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Journal articles on the topic 'Multi-agent LLM systems'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Ma, Jianxiang. "Research on the role of LLM in multi-agent systems: A survey." Applied and Computational Engineering 71, no. 1 (2024): 180–86. http://dx.doi.org/10.54254/2755-2721/71/20241674.

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In recent years, the rapid development of large language model (LLM) has demonstrated superior performance in language understanding, text generation, planning, reasoning, and knowledge integration. This has led to the emergence of intelligent agents based on LLM. By leveraging the capabilities of LLM, these agents can effectively make decisions based on given objectives and possess certain learning and adaptation abilities. However, single-agent systems are generally suited to solving relatively simple problems and are limited in handling complex tasks that require coordination. For instance,

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Kalyuzhnaya, Anna, Sergey Mityagin, Elizaveta Lutsenko, et al. "LLM Agents for Smart City Management: Enhancing Decision Support Through Multi-Agent AI Systems." Smart Cities 8, no. 1 (2025): 19. https://doi.org/10.3390/smartcities8010019.

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This study investigates the implementation of LLM agents in smart city management, leveraging both the inherent language processing abilities of LLMs and the distributed problem solving capabilities of multi-agent systems for the improvement of urban decision making processes. A multi-agent system architecture combines LLMs with existing urban information systems to process complex queries and generate contextually relevant responses for urban planning and management. The research is focused on three main hypotheses testing: (1) LLM agents’ capability for effective routing and processing diver

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Liu, Miao, Qing Shao, and Guangzeng Xie. "Multi-Agent Approach to Political Discourse Translation: From Large Language Models to MAGIC-PTF System." Litera, no. 11 (November 2024): 28–46. http://dx.doi.org/10.25136/2409-8698.2024.11.72197.

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This research addresses the automated translation of Chinese political discourse into Russian utilizing Large Language Model (LLM) optimization technologies and a multi-agent approach. The study focuses on developing the MAGIC-PTF system, which implements multi-stage text processing through the interaction of four specialized agents. The system's key component is the Style Agent, which ensures stylistic uniformity and terminological accuracy based on a specifically trained LLM. The Translator Agent performs the primary translation work and is responsible for the final text formatting. The Edit

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Mediakov, O. O., and M. Yu Khorkanin. "Automated Planning in Intelligent Distributed Systems Using a Multi-Agent Approach Based on LLM." Visnyk of Vinnytsia Politechnical Institute 179, no. 2 (2025): 111–17. https://doi.org/10.31649/1997-9266-2025-179-2-111-117.

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Vaccaro, Michael, Mikayla Friday, and Arash Zaghi. "Multi-Agentic LLMs for Personalizing STEM Texts." Applied Sciences 15, no. 13 (2025): 7579. https://doi.org/10.3390/app15137579.

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Multi-agent large language models promise flexible, modular architectures for delivering personalized educational content. Drawing on a pilot randomized controlled trial with middle school students (n = 23), we introduce a two-agent GPT-4 framework in which a Profiler agent infers learner-specific preferences and a Rewrite agent dynamically adapts science passages via an explicit message-passing protocol. We implement structured system and user prompts as inter-agent communication schemas to enable real-time content adaptation. The results of an ordinal logistic regression analysis hinted that

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Researcher. "INNOVATIVE TECHNOLOGIES RESHAPING ENTERPRISE AND INDUSTRIAL OPERATIONS." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 41–419. https://doi.org/10.5281/zenodo.13990275.

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This article explores the transformative potential of two emerging technologies in enterprise and industrial operations: multi-agent Large Language Model (LLM) systems and Augmented Reality/Virtual Reality (AR/VR) technology. It examines how LLM systems are revolutionizing enterprise processes and documentation by automating complex interactions and enhancing collaboration across organizational layers. The paper also delves into the application of AR/VR technologies in intermodal terminals and railroad intermodal terminals, highlighting their role in improving safety, efficiency, and training

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Li, Guoliang, Xuanhe Zhou, and Xinyang Zhao. "LLM for Data Management." Proceedings of the VLDB Endowment 17, no. 12 (2024): 4213–16. http://dx.doi.org/10.14778/3685800.3685838.

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Machine learning techniques have been verified to be effective in optimizing data management systems and are widely researched in recent years. However, traditional small-sized ML models often struggle to generalize to new scenarios, and have limited context understanding ability (e.g., inputting discrete features only). The emergence of LLMs offers a promising solution to these challenges. LLMs have been trained over a vast number of scenarios and tasks and acquire human-competitive capabilities like context understanding and summarization, which can be highly beneficial for data management t

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Seabra, Antony, Claudio Cavalcante, Jo˜ao Nepomuceno, Lucas Lago, Nicolaas Ruberg, and S´ergio Lifschitz. "Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models." International Journal on Cybernetics & Informatics 13, no. 6 (2024): 11–30. https://doi.org/10.5121/ijci.2024.130602.

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We propose a methodology that combines several advanced techniques in Large Language Model (LLM) retrieval to support the development of robust, multi-source questionanswer systems. This methodology is designed to integrate information from diverse data sources, including unstructured documents (PDFs) and structured databases, through a coordinated multi-agent orchestration and dynamic retrieval approach. Our methodology leverages specialized agents—such as SQL agents, Retrieval-Augmented Generation (RAG) agents, and router agents—that dynamically select the most appropriate retrieval strategy

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Yu, Chung-En (Johnny), Brian Jalaian, and Nathaniel D. Bastian. "Mitigating Large Vision-Language Model Hallucination at Post-hoc via Multi-agent System." Proceedings of the AAAI Symposium Series 4, no. 1 (2024): 110–13. http://dx.doi.org/10.1609/aaaiss.v4i1.31780.

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This paper addresses the critical issue of hallucination in Large Vision-Language Models (LVLMs) by proposing a novel multi-agent framework. We integrate three post-hoc correction techniques: self-correction, external feedback, and agent debate, to enhance LVLM trustworthiness. Our approach tackles key challenges in LVLM hallucination, including weak visual encoders, parametric knowledge bias, and loss of visual attention during inference. The framework employs a Plug-in LVLM as the base model to reduce its hallucination, a Large Language Model (LLM) for guided refinement, external toolbox mod

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Jeong, Cheonsu. "Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platform." Journal of Intelligence and Information Systems 31, no. 1 (2025): 191–231. https://doi.org/10.13088/jiis.2025.31.1.191.

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de Zarzà, I., J. de Curtò, Gemma Roig, Pietro Manzoni, and Carlos T. Calafate. "Emergent Cooperation and Strategy Adaptation in Multi-Agent Systems: An Extended Coevolutionary Theory with LLMs." Electronics 12, no. 12 (2023): 2722. http://dx.doi.org/10.3390/electronics12122722.

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The increasing complexity of Multi-Agent Systems (MASs), coupled with the emergence of Artificial Intelligence (AI) and Large Language Models (LLMs), have highlighted significant gaps in our understanding of the behavior and interactions of diverse entities within dynamic environments. Traditional game theory approaches have often been employed in this context, but their utility is limited by the static and homogenous nature of their models. With the transformative influence of AI and LLMs on business and society, a more dynamic and nuanced theoretical framework is necessary to guide the desig

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Isakov, A. O., N. F. Gusarova, D. A. Dobrenko, and A. A. Golubev. "Explainability of Agent Behavior in Clinical Decision Support Systems." Economics Law Innovaion, no. 4 (December 29, 2024): 50–59. https://doi.org/10.17586/2713-1874-2024-4-50-59.

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The article considers the task of improving the explainability of agent behavior in clinical decision support systems using multi-agent reinforcement learning, taking into account the behavioral characteristics of individual agents. Special attention is paid to the application of the proximal policy optimization (PPO) algorithm, which is used to simulate the interaction of artificial and natural intelligence agents. In addition, the importance of taking into account the behavior-al characteristics of patients is considered, which is achieved through the use of the developed framework «beliefs-

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Berlec, Tomaž, Marko Corn, Sergej Varljen, and Primož Podržaj. "Exploring Decentralized Warehouse Management Using Large Language Models: A Proof of Concept." Applied Sciences 15, no. 10 (2025): 5734. https://doi.org/10.3390/app15105734.

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The Fourth Industrial Revolution has introduced “shared manufacturing” as a key concept that leverages digitalization, IoT, blockchain, and robotics to redefine the production and delivery of manufacturing services. This paper presents a novel approach to decentralized warehouse management integrating Large Language Models (LLMs) into the decision-making processes of autonomous agents, which serves as a proof of concept for shared manufacturing. A multi-layered system architecture consisting of physical, digital shadow, organizational, and protocol layers was developed to enable seamless inter

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Yu, Jiangbo, and Graeme McKinley. "Synthetic Participatory Planning of Shared Automated Electric Mobility Systems." Sustainability 16, no. 13 (2024): 5618. http://dx.doi.org/10.3390/su16135618.

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Unleashing the synergies among rapidly evolving mobility technologies in a multi-stakeholder setting presents unique challenges and opportunities for addressing urban transportation problems. This paper introduces a novel synthetic participatory method that critically leverages large language models (LLMs) to create digital avatars representing diverse stakeholders to plan shared automated electric mobility systems (SAEMS). These calibratable agents collaboratively identify objectives, envision and evaluate SAEMS alternatives, and strategize implementation under risks and constraints. The resu

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Zhu, Feifan, Fei Huang, Yantao Yu, Guojin Liu, and Tiancong Huang. "Task Offloading with LLM-Enhanced Multi-Agent Reinforcement Learning in UAV-Assisted Edge Computing." Sensors 25, no. 1 (2024): 175. https://doi.org/10.3390/s25010175.

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Unmanned aerial vehicles (UAVs) furnished with computational servers enable user equipment (UE) to offload complex computational tasks, thereby addressing the limitations of edge computing in remote or resource-constrained environments. The application of value decomposition algorithms for UAV trajectory planning has drawn considerable research attention. However, existing value decomposition algorithms commonly encounter obstacles in effectively associating local observations with the global state of UAV clusters, which hinders their task-solving capabilities and gives rise to reduced task co

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Han, Seungjun, and Wongyung Choi. "Development of a Large Language Model-based Multi-Agent Clinical Decision Support System for Korean Triage and Acuity Scale (KTAS)-Based Triage and Treatment Planning in Emergency Departments." Advances in Artificial Intelligence and Machine Learning 05, no. 01 (2025): 3261–75. https://doi.org/10.54364/aaiml.2025.51187.

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Emergency department (ED) overcrowding and the complexity of rapid decision-making in critical care settings pose significant challenges to healthcare systems worldwide. While clinical decision support systems (CDSS) have shown promise, the integration of large language models (LLMs) offers new possibilities for enhancing triage accuracy and clinical decisionmaking. This study presents an LLM-driven CDSS designed to assist ED physicians and nurses in patient triage, treatment planning, and overall emergency care management. We developed a multi-agent CDSS utilizing Llama-3-70b as the base LLM,

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Papageorgiou, George, Vangelis Sarlis, Manolis Maragoudakis, and Christos Tjortjis. "Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant." Applied Sciences 15, no. 11 (2025): 6315. https://doi.org/10.3390/app15116315.

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As public institutions increasingly adopt AI-driven virtual assistants to support transparency and citizen engagement, the need for explainable, accurate, and context-aware language systems becomes vital. While traditional retrieval-augmented generation (RAG) frameworks effectively integrate external knowledge into Large Language Models (LLMs), their reliance on flat, unstructured document retrieval limits multi-hop reasoning and interpretability, especially with complex, structured e-government datasets. This study introduces a modular, extensible, multi-agent graph retrieval-augmented genera

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Shi, Qihan. "Design and Implementation of Game Decision-making System Based on Personified LLM Agents." Applied and Computational Engineering 158, no. 1 (2025): 87–95. https://doi.org/10.54254/2755-2721/2025.tj23326.

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As the core carrier of multi-agent body collaboration and competition, game decision systems have significant value in economics, artificial intelligence and complex system modeling. Traditional methods have significant limitations in terms of strategy diversity, dynamic adaptability and interpretability, which make it difficult to cope with the real-time evolution needs of complex game scenarios. This study propose a game decision-making system based on personified large language model (LLM), which realizes dynamic strategy aggregation through the synergistic mechanism of competitive, coopera

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Agarwal, Prerna, Harshit Dave, Jayachandu Bandlamudi, Renuka Sindhgatta, and Kushal Mukherjee. "Multi-Stage Prompting for Next Best Agent Recommendations in Adaptive Workflows." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 22843–49. http://dx.doi.org/10.1609/aaai.v38i21.30319.

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Traditional business processes such as loan processing, order processing, or procurement have a series of steps that are pre-defined at design and executed by enterprise systems. Recent advancements in new-age businesses, however, focus on having adaptive and ad-hoc processes by stitching together a set of functions or steps enabled through autonomous agents. Further, to enable business users to execute a flexible set of steps, there have been works on providing a conversational interface to interact and execute automation. Often, it is necessary to guide the user through the set of possible s

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Kozlov, Vadym, Vadym Slyusar, Volodymyr Tverdokhlibov, and Zoia Andriichuk. "Integration and coordination of electronic warfare assets through large-scale language models." Technology audit and production reserves 1, no. 2(81) (2025): 54–61. https://doi.org/10.15587/2706-5448.2025.323916.

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As an object of research, the work considers the process of functioning of electronic warfare (EW) means using artificial intelligence (AI) technologies based on large language models (LLM). One of the most problematic issues in increasing the efficiency of their functioning is ensuring the adaptability function in EW means, as well as timely detection of threats and formation of appropriate countermeasures. This problem is solved by implementing a multi-agent architecture, the task of which is to ensure continuous exchange of information, both between agents in the EW means themselves and in

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Zhu, Jun-Peng, Peng Cai, Kai Xu, et al. "AutoTQA: Towards Autonomous Tabular Question Answering through Multi-Agent Large Language Models." Proceedings of the VLDB Endowment 17, no. 12 (2024): 3920–33. http://dx.doi.org/10.14778/3685800.3685816.

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With the growing significance of data analysis, several studies aim to provide precise answers to users' natural language questions from tables, a task referred to as tabular question answering (TQA). The state-of-the-art TQA approaches are limited to handling only single-table questions. However, real-world TQA problems are inherently complex and frequently involve multiple tables, which poses challenges in directly extending single-table TQA designs to handle multiple tables, primarily due to the limited extensibility of the majority of single-table TQA methods. This paper proposes AutoTQA,

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Deo, Akhil. "QAagent: A Multiagent System for Unit Test Generation via Natural Language Pseudocode (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29345–47. https://doi.org/10.1609/aaai.v39i28.35246.

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Unit testing is essential for ensuring software quality, but it is often time-consuming and prone to developer oversight. With the rise of large language models (LLMs) in code generation, there is an increasing need for reliable and automated test generation systems. This work presents QAagent, a multi-agent system designed to generate unit tests using natural language pseudocode. QAagent leverages LLMs to create a detailed natural language plan of a function's implementation and then generates a comprehensive suite of test cases covering both base and edge scenarios. Experiments conducted on

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Papageorgiou, George, Vangelis Sarlis, Manolis Maragoudakis, and Christos Tjortjis. "Enhancing E-Government Services through State-of-the-Art, Modular, and Reproducible Architecture over Large Language Models." Applied Sciences 14, no. 18 (2024): 8259. http://dx.doi.org/10.3390/app14188259.

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Integrating Large Language Models (LLMs) into e-government applications has the potential to improve public service delivery through advanced data processing and automation. This paper explores critical aspects of a modular and reproducible architecture based on Retrieval-Augmented Generation (RAG) for deploying LLM-based assistants within e-government systems. By examining current practices and challenges, we propose a framework ensuring that Artificial Intelligence (AI) systems are modular and reproducible, essential for maintaining scalability, transparency, and ethical standards. Our appro

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Yang, Chenyuan, Yinlin Deng, Runyu Lu, et al. "WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models." Proceedings of the ACM on Programming Languages 8, OOPSLA2 (2024): 709–35. http://dx.doi.org/10.1145/3689736.

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Compiler correctness is crucial, as miscompilation can falsify program behaviors, leading to serious consequences over the software supply chain. In the literature, fuzzing has been extensively studied to uncover compiler defects. However, compiler fuzzing remains challenging: Existing arts focus on black- and grey-box fuzzing, which generates test programs without sufficient understanding of internal compiler behaviors. As such, they often fail to construct test programs to exercise intricate optimizations. Meanwhile, traditional white-box techniques, such as symbolic execution, are computati

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Pallagani, Vishal, Bharath Chandra Muppasani, Kaushik Roy, et al. "On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)." Proceedings of the International Conference on Automated Planning and Scheduling 34 (May 30, 2024): 432–44. http://dx.doi.org/10.1609/icaps.v34i1.31503.

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Automated Planning and Scheduling is among the growing areas in Artificial Intelligence (AI) where mention of LLMs has gained popularity. Based on a comprehensive review of 126 papers, this paper investigates eight categories based on the unique applications of LLMs in addressing various aspects of planning problems: language translation, plan generation, model construction, multi-agent planning, interactive planning, heuristics optimization, tool integration, and brain-inspired planning. For each category, we articulate the issues considered and existing gaps. A critical insight resulting fro

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Rodriguez Almaraz, E., B. Miao, Y. Yadav, et al. "P10.06.A AGENT-BASED MODELING FOR PATIENT-CENTERED CLINICAL DECISION SUPPORT IN NEURO-ONCOLOGY." Neuro-Oncology 26, Supplement_5 (2024): v56. http://dx.doi.org/10.1093/neuonc/noae144.182.

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Abstract BACKGROUND Complex clinical decision-making in neuro-oncology is a multifaceted process involving numerous specialties influenced by various objective factors and patient preferences. Modeling these multidisciplinary neuro-oncology discussions presents significant challenges, particularly given the multimodal nature of the data. Recent advancements in large language models (LLMs) have enabled the development of LLM “agents”. These agents pave the way for multi-agent systems capable of simulating interacting components, thus capturing the nuances of interdisciplinary clinical discussio

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Trejo Omeñaca, Alex, Esteve Llargués Rocabruna, Jonny Sloan, et al. "Leave as Fast as You Can: Using Generative AI to Automate and Accelerate Hospital Discharge Reports." Computers 14, no. 6 (2025): 210. https://doi.org/10.3390/computers14060210.

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Clinical documentation, particularly the hospital discharge report (HDR), is essential for ensuring continuity of care, yet its preparation is time-consuming and places a considerable clinical and administrative burden on healthcare professionals. Recent advancements in Generative Artificial Intelligence (GenAI) and the use of prompt engineering in large language models (LLMs) offer opportunities to automate parts of this process, improving efficiency and documentation quality while reducing administrative workload. This study aims to design a digital system based on LLMs capable of automatica

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Rech, Andrew, Yury Goltsev, Nikolay Samusik, et al. "Mapping Path Forward for Using the Generative AI for Comprehension of Prognostic Genesets Implicated in Hematologic Malignancies." Blood 144, Supplement 1 (2024): 7521. https://doi.org/10.1182/blood-2024-211865.

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Recent advancements in generative AI, including models such as GPT-4 and LLAMA2, have been rapidly integrated into cancer research. It is not clear to which extent the AI assisted outputs can be trusted and used to the advancement of biomedical knowledge. We discuss and demonstrate several ways the genAI could be used in hematology and related fields. We have compared a number of commonly available LLM based tools as well as custom trained AIs to determine their ability to enhance the accuracy of functional and genomic discovery screens by reducing the incidence of false positives and refining

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Brawer, Jake, Kayleigh Bishop, Bradley Hayes, and Alessandro Roncone. "Towards a Natural Language Interface for Flexible Multi-Agent Task Assignment." Proceedings of the AAAI Symposium Series 2, no. 1 (2024): 167–71. http://dx.doi.org/10.1609/aaaiss.v2i1.27665.

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Task assignment and scheduling algorithms are powerful tools for autonomously coordinating large teams of robotic or AI agents. However, the decisions these system make often rely on components designed by domain experts, which can be difficult for non-technical end-users to understand or modify to their own ends. In this paper we propose a preliminary design for a flexible natural language interface for a task assignment system. The goal of our approach is both to grant users more control over a task assignment system's decision process, as well as render these decisions more transparent. Use

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Proskurnikov, A. V., and I. S. Zabaryanskaya. "Alternating Projection Method for Intersection of Convex Sets, Multi-Agent Consensus Algorithms and Averaging Inequalities." Žurnal vyčislitelʹnoj matematiki i matematičeskoj fiziki 64, no. 4 (2024): 671–96. http://dx.doi.org/10.31857/s0044466924040078.

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The history of the alternating projection method for finding a common point of several convex sets in Euclidean space goes back to the well-known Kaczmarz algorithm for solving systems of linear equations, which was devised in the 1930s and later found wide applications in image processing and computed tomography. An important role in the study of this method was played by I.I. Eremin’s, L.M. Bregman’s, and B.T. Polyak’s works, which appeared nearly simultaneously and contained general results concerning the convergence of alternating projections to a point in the intersection of sets, assumin

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Gong, Dennis, Jimmy A. Guo, Jennifer Su, et al. "Abstract 1254: Neural-like progenitor pancreatic cancer cells are enriched in residual disease following neoadjuvant chemoradiotherapy and acquire invasive and stem-like features through inflammatory reprogramming." Cancer Research 85, no. 8_Supplement_1 (2025): 1254. https://doi.org/10.1158/1538-7445.am2025-1254.

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Abstract Pancreatic ductal adenocarcinoma (PDAC) is notable for a high rate of recurrence and metastasis despite intensive multi-agent chemoradiation. We previously identified a neural-like progenitor (NRP) transcriptional program strongly enriched in PDAC specimens with neoadjuvant chemoradiation and correlated with improved response to therapy. However, the mechanisms for why malignant cells in the post-treatment setting had collectively increased expression of NRP transcriptional signatures remained unclear. In this study, we sought to understand the contexts in which NRP becomes expressed

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Parker, Terri L., Adam Rosenthal, Vaishali Sanchorawala, et al. "A Phase II Study of Isatuximab (SAR650984) (NSC-795145) for Patients with Previously Treated AL Amyloidosis (SWOG S1702; NCT#03499808)." Blood 136, Supplement 1 (2020): 20–21. http://dx.doi.org/10.1182/blood-2020-143180.

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Background: Isatuximab (SAR650984) is an IgG1k monoclonal antibody that binds with high affinity to CD38 expressed on plasma cells in AL amyloidosis. It has been shown to be efficacious and well tolerated in relapsed and refractory multiple myeloma as a single agent and in combination. Here we report on the preliminary results of a prospective multi-center, phase II study of isatuximab in previously treated patients with AL amyloidosis (NCT03499808). Methods: Eligibility included age ≥ 18 years, relapsed or refractory systemic AL amyloidosis, ≥ 1 prior line of therapy, measurable disease (defi

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Lalitha Ramaswamy. "Antimicrobial Properties of Cocos nucifera: A Review." CORD 31, no. 1 (2015): 6. http://dx.doi.org/10.37833/cord.v31i1.62.

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Akinyele, T.A., 2011. Assessment of the antibacterial properties of n-Hexane extract of Cocos Nucifera and its interactions with some Conventional antibiotics. Masters Dissertation. University of Fort Hare, Alice. Alan´ıs, A.D., Calzada, F, Cervantes J.A., Torres, J., and Ceballos, G.M. 2005. Antibacterial properties of some plants used in Mexican traditional medicine for the treatment of gastrointestinal disorders, Journal of Ethnopharmacology. 100, 153–157. Alviano, W.S., Alviao, D.S., Diniz, C.G., Antoniolli, A.R., Alviano, C.S., Frias, L.M. 2008. In vitro antioxidant potential

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Li, Xinyi, Sai Wang, Siqi Zeng, Yu Wu, and Yi Yang. "A survey on LLM-based multi-agent systems: workflow, infrastructure, and challenges." Vicinagearth 1, no. 1 (2024). http://dx.doi.org/10.1007/s44336-024-00009-2.

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AbstractThe pursuit of more intelligent and credible autonomous systems, akin to human society, has been a long-standing endeavor for humans. Leveraging the exceptional reasoning and planning capabilities of large language models (LLMs), LLM-based agents have been proposed and have achieved remarkable success across a wide array of tasks. Notably, LLM-based multi-agent systems (MAS) are considered a promising pathway towards realizing general artificial intelligence that is equivalent to or surpasses human-level intelligence. In this paper, we present a comprehensive survey of these studies, o

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Yu, Jiapeng, Yuqian Wu, Yajing Zhan, Wenhao Guo, Zhou Xu, and Raymond Lee. "Co-Learning: code learning for multi-agent reinforcement collaborative framework with conversational natural language interfaces." Frontiers in Artificial Intelligence 8 (May 15, 2025). https://doi.org/10.3389/frai.2025.1431003.

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Online question-and-answer (Q&amp;A) systems based on the Large Language Model (LLM) have progressively diverged from recreational to professional use. However, beginners in programming often struggle to correct code errors independently, limiting their learning efficiency. This paper proposed a Multi-Agent framework with environmentally reinforcement learning (E-RL) for code correction called Code Learning (Co-Learning) community, assisting beginners to correct code errors independently. It evaluates the performance of multiple LLMs from an original dataset with 702 error codes, uses it a

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De Curtò, J., and I. De Zarzà. "LLM-Driven Social Influence for Cooperative Behavior in Multi-Agent Systems." IEEE Access, 2025, 1. https://doi.org/10.1109/access.2025.3548451.

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Chen, Rongjun, and Chengbo He. "Fostering collective intelligence in CPSS: an LLM-driven multi-agent cooperative tuning framework." Frontiers in Physics 13 (June 30, 2025). https://doi.org/10.3389/fphy.2025.1613499.

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Cyber-Physical-Social Systems (CPSS) have emerged as a transformative paradigm in recent years, embracing computational processes, physical systems, and human social interactions within an integrated architectural framework. Advances in artificial intelligence technologies are targeted at addressing the complexity of CPSS design, especially in modeling human reactions in cyber-physical environment. Notably, LLM-based agents have shown significant potential, and numerous studies have leveraged multi-agent collaboration frameworks to solve reasoning tasks. Some approaches achieve multi-agent col

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Zhang, Xueqiao, Chao Zhang, Jianwen Sun, Jun Xiao, Yi Yang, and Yawei Luo. "EduPlanner: LLM-Based Multi-Agent Systems for Customized and Intelligent Instructional Design." IEEE Transactions on Learning Technologies, 2025, 1–11. https://doi.org/10.1109/tlt.2025.3561332.

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saxena, Priyanshi, and Roshan Lal -. "Leveraging Large Language Models in Multiagent System." International Journal For Multidisciplinary Research 7, no. 2 (2025). https://doi.org/10.36948/ijfmr.2025.v07i02.41697.

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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%). C

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Tran, Hao, Viren Joseph, Zaidon Al-Falahi, et al. "Abstract 4138722: Large Language Model based multi-agent Transcatheter Aortic Valve Implantation team to augment multidisciplinary meetings - proof of concept." Circulation 150, Suppl_1 (2024). http://dx.doi.org/10.1161/circ.150.suppl_1.4138722.

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Introduction: Multidisciplinary team (MDT) discussions are integral to Transcatheter Aortic Valve Implantation (TAVI) decision making. Large language model (LLM) ubiquity and low-code no-code platforms have enabled clinician lead solution development. Specialised chatbots or ‘agents’ have evolved into multi-agent systems that can personify human collaboration. We assess the performance of an artificial intelligence (AI) multi-agent TAVI MDT. Methods: Four de-identified TAVI cases from two metropolitan Australian hospitals were assessed by a mock human TAVI MDT (h-MDT) and an AI multi-agent TAV

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He, Junda, Christoph Treude, and David Lo. "LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead." ACM Transactions on Software Engineering and Methodology, January 13, 2025. https://doi.org/10.1145/3712003.

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Integrating Large Language Models (LLMs) into autonomous agents marks a significant shift in the research landscape by offering cognitive abilities that are competitive with human planning and reasoning. This paper explores the transformative potential of integrating Large Language Models into Multi-Agent (LMA) systems for addressing complex challenges in software engineering (SE). By leveraging the collaborative and specialized abilities of multiple agents, LMA systems enable autonomous problem-solving, improve robustness, and provide scalable solutions for managing the complexity of real-wor

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Jimenez-Romero, Cristian, Alper Yegenoglu, and Christian Blum. "Multi-agent systems powered by large language models: applications in swarm intelligence." Frontiers in Artificial Intelligence 8 (May 21, 2025). https://doi.org/10.3389/frai.2025.1593017.

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This work examines the integration of large language models (LLMs) into multi-agent simulations by replacing the hard-coded programs of agents with LLM-driven prompts. The proposed approach is showcased in the context of two examples of complex systems from the field of swarm intelligence: ant colony foraging and bird flocking. Central to this study is a toolchain that integrates LLMs with the NetLogo simulation platform, leveraging its Python extension to enable communication with GPT-4o via the OpenAI API. This toolchain facilitates prompt-driven behavior generation, allowing agents to respo

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Zaleski, Alexander, and Jaroslaw A. Chudziak. "LLM-Based multi-agent system for individual investment in energy and natural resources." International Journal of Electronics and Telecommunications, April 7, 2025, 11. https://doi.org/10.24425/ijet.2025.153538.

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Recent advancements in large language models and multiagent large language model based systems show that these technologies can be applied to a large number of problems. They can automate complex tasks and perform advanced analyses that would take an expert a significant amount of time. This article describes a multiagent large language model (LLM) based platform for investment advisory in the energy natural resources sector. The system integrates multiple types of investment analyses e.g. technical analysis, fundamental analysis, sentiment analysis and stock price prediction. The approach of

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Xing, Frank. "Designing Heterogeneous LLM Agents for Financial Sentiment Analysis." ACM Transactions on Management Information Systems, August 13, 2024. http://dx.doi.org/10.1145/3688399.

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Large language models (LLMs) have drastically changed the possible ways to design intelligent systems, shifting the focus from massive data acquisition and new model training to human alignment and strategical elicitation of the full potential of existing pre-trained models. This paradigm shift, however, is not fully realized in financial sentiment analysis (FSA), due to the discriminative nature of this task and a lack of prescriptive knowledge of how to leverage existing generative models in such a context. This study investigates the effectiveness of the new paradigm, i.e., using LLMs witho

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Kim, Soyeon, Yonggyun Yu, and Hogeon Seo. "Artificial intelligence orchestration for text-based ultrasonic simulation via self-review by multi-large language model agents." Scientific Reports 15, no. 1 (2025). https://doi.org/10.1038/s41598-025-97498-y.

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Abstract Widely used ultrasonic simulation systems often rely on complex graphical user interfaces (GUIs) or scripting, resulting in substantial time investments and reduced accessibility for new users. In this study, we propose a novel text-based simulation control architecture, which leverages a large language model (LLM) and the ground artificial intelligence (AI) approach to streamline the control of ultrasonic simulation systems. By modularizing the functionalities of the SimNDT program into discrete functions and enabling natural language-based command interpretation, the proposed method

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Yang, Tingting, Ping Feng, Qixin Guo, et al. "AutoHMA-LLM: Efficient Task Coordination and Execution in Heterogeneous Multi-Agent Systems Using Hybrid Large Language Models." IEEE Transactions on Cognitive Communications and Networking, 2025, 1. https://doi.org/10.1109/tccn.2025.3528892.

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Mouri Zadeh Khaki, Ahmad, Ahyoung Choi, and Laleh Seyyed-Kalantari. "Simulating Social Behavior of LLM-Based Autonomous Negotiator Agents in a Game-Theoretical Framework Using Multi-Agent Systems." International Journal of Human–Computer Interaction, May 14, 2025, 1–10. https://doi.org/10.1080/10447318.2025.2495117.

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Kim, Soyoen, Jeajun Lee, Yonggyun Yu, and Hogeon Seo. "Ultrasonic Simulations with AI Agents: A Text-Driven Framework Using Multi-LLM Self-Review." e-Journal of Nondestructive Testing 30, no. 7 (2025). https://doi.org/10.58286/31366.

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Ultrasonic simulation systems are indispensable in numerous applications. Nevertheless, they frequently necessitate complex graphical user interfaces (GUIs) or scripting, which demand considerable time investments and reduce accessibility. This study proposes a novel text-based simulation control architecture that utilizes large language models (LLMs) in conjunction with Ground Artificial Intelligence (AI) methods. The proposed approach involves modularizing the SimNDT program's functions and integrating natural language command interpretation, leading to a reduction in simulation setup time b

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Zhou, Andy, Kevin Wu, Francesco Pinto, et al. "AutoRedTeamer: Autonomous Red Teaming with Lifelong Attack Integration." SuperIntelligence - Robotics - Safety & Alignment 2, no. 2 (2025). https://doi.org/10.70777/si.v2i2.14433.

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As large language models (LLMs) become increasingly capable, security and safety evaluation are crucial. While current red teaming approaches have made strides in assessing LLM vulnerabilities, they often rely heavily on human input and lack comprehensive coverage of emerging attack vectors. This paper introduces AutoRedTeamer, a novel framework for fully automated, end-to-end red teaming against LLMs. AutoRedTeamer combines a multi-agent architecture with a memory-guided attack selection mechanism to enable continuous discovery and integration of new attack vectors. The dual-agent framework c

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Nandkumar, Chandran, and Luka Peternel. "Enhancing supermarket robot interaction: an equitable multi-level LLM conversational interface for handling diverse customer intents." Frontiers in Robotics and AI 12 (April 29, 2025). https://doi.org/10.3389/frobt.2025.1576348.

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This paper presents the design and evaluation of a comprehensive system to develop voice-based interfaces to support users in supermarkets. These interfaces enable shoppers to convey their needs through both generic and specific queries. Although customisable state-of-the-art systems like GPTs from OpenAI are easily accessible and adaptable, featuring low-code deployment with options for functional integration, they still face challenges such as increased response times and limitations in strategic control for tailored use cases and cost optimization. Motivated by the goal of crafting equitabl

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