{
  "id": "mixture-of-agents",
  "code": "PTL-0052",
  "term": "Mixture-of-Agents",
  "aliases": [
    "MoA"
  ],
  "category": "verification",
  "definition": "Mixture-of-Agents (MoA) arranges language models in layers, where each model receives all outputs from the previous layer as auxiliary input and an aggregator synthesizes a final response.",
  "description": "Wang et al. reported that a mixture of open models outperformed a single strong proprietary model on an instruction-following benchmark.",
  "example": null,
  "broader": [],
  "narrower": [],
  "related": [
    "multi-agent-debate",
    "parallelization"
  ],
  "introduced": 2024,
  "sources": [
    {
      "title": "Mixture-of-Agents Enhances Large Language Model Capabilities",
      "authors": "Wang et al.",
      "year": 2024,
      "url": "https://arxiv.org/abs/2406.04692"
    }
  ],
  "url": "https://protologue.com/t/mixture-of-agents/",
  "citation": "Protologue. (2026). Mixture-of-Agents. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0052). https://protologue.com/t/mixture-of-agents/"
}