International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Leveraging Large Language Models in Multiagent System

Author(s) Ms. Priyanshi saxena, Roshan Lal
Country India
Abstract The combination of Large Language Models (LLMs) and Multi-Agent Systems (MAS) represents an artificial intelligence paradigm that empowers decentralized agents to reason, communicate, and coordinate with human-level flexibility. This review integrates the progress of LLM-based MAS, with focus on architectural innovation like neuro- symbolic architectures and decentralized coordination techniques, for improvement in healthcare, autonomous systems, and smart cities. 20 foundation studies analysis shows uses such as diagnostic error prevention (32%) and autonomous vehicle crash avoidance (37%). Challenges remain, however, such as computational expense (e.g., tenfold cloud costs for 100-agent systems), ethical hazards (e.g., 34% recruitment simulation bias), and latency problems (500–800ms decision-making delay). Scalability limitations also occur due to energy wastage in edge deployment. Priorities in the future are light-weight LLM models for real-time applications, global governance paradigms to tackle regulatory fragmentation, and inter-disciplinary collaboration to promote ethical accountability. With the appropriate balance between innovation and societal justice, LLM- based MAS can become effective instruments for scalable, human-centered problem-solving.
Keywords Large Language Models (LLMs), Multi-Agent Systems (MAS), Neuro-Symbolic Reasoning, Ethical Governance, Human-AI Collaboration.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-04-15
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.41697
Short DOI https://doi.org/g9fmx6
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