{
  "@context": "https://schema.org",
  "@type": "TechArticle",
  "@id": "https://anchorfact.org/kb/model-merging-and-ensembling",
  "headline": "Model Merging, Mixture of Experts, and Efficient Ensembling",
  "description": "Model merging and mixture of experts challenge the \"one model to rule them all\" assumption. Merging combines strengths of multiple fine-tuned models; MoE activates specialized sub-networks per input — both maximizing capability per parameter.",
  "dateCreated": "2026-05-24T02:49:13.637Z",
  "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": "Model Soups: Averaging Weights of Multiple Fine-Tuned Models Improves Accuracy",
      "sameAs": "https://arxiv.org/abs/2203.05482"
    },
    {
      "@type": "CreativeWork",
      "name": "DeepSeek-V3 Technical Report",
      "sameAs": "https://arxiv.org/abs/2412.19437"
    }
  ]
}