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The Sequence Knowledge #858: How State Space Models Went from Curiosity to Serious Transformer Competitor

State space models (SSMs) are emerging as a viable alternative to Transformers, offering linear time complexity and constant memory during inference. This article explores the mathematical foundations, recent breakthroughs, and how SSMs now compete on key language tasks.

SourceTheSequenceAuthor: Jesus Rodriguez
The Sequence Knowledge #858: How State Space Models Went from Curiosity to Serious Transformer Competitor
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Under the source content permission, this page provides an editorial summary, key points, and a link to the original article.

Key points and analysis

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Key points

  • Transformer self-attention suffers from O(n²) complexity, limiting long-context scalability.
  • State space models achieve linear complexity with no KV-cache, enabling efficient inference.
  • As of March 2026, SSMs match Transformers on perplexity, in-context learning, and reasoning.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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