MOSAIC: Efficient Mixture-of-Agent Scheduling via Adaptive Aggregation and Inference Concurrency
arXiv:2606.03014v1 Announce Type: new Abstract: Mixture-of-Agents (MoA) systems improve reasoning accuracy by routing each query to multiple expert LLMs and aggregating their outputs. Efficiently executing this workload on limited GPU resources has bottlenecks. Skill-based routing creates skewed expert demand, and combining instruction-tuned LLMs with long-reasoning models results in extreme variability in generation lengths. Consequently, traditional scheduling strategies suffer from significant GPU idling and throughput collapse due to load imbalances. We present MOSAIC, a scheduling framewo
The increasing complexity and resource demands of Mixture-of-Agents (MoA) systems are exposing critical bottlenecks in current GPU resource management, necessitating immediate solutions.
Efficient scheduling of MoA systems addresses a major constraint for scaling AI agentic workloads, directly impacting the feasibility and cost-effectiveness of advanced AI deployments.
The ability to more efficiently utilize GPU resources for complex AI agent systems will accelerate the development and deployment of sophisticated AI applications, making them more commercially viable.
- · AI compute providers
- · Developers of AI agent frameworks
- · Companies implementing AI agents
- · GPU manufacturers
- · Inefficient AI scheduling solutions
- · Organizations with suboptimal compute architectures
Improved resource utilization will reduce operational costs and increase the throughput of AI agent systems.
The cost-effectiveness will drive broader adoption and integration of advanced AI agents into various industries.
Accelerated AI agent deployment could further concentrate AI capabilities within organizations optimized for advanced compute management.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG