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[2312.00752] Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://arxiv.org/abs/2312.00752
6
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6
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2026-07-22 09:17:12
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arxiv.org
Text Highlight2026-07-22 15:41:12
Original Highlight Excerpt
"arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website."
Whisper Note
Sounds like a cool way for devs to pitch in, but I wonder how much actual control arXiv keeps.
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arxiv.org
Text Highlight2026-07-22 12:38:12
Original Highlight Excerpt
"Many subquadratic-time architectures"
Whisper Note
tried mamba on a long audio task, the speed bump was actually noticeable
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arxiv.org
Text Highlight2026-07-22 12:29:12
Original Highlight Excerpt
"Many subquadratic-time architectures"
Whisper Note
subquadratic stuff keeps popping up but attention just won't die, curious if mamba's the real deal
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arxiv.org
Text Highlight2026-07-22 09:35:12
Original Highlight Excerpt
"Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer"
Whisper Note
Honestly, attention is the bottleneck, so any subquadratic alternative is worth a shot.
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arxiv.org
Text Highlight2026-07-22 09:26:12
Original Highlight Excerpt
"Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer"
Whisper Note
I've seen Mamba's benchmarks, it's impressive but let's see how it holds up in production.
arxiv.org favicon
arxiv.org
Text Highlight2026-07-22 09:17:12
Original Highlight Excerpt
"Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer"
Whisper Note
Transformers are great but the quadratic attention cost is a real pain for long sequences.

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