DeepSeek AI Researchers Introduce Engram: A Conditional Memory Axis For Sparse LLMs

DeepSeek researchers introduce Engram, a conditional memory axis for sparse LLMs. Current transformers using attention and Mixture-of-Experts lack native knowledge retrieval mechanisms, repeatedly computing identical local patterns inefficiently. Engram addresses this gap by adding a conditional memory axis alongside MoE, enabling better knowledge lookup without replacing existing architectures and reducing computational waste.