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# DeepSeek Just Fixed One Of The Biggest Problems With AI

**[Two Minute Papers](https://daily.dev/sources/twoninutepapers)** · 9 min read · 0 upvotes · 0 comments

## Summary

DeepSeek researchers introduced a technique called Engram that adds a memory retrieval mechanism (like a pantry) to transformer-based AI models. Instead of recomputing facts from scratch every time, the model can look up stored n-gram embeddings via multi-head hashing. Surprisingly, replacing 20-25% of the mixture-of-experts layers with this simple lookup table not only improves efficiency but also improves accuracy across all benchmarks. A context-aware gating mechanism filters out irrelevant retrieved memories. Ablation tests showed that disabling Engram dropped trivia accuracy by 70% while reading comprehension stayed at 93%, suggesting the model uses Engram specifically for factual storage. The authors argue this could lead to cheaper, locally-runnable AI systems.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.youtube.com/watch?v=DmtoVnTkQnM>

## Similar posts on daily.dev

- [DeepSeek may have found a new way to improve AI’s ability to remember](https://daily.dev/posts/deepseek-may-have-found-a-new-way-to-improve-ai-s-ability-to-remember-qw9udazov) · MIT Technology Review · 0 upvotes · 0 comments

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Tags: [#deep-learning](https://daily.dev/tags/deep-learning), [#neural-networks](https://daily.dev/tags/neural-networks), [#deepseek](https://daily.dev/tags/deepseek)

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