RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents
Mixture-of-Agents (MoA) improves LLM performance through layered collaboration, but its dense topology raises costs and latency. Existing methods employ LLM judges to filter responses, yet still require all models to perform inference before judging, failing to cut costs effectively. They also lack model selection criteria and struggle with large model pools, where full inference is costly and can exceed context limits. To address this, we propose RouteMoA, an efficient mixture-of-agents framework with dynamic routing. It employs a lightweight scorer to perform initial screening by predicting coarse-grained performance from the query, narrowing candidates to a high-potential subset without inference. A mixture of judges then refines these scores through lightweight self- and cross-assessment based on existing model outputs, providing posterior correction without additional inference. Finally, a model ranking mechanism selects models by balancing performance, cost, and latency. RouteMoA outperforms MoA across varying tasks and model pool sizes, reducing cost by 89.8% and latency by 63.6% in the large-scale model pool.
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Jize Wang
admin_assigned
2026-01-27T14:40:22.251Z
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Jize
Jize1
user
Jize1
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Han Wu
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Zhiyuan You
admin_assigned
2026-01-27T14:40:28.630Z
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zhiyuanyou
zhiyuanyou
user
zhiyuanyou
697835b2026bdf0473116ebb
Yiming Song
697835b2026bdf0473116ebc
Yijun Wang
697835b2026bdf0473116ebd
Zifei Shan
697835b2026bdf0473116ebe
Yining Li
claimed_verified
2026-04-16T20:22:52.454Z
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Yining Li
ly015
user
ly015
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Songyang Zhang
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Xinyi Le
admin_assigned
2026-01-27T14:40:47.725Z
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Xinyi Le
larryle
user
larryle
697835b2026bdf0473116ec1
Cailian Chen
697835b2026bdf0473116ec2
Xinping Guan
697835b2026bdf0473116ec3
Dacheng Tao
RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents
Mixture-of-Agents (MoA) improves LLM performance through layered collaboration, but its dense topology raises costs and latency. Existing methods employ LLM judges to filter responses, yet still require all models to perform inference before judging, failing to cut costs effectively. They also lack model selection criteria and struggle with large model pools, where full inference is costly and can exceed context limits. To address this, we propose RouteMoA, an efficient mixture-of-agents framework with dynamic routing. It employs a lightweight scorer to perform initial screening by predicting coarse-grained performance from the query, narrowing candidates to a high-potential subset without inference. A mixture of judges then refines these scores through lightweight self- and cross-assessment based on existing model outputs, providing posterior correction without additional inference. Finally, a model ranking mechanism selects models by balancing performance, cost, and latency. RouteMoA outperforms MoA across varying tasks and model pool sizes, reducing cost by 89.8% and latency by 63.6% in the large-scale model pool.
RouteMoA reduces computational costs and latency in mixture-of-agents frameworks by using dynamic routing with lightweight scoring and judgment mechanisms.
mixture-of-agents LLM judges dynamic routing lightweight scorer model ranking mechanism coarse-grained performance posterior correction inference efficiency