MeshHeal: Two-Timescale Self-Healing for Gray Failures in Decentralized LLM Agent Networks

原文：MeshHeal: Two-Timescale Self-Healing for Gray Failures in Decentralized LLM Agent Networks

作者：Keru Chen, Sen Lin, Yingbin Liang, Nathaniel D. Bastian, Shaofeng Zou

来源：arXiv cs.DC（分布式并行）

正文

Computer Science > Artificial Intelligence

arXiv:2609.29015v1 (cs)

[Submitted on 24 Sep 2026]

Title:MeshHeal: Two-Timescale Self-Healing for Gray Failures in Decentralized LLM Agent Networks

Authors:Keru Chen, Sen Lin, Yingbin Liang, Nathaniel D. Bastian, Shaofeng Zou

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Abstract:Decentralized LLM-based multi-agent systems coordinate through local interactions, but an agent can remain responsive while its task-solving quality persistently degrades. Such gray failures require protecting current tasks before sufficient evidence exists to alter future routing, while still allowing recovered agents to rejoin. We introduce MeshHeal, a fully decentralized self-healing framework that couples ability-matched peer review across two timescales. At the fast timescale, an adaptive hierarchy escalates uncertain or low-scoring outputs from repeated single-reviewer evaluation to committee deliberation and, when needed, correction before use. At the slow timescale, a task- and ability-conditioned peer-relative detector aggregates scores to distinguish persistent degradation from ordinary output variation, trigger mandatory committee review, and eventually exclude degraded agents from ordinary routing; recovery probes provide fresh evidence for reintegration. To faithfully evaluate routing, we introduce Model-Backed MAS Evaluation, which ties ability assignments to execution models, since prompt-based ability assignments alone can leave routing errors hidden. Across BBH, MATH, and MMLU-Pro, MeshHeal achieves 0.839 degraded-phase accuracy using 51k total model tokens per task, versus the strongest baseline Symphony’s 0.807 accuracy using 115k per task. Under staggered degradation and recovery, MeshHeal isolates degraded agents, keeps them excluded from ordinary task execution until recovery, and returns them to normal routing.

Comments:

31 pages

Subjects:

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)

Cite as:

arXiv:2609.29015 [cs.AI]

(or

arXiv:2609.29015v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.29015

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arXiv-issued DOI via DataCite (pending registration)

主题

人工智能 · 自然语言处理 · 分布式计算

由「前沿雷达」于 2026-09-24 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。