EMS: Multi-Agent Voting via Efficient Majority-then-Stopping

ACL ARR 2026 May Submission17057 Authors

Keywords: Multi-Agent System ; Multi-Agent Voting ; Efficient Majority-then-Stopping

Abstract:

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.

Paper Type: Long

Research Area: LLM agents

Research Area Keywords: AI / LLM Agents

Contribution Types: Model analysis & interpretability

Languages Studied: english

EMNLP 2026 AI Reviewing Experiment: yes

Submission Number: 17057