---
title: "MA-Mesh: Resilient Multi-Agent Collaboration via Role-based Self-Healing"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.06389v1"
permalink: "https://feed.astralor.com/items/itm_e8b4dfe9ab9a7df5fe8bc0f6"
published: "2026-11-04T11:54:59.000Z"
score: 59
channel: "tech"
tags: ["AI Agent","MA-Mesh","Resilient Multi-Agent Collaboration","Role-based Self-Healing Effective","However","Effective","Multi-Agent Mesh"]
---

# MA-Mesh: Resilient Multi-Agent Collaboration via Role-based Self-Healing

## 速览

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, inste…

## 关键点

- 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.

## 全文

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.

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