{
  "@context": "https://schema.org",
  "@type": "TechArticle",
  "@id": "https://anchorfact.org/kb/transformer-architecture-variants",
  "headline": "Transformer Variants: From Encoder-Decoder to Mamba State Space Models",
  "description": "While the Transformer architecture dominates AI, alternatives are emerging. State Space Models (Mamba, Jamba) promise linear complexity for long sequences, challenging attention's O(n²) bottleneck.",
  "dateCreated": "2026-05-24T02:49:13.671Z",
  "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": "Attention Is All You Need",
      "sameAs": "https://arxiv.org/abs/1706.03762"
    }
  ]
}