LAMaS: Latency-Aware Orchestration for Multi-Agent Systems

Jul 16, 2026

A new framework called LAMaS is proposed for orchestrating multi-agent systems with a focus on reducing end-to-end latency. LAMaS combines constrained optimization and critical-path-aware credit assignment during training with a lightweight controller at inference time to adaptively eliminate redundant agent interactions. Experiments across four benchmarks show that LAMaS reduces latency by over 50% compared to existing learning-based baselines, while maintaining competitive or better accuracy. The approach is modular and transfers easily to other multi-agent systems.

Why it matters: This work addresses the significant challenge of inference latency in multi-agent systems, enabling faster and more efficient coordination without sacrificing accuracy.

Full story at: arXiv Multiagent Systems ↗