{
  "@context": "https://schema.org",
  "@type": "article",
  "@id": "https://anchorfact.org/kb/state-space-models",
  "headline": "State Space Models: Mamba, Linear-Time Sequence Modeling, and Alternatives to Transformers",
  "description": "State Space Models (SSMs), particularly Mamba, offer a linear-complexity alternative to Transformer attention — processing sequences in O(N) time instead of O(N²). By making SSM parameters input-dependent (selective SSMs), Mamba achieves Transformer-competitive quality with dramatically faster inference on long sequences.",
  "dateCreated": "2026-05-24T02:49:13.662Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces",
      "sameAs": "https://arxiv.org/abs/2312.00752"
    },
    {
      "@type": "CreativeWork",
      "name": "Mamba-2: Structured State Space Duality",
      "sameAs": "https://arxiv.org/abs/2405.21060"
    }
  ]
}