# self-healing for gray failures in decentralized LLM agent networks

The work introduces MeshHeal, a fully decentralized self-healing framework that couples ability-matched peer review across two timescales to address gray failures in decentralized LLM-based multi-agent systems: 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, with recovery probes providing fresh evidence for reintegration; the authors also introduce Model-Backed MAS Evaluation, which ti

The work introduces MeshHeal, a fully decentralized self-healing framework that couples ability-matched peer review across two timescales to address gray failures in decentralized LLM-based multi-agent systems: 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, with recovery probes providing fresh evidence for reintegration; the authors also introduce Model-Backed MAS Evaluation, which ti