ENGRAM: HOW DEEPSEEK ADDED A SECOND BRAIN TO THEIR LLM

Summary from the TLDR AI newsletter

DeepSeek's Engram architecture introduces a conditional memory system that uses lookup tables for common N-gram patterns, significantly reducing computational overhead in LLMs. This approach boosts performance on knowledge benchmarks and reasoning tasks by efficiently freeing up neural resources for complex reasoning. Engram's design leverages tokenizer compression, multi-head hashing, and context-aware gating, providing a marked improvement over MoE baselines without substantial memory impact.