AI 中文总结
针对多智能体系统推理延迟高的问题，提出延迟感知多智能体系统（LAMaS）。训练时通过约束优化学习延迟感知执行图，推理时用轻量级控制器补充。实验显示其在降低端到端延迟超50%的同时保持精度，且具模块化可移植性。
AI 中文摘要
多智能体系统（MAS）通过结构化工作流程协调多个由大语言模型驱动的智能体，虽获得推理能力，但因多步执行和重复模型调用导致推理延迟高。现有编排方法主要优化任务性能和推理成本，未解决延迟问题。我们提出延迟感知多智能体系统（LAMaS），在训练时通过带关键路径感知信用分配的约束优化学习延迟感知执行图，推理时用轻量级控制器补充图构建以消除冗余交互。实验表明LAMaS在基于学习的MAS基线中延迟最佳，端到端延迟降低超50%且保持竞争力或更好精度，还具模块化可移植性。
英文摘要
Multi-agent systems (MAS) coordinate multiple LLM-powered agents through structured workflows, gaining reasoning power but incurring high inference latency from multi-step execution and repeated model invocations. Existing orchestration methods primarily optimize task performance and inference cost, leaving latency largely unaddressed. In MAS, end-to-end latency is governed by the critical execution path, so reducing total cost alone does not reliably reduce latency. Moreover, optimizing latency while preserving accuracy remains non-trivial: naive latency optimization can misassign operator-level credit and degrade task accuracy. To address this gap, we propose Latency-Aware Multi-agent System (LAMaS), a latency-aware orchestration framework for learning-based multi-agent systems. LAMaS addresses this challenge at two levels: at training time, it learns latency-aware execution graphs through constrained optimization with critical-path-aware credit assignment; at inference time, since a graph committed at training time cannot exploit runtime evidence, it complements graph construction with a lightweight controller that adaptively eliminates redundant future agent interactions as execution unfolds. Experiments on four benchmarks show that LAMaS achieves the best latency among evaluated learning-based MAS baselines, reducing end-to-end latency by over 50\% while maintaining competitive or better accuracy. LAMaS is also modular and transfers to other MAS with minimal changes, consistently yielding latency reductions.
CommentsThis submission was intended to be an updated version of arXiv:2601.10560. Any subsequent updates will appear there
