Abstract
Multi-agent systems (MAS) enable complex reasoning by coordinating multiple agents, but often incur high inference latency due to multi-step execution and repeated model invocations, severely limiting their scalability and usability in time-sensitive scenarios. Most existing approaches primarily optimize task performance and inference cost, and explicitly or implicitly assume sequential execution, making them less optimal for controlling latency under parallel execution. In this work, we investigate learning-based orchestration of multi-agent systems with explicit latency supervision under parallel execution. We propose Latency-Aware Multi-agent System (LAMaS), a latency-aware multi-agent orchestration framework that enables parallel execution and explicitly optimizes the critical execution path, allowing the controller to construct execution topology graphs with lower latency under parallel execution. Our experiments show that our approach reduces critical path length by 38-46% compared to the state-of-the-art baseline for multi-agent architecture search across multiple benchmarks, while maintaining or even improving task performance. These results highlight the importance of explicitly optimizing latency under parallel execution when designing efficient multi-agent systems. The code is available at this https URL
Abstract (translated)
多智能体系统（MAS）通过协调多个代理来实现复杂的推理，但通常由于多步执行和重复模型调用而导致高推断延迟，这严重限制了它们在时间敏感场景中的可扩展性和可用性。大多数现有的方法主要优化任务性能和推断成本，并且明确或隐含地假设顺序执行，使得这些方法在并行执行时控制延迟的效果较差。 在这项工作中，我们研究了在并行执行中带有显式延迟监督的基于学习的多智能体系统编排。我们提出了一个具有意识的延迟多代理系统（LAMaS），这是一种延迟感知的多代理编排框架，它能够进行并行执行，并明确地优化关键执行路径，使得控制器能够在并行执行时构建延迟较低的执行拓扑图。 我们的实验表明，在多个基准测试中，与最先进的多代理架构搜索基线相比，我们所提出的方法在保持或甚至改进任务性能的同时，可以将关键路径长度减少38-46%。这些结果突显了在设计高效的多智能体系统时明确优化并行执行中的延迟的重要性。 代码可在提供的链接处获取。
URL
https://arxiv.org/abs/2601.10560
