arXiv:2606.00953v1 Announce Type: new Abstract: Multi-agent Large Language Model (LLM) systems offer a way to decompose complex tasks, such as coding, through parallelization and context isolation. However, adding agents in practice introduces inter-agent communication overhead, which incurs extra cost and can sometimes offset the efficiency gains. We formalize multi-agent orchestration as a graph partitioning problem that captures the communication-to-computation trade-off: task decomposition can shorten critical-path computation, but cross-agent dependencies require costly context transfer.
The proliferation of multi-agent LLM systems necessitates efficient orchestration, and this research addresses a critical bottleneck: communication overhead.
Optimizing multi-agent LLM performance is crucial for advancing AI capabilities and integrating them into complex workflows, impacting productivity and the scope of automation.
Approaches to deploying multi-agent systems will evolve to explicitly account for communication costs, leading to more efficient and scalable AI-driven solutions.
- · AI developers
- · Software engineering teams
- · Cloud providers
- · Companies adopting AI agents
- · Inefficient multi-agent system designs
Improved performance and cost-effectiveness of multi-agent Large Language Model systems.
Accelerated adoption of AI agents for complex coding and software development tasks.
Automation scales faster across industries as AI agent systems become more robust and less resource-intensive.
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Read at arXiv cs.LG