs on Llama family models without stitching together multiple research repos. Read the full analysis/article here.

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