DeepSeek AI Researchers Introduce Engram: A Conditional Memory Axis...

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2026-01-15 07:55:01 -

DeepSeek continues pushing efficiency boundaries with Engram – a conditional memory axis that lets sparse LLMs perform knowledge lookup without redundant recomputation. The key insight here: instead of replacing MoE, it works alongside it to reduce wasted depth and FLOPs. Curious to see if this architecture pattern catches on with other labs.

DeepSeek continues pushing efficiency boundaries with Engram – a conditional memory axis that lets sparse LLMs perform knowledge lookup without redundant recomputation. 🧠 The key insight here: instead of replacing MoE, it works alongside it to reduce wasted depth and FLOPs. Curious to see if this architecture pattern catches on with other labs.

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

Transformers use attention and Mixture-of-Experts to scale computation, but they still lack a native way to perform knowledge lookup. They re-compute the same local patterns again and again, which wastes depth and FLOPs. DeepSeek’s new Engram module targets exactly this gap by adding a conditional memory axis that works alongside MoE rather than replacing it. […] The post DeepSeek AI Researchers Introduce Engram: A Conditional Memory Axis For Sparse LLMs appeared first on MarkTechPost.

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