MA-Mesh 提出基于角色自愈的多智能体协作框架
MA-Mesh: Resilient Multi-Agent Collaboration via Role-based Self-Healing
多智能体系统一挂就崩的问题有解了：MA-Mesh 让 Agent 能互相评估表现，挂了还能自动换角色顶上，做 Agent 开发的可以看看。
arXiv 论文提出 Multi-Agent Mesh（MA-Mesh），在协作模式之上构建基于角色的自愈层。框架将韧性感知的系统规格合成为可执行的多智能体策略代码。核心机制有两个：跨智能体反思（Cross-agent reflection）让 Agent 评估同伴表现并集体调整策略；基于角色的故障转移（Role-based failover）在子任务失败或某个 Agent 表现不佳时动态重新分配角色。实验覆盖软件开发、工具调用、科学推理等任务，MA-Mesh 在任务性能和不确定性下的鲁棒性上均优于现有多智能体系统。
MA-Mesh: Resilient Multi-Agent Collaboration via Role-based Self-Healing
Effective collaboration in multi-agent systems requires mechanisms for coordination and adaptability in dynamic and uncertain environments. However, existing multi-agent systems rarely model resilience explicitly, instead relying on ad-hoc output correction that limits systematic, runtime management of robustness. In this work, we propose Multi-Agent Mesh (MA-Mesh), a framework that constructs a role-based self-healing layer on top of a collaboration pattern. Specifically, MA-Mesh generates resilience-aware system specifications and synthesizes them into executable multi-agent policy code. To enhance resilience, we introduce two self-healing mechanisms: Cross-agent reflection, a mechanism that allows agents to evaluate not only their own outcomes but also the performance of their peers, enabling informed behavioral adjustments and collective policy refinement; Role-based failover, a strategy which dynamically reassigns roles and enables agents to substitute for one another when sub-tasks fail or agents underperform, ensuring continuity of task execution. Experimental results across diverse tasks (e.g., software development, tool use, scientific reasoning) demonstrate that MA-Mesh significantly outperforms existing multi-agent systems in both task performance and robustness under system uncertainty.