arXiv:2604.02863v2 Announce Type: replace 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-Condit
The proliferation of multi-agent AI systems necessitates more efficient decision-making mechanisms to manage increasing computational demands, making this research timely.
This work introduces a novel method to significantly reduce the computational overhead of multi-agent systems, improving their efficiency and scalability for complex tasks.
Traditional multi-agent aggregation requiring all agents to complete reasoning before decision-making can be replaced with a more dynamic, resource-aware approach, optimizing computational resources.
- · AI developers
- · Cloud computing providers
- · Organizations deploying multi-agent systems
- · Traditional, less efficient multi-agent system architectures
Multi-agent systems will become more computationally efficient, reducing operational costs.
The improved efficiency could enable more widespread and complex deployments of AI agents in various industries.
This could accelerate the development of autonomous AI systems, potentially leading to more sophisticated and pervasive AI agentic applications across the economy.
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Read at arXiv cs.AI