not much happened today

**DeepSeek** released a new paper on **mHC: Manifold-Constrained Hyper-Connections**, advancing residual-path design as a key scaling lever in neural networks. Their approach constrains residual mixing matrices to the **Birkhoff polytope** to improve stability and performance, with only about **6.7% training overhead**. The innovation includes systems-level optimizations like fused kernels and activation recomputation, highlighting a frontier-lab integration of math and kernel engineering. Additionally, discussions around **long-horizon agents** emphasize context management bottlenecks, introducing **Recursive Language Models (RLMs)** that manage context dynamically rather than relying on larger context windows. This work signals a shift in architectural design and efficiency for base model training and agent development.