📌 DeepSeek Just Gave LLMs Real Memory (Engram Explained)

What if one of the biggest inefficiencies in modern LLMs is that they are forced to compute things they should simply remember?

DeepSeek's latest paper, Engram: Conditional Memory via Scalable Lookup, introduces a new axis of sparsity for large language models. While Mixture-of-Experts scales computation, Engram scales memory - a parametric, differentiable lookup table embedded inside the transformer that retrieves static facts in O(1) time, independent of sequence length.

In this video we break down how Engram works, why context-aware gating makes it robust, the U-shaped Sparsity Allocation Law (the sweet spot is ~20-25% memory, 75-80% MoE), and why offloading 100B-parameter memory tables to CPU could reshape the economics of scaling. We also look at why a memory module surprisingly boosts reasoning, math, and long-context benchmarks - not just factual recall.

If this sparked a thought, hit like and subscribe for more paper breakdowns on how modern LLMs actually work under the hood.


Full Post https://docs.doc-vision.com/blog/engram-deepseek-conditional-memory

#DeepSeek #LLM #Engram #MixtureOfExperts #AIResearch