arXiv:2606.10662v1 Announce Type: cross Abstract: Multi-agent systems (MAS) can scale large language model reasoning at test time by decomposing complex problems into parallel subtasks. However, most existing MAS rely on centralized orchestration, where a main agent assigns work, collects outputs, and merges results. As the number of subtasks grows, this controller becomes a communication and integration bottleneck. We propose Decentralized Language Models (DeLM), a MAS framework that decentralizes coordination through parallel agents, a shared verified context, and a task queue. Agents asynch
The increasing complexity and scale of problems assigned to Large Language Models are necessitating more sophisticated and efficient multi-agent coordination architectures.
This research addresses fundamental scaling and bottleneck issues in multi-agent AI systems, which are crucial for the development of more capable and robust autonomous AI agents.
The shift from centralized to decentralized multi-agent system orchestration could enable more resilient, scalable, and powerful AI applications by eliminating single points of failure and communication bottlenecks.
- · AI software developers
- · Cloud computing providers
- · Enterprises adopting AI agents
- · Legacy centralized AI orchestration platforms
- · Inefficient monolithic AI systems
More complex and autonomous AI systems become technically feasible.
Accelerated deployment of AI agents across various industries due to improved scalability and reliability.
Increased demand for distributed computing resources and secure, verified shared contexts for AI coordination.
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Read at arXiv cs.AI