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2026-01-15 07:55:01 -

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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.

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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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