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research#llm📝 BlogAnalyzed: Jan 15, 2026 08:00

DeepSeek AI's Engram: A Novel Memory Axis for Sparse LLMs

Published:Jan 15, 2026 07:54
1 min read
MarkTechPost

Analysis

DeepSeek's Engram module addresses a critical efficiency bottleneck in large language models by introducing a conditional memory axis. This approach promises to improve performance and reduce computational cost by allowing LLMs to efficiently lookup and reuse knowledge, instead of repeatedly recomputing patterns.
Reference

“DeepSeek’s new Engram module targets exactly this gap by adding a conditional memory axis that works alongside MoE rather than replacing it.”

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Research#llm📝 BlogAnalyzed: Dec 24, 2025 08:43

AI Interview Series #4: KV Caching Explained

Published:Dec 21, 2025 09:23
1 min read
MarkTechPost

Analysis

This article, part of an AI interview series, focuses on the practical challenge of LLM inference slowdown as the sequence length increases. It highlights the inefficiency related to recomputing key-value pairs for attention mechanisms in each decoding step. The article likely delves into how KV caching can mitigate this issue by storing and reusing previously computed key-value pairs, thereby reducing redundant computations and improving inference speed. The problem and solution are relevant to anyone deploying LLMs in production environments.
Reference

“Generating the first few tokens is fast, but as the sequence grows, each additional token takes progressively longer to generate”

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