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Mixture of Agents(MoA)
Layered ensemble in which multiple LLM proposer agents at each layer receive all responses from the previous layer as auxiliary context, iteratively refining their answers before a final aggregator synthesizes them. Because models produce better answers when shown peer responses, a stack of open-source proposers can outperform a single frontier model.
In 30 seconds
- What
- Stack of LLM agents iteratively refines answers by conditioning each layer on all prior-layer outputs, then aggregates into a final response.
- When to use
- Complex problems where diverse model perspectives improve quality and open-source models together can match or exceed frontier model performance.
- Watch out
- Latency and cost scale linearly with layers and proposers; diminishing returns often plateau after 2-3 layers, wasting compute.
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Mixture of Agents: Overview
Layered ensemble in which multiple LLM proposer agents at each layer receive all responses from the previous layer as auxiliary context, iteratively refining their answers before a final aggregator synthesizes them. Because models produce better answers when shown peer responses, a stack of open-source proposers can outperform a single frontier model.
- Layered proposer-aggregator architecture
- Each layer conditions on all prior-layer outputs
- Iterative cross-model refinement
- Aggregator synthesizes a best-of-all response
- Works with heterogeneous open-source models
- Exploits model collaborativeness (peers improve output)
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References
The papers, specifications, and repositories this pattern is based on.
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