Adamas makes giant texts fast — read more, wait less
Meet Adamas, a new way for language models to handle very long text without slowing down.
It helps models work with long documents like books, big reports or long code files, and still remember the right parts.
The trick is a compact way to squash and sort the important bits so the model only looks where it matters.
That means much more speed during reading and replying, while keeping the same sense of what’s important.
On real tests Adamas kept nearly the same answers as the full, slow method but used far less work.
It even kept or improved accuracy when pushed hard.
In practice this means models can run faster on long chats or summaries, cost less, and still give good results.
Some setups showed big boosts — up to many times faster with much less memory use.
It’s a practical idea to make big-context AI tools feel snappier, without trading away the quality you care about.
Read article comprehensive review in Paperium.net:
Adamas: Hadamard Sparse Attention for Efficient Long-Context Inference
🤖 This analysis and review was primarily generated and structured by an AI . The content is provided for informational and quick-review purposes.
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