让智能体按可靠度排队投票，早停省资源
EMS: Multi-Agent Voting via Efficient Majority-then-Stopping
- 按历史表现排序智能体，优先调用更可靠的
- 一旦多数结果无法被后续投票推翻就立即停止
- 减少35%调用次数，44%耗能，适合高效推理场景
多数投票是聚合多智能体响应的常用方法。但传统方法需等待所有智能体完成推理才开始聚合，造成大量冗余计算。本文将高效多智能体投票建模为可靠性感知的调度问题，提出高效先多数后停止（EMS）方法：首先通过检索语义相似查询的历史共识证据，对每个智能体构建任务条件可靠性排序（TCRO），并按降序调用；接着采用自适应增量投票（AIV），在当前领先答案无法被剩余智能体改变时提前终止；最后仅更新参与调用的智能体的可靠性历史。在五个基准测试上验证显示，EMS在保持多数投票准确率的同时，平均调用智能体数减少35%，令牌消耗降低44%。代码已开源。
原文摘要 · Abstract (English)
Majority voting is the standard for aggregating multi-agent responses into a final decision. However, traditional methods typically require all agents to complete their reasoning before aggregation begins, leading to significant computational overhead, as many responses become redundant once a majority consensus is achieved. In this work, we formulate efficient multi-agent voting as a reliability-aware agent scheduling problem and propose Efficient Majority-then-Stopping (EMS) to improve reasoning efficiency. EMS first estimates a Task-Conditioned Reliability Ordering (TCRO) for each agent by retrieving its historical consensus evidence on semantically similar queries, and then invoking agents in descending reliability order. Next, Adaptive Incremental Voting (AIV) terminates the process once the current leading answer cannot be overturned by any possible votes from the remaining agents, and returns this answer. Finally, Reliability History Updating (RHU) updates only the invoked agents according to their consensus with the final decision. Extensive evaluations across five benchmarks show that EMS preserves the accuracy of Majority Voting while reducing the average number of invoked agents by 35% and token consumption by 44%, respectively. The code is available at https://github.com/fuyu66/EMS.
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