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multimodal Learning in Natural Language Processing: Fact-Checking, Ethics and Misinformation Detection

Multimodal Learning in Natural Language Processing: Fact-Che...

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2025 international conference on multi-agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Joshi, Himanshu Sanjay Taherdoost, Hamed College of Business Westcliff University CA United States College of Technology & Engineering Westcliff University CA United States University Canada West Vancouver Canada Gus Institute Global University Systems London United Kingdom Q Minded Quark Minded Technology Inc. Vancouver Canada

ISBN: (纸本)9798331509828

multimodal learning (MML) is a subtype of deep learning that improves the capabilities of machine learning models by integrating modalities from multiple data sources such as text, images, audio, and video. This integration of modalities produces a more comprehensive understanding of complex data and nuanced outputs. Applications of this approach in real-time decision-making, information retrieval, and knowledge representation can improve the insights derived from the natural language processing (NLP) system. Moreover, in a time of information overload, there are concerns regarding the credibility of the content. The role of NLP systems is central to processing textual data in MML systems. In this environment, the ethical development of multimodal systems for fact-checking is crucial. This paper addresses an existing gap in use of multimodal learning in the context of fact-checking by investigating issues related to integration of NLP, Question-Answering (QA) systems, and knowledge graphs to combat misinformation. This research presents multimodal data based frameworks based on knowledge graph integration. The exploration of the frameworks enables to enhance the accuracy, reliability, and ethical soundness of fact-checking mechanisms. This study also highlights new directions for overcoming challenges related to modality alignment, computational efficiency, and ethical transparency. © 2025 IEEE.

关键词： knowledge graph

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Frontiers of Large Language Model-Based agentic systems Construction, Efficacy and Safety 24

Frontiers of Large Language Model-Based Agentic Systems Cons...

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33rd ACM international conference on Information and knowledge Management (CIKM)

作者： He, Jia Ghosh, Reshmi Walia, Kabir Chen, Jieqiu Dhadiwal, Tushar Hazel, April Inguva, Chandra Microsoft Corp Cambridge MA 02142 USA Microsoft Corp Redmond WA 98052 USA Microsoft Corp St Louis MO USA Microsoft Corp Salt Lake City UT USA

ISBN: (纸本)9798400704369

"The previous era was about information at your fingertips;I think of the AI era as expertise at your fingertips." - Satya Nadella, CNBC [1] This tutorial explores Large Language Model (LLM)-based autonomous agents, addressing the lack of comprehensive guides on the topic. It systematically examines key components such as profiling, perception, memory, planning, and action, using an established taxonomy. The tutorial also extends the discussion to multi-agent frameworks, offering insights into collaborative intelligence. Additionally, it compares popular open-source frameworks for LLM-based agent development and discusses evaluation methodologies, focusing on efficiency and safety. The tutorial aims to catalyze dialogue and partnership among practitioners, propelling forward the integration of robust and effective LLM agent systems into the production environment.

关键词： large language models autonomous agent system agent construction evaluation efficiency safety

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Transforming Software Development: A Study on the integration of multi-agent systems and Large Language Models for Automatic Code Generation 12

Transforming Software Development: A Study on the Integratio...

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12th international conference in Software Engineering Research and Innovation, CONISOFT 2024

作者： Ramirez-Rueda, Rolando Benitez-Guerrero, Edgard Mezura-Godoy, Carmen Barcenas, Everardo Universidad veracruzana Facultad de Estadística e Informática Xalapa Mexico Universidad Nacional Autónoma de México Facultad de Ingeniería Ciudad de México Mexico

ISBN: (纸本)9798331532116

This paper explores the integration of multi-agent systems (MAS) and Large Language Models (LLMs) for auto-matic code generation, addressing the limitations of traditional manual coding. By conducting a comprehensive review of existing literature and analyzing a practical case study, we demonstrate how MAS and LLMs can collaboratively enhance software development processes. The research focuses on the technical and theoretical challenges of this integration, highlighting the potential for improved productivity, adaptability, and quality in code generation. The findings contribute to AI-based software engineering by revealing new research directions in collective intelligence and automated programming. © 2024 IEEE.

关键词： Software design

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EduMAS: A Novel LLM-Powered multi-agent Framework for Educational Support

EduMAS: A Novel LLM-Powered Multi-Agent Framework for Educat...

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2024 IEEE international conference on Big Data, BigData 2024

作者： Li, Qiaomu Xie, Ying Chakravarty, Sumit Lee, Dabae Kennesaw State University College of Computing and Software Engineering MariettaGA30060 United States Kennesaw State University Department of Instructional Technology MariettaGA30060 United States

ISBN: (纸本)9798350362480

In general, educational support with Large Language Models (LLMs) faces challenges in knowledge organization, expertise integration, and contextual adaptation. So, we present EduMAS, a novel multi-agent framework that coordinates specialized agents with graph-based knowledge navigation. Our framework introduces three key innovations: (1) Specialized agents that provide expertise in different learning aspects to solve decomposed subtasks professionally;(2) Graph Navigator for graph-based knowledge extraction and selection to improve the quality of responses;(3) The Emotional Awareness mechanism for better contextual adaptation. Through comprehensive experiments on college-level physics education and evaluated by six state-of-the-art LLMs, EduMAS demonstrates significant improvements over the baseline model in complex concept integration, cross-disciplinary understanding, and theory-to-application translation. Ablation studies further validate the contribution of each framework component, Specialized agents and Graph Navigator play important roles in performance improvement. Our work provides strong support for LLM-powered multi-agent system in AI-assisted education. © 2024 IEEE.

关键词： multi agent systems

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Ontology-Based Feedback to Improve Runtime Control for multi-agent Manufacturing systems 19

Ontology-Based Feedback to Improve Runtime Control for Multi...

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19th IEEE international conference on Automation Science and Engineering, CASE 2023

作者： Lim, Jonghan Pfeiffer, Leander Ocker, Felix Vogel-Heuser, Birgit Kovalenko, Ilya Pennsylvania State University Department of Industrial and Manufacturing Department of Mechanical Engineering State College United States Institute of Automation and Information Systems Technical University of Munich Munich Germany

ISBN: (纸本)9798350320695

Improving the overall equipment effectiveness (OEE) of machines on the shop floor is crucial to ensure the productivity and efficiency of manufacturing systems. To achieve the goal of increased OEE, there is a need to develop flexible runtime control strategies for the system. Decentralized strategies, such as multi-agent systems, have proven effective in improving system flexibility. However, runtime multi-agent control of complex manufacturing systems can be challenging as the agents require extensive communication and computational efforts to coordinate agent activities. One way to improve communication speed and cooperation capabilities between system agents is by providing a common language between these agents to represent knowledge about system behavior. The integration of ontology into multi-agent systems in manufacturing provides agents with the capability to continuously update and refine their knowledge in a global context. This paper contributes to the design of an ontology for multi-agent systems in manufacturing, introducing an extendable knowledge base and a methodology for continuously updating the production data by agents during runtime. To demonstrate the effectiveness of the proposed framework, a case study is conducted in a simulated environment, which shows improvements in OEE during runtime. © 2023 IEEE.

关键词： multi agent systems

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PICOAS: A clinical knowledge linking model for delivering up-to-date, interrelated, and personalized decision support

PICOAS: A clinical knowledge linking model for delivering up...

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2024 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Ziji Xiao, Liang He, Miaomiao Zhu, Rujun Yang, Hang Chen, Jianxia Hubei University of Technology School of Computer Science Wuhan China

ISBN: (纸本)9798350386226

Clinical decision support is aimed at delivering the best evidence available encapsulated in practice guidelines. The current challenges in reaching this goal include keeping guidelines up-to-date, linking them to address multi-morbidity, and flexible customization to fit patient preferences. Although a variety of solutions have been proposed to address these challenges, a comprehensive approach for their systematic integration remains absent. We propose PICOAS, a knowledge linking model composed of three modes, to establish the relationship between knowledge sources of guidelines, medical literature, and patient reviews. An LLM-agent architecture is developed, which is capable of understanding and providing decision support informed by the knowledge link model. We demonstrate the feasibility and effectiveness of our approach using a study of breast cancer. An experiment was conducted for evaluating its capabilities in addressing the three challenges. © 2024 IEEE.

关键词： multi agent systems

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Smart Pavement Repair Bot with IoT and Machine Learning integration

Smart Pavement Repair Bot with IoT and Machine Learning Inte...

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2025 international conference on multi-agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Sujitha, S. Singh, Raj Narayan, Lalith Ray, Chaudhary Manas Gupta, Nishant New Horizon College of Engineering Department of Electrical and Electronics Engineering Bengaluru India

ISBN: (纸本)9798331509828

Potholes on road surfaces pose significant challenges, leading to vehicle damage, traffic inefficiencies, and increased safety risks. Conventional pothole detection and repair methods are labor-intensive, time-consuming, and costly. This project presents an autonomous Pothole Detection and Refilling Robot that utilizes Internet of Things (IoT) and Machine Learning (ML) technologies to automate the process of pothole identification and repair. The system incorporates ultrasonic sensors for detecting road surface anomalies and a camera, integrated with ML algorithms, for visual confirmation and classification of potholes. The robot wirelessly transmits detection data via Zigbee modules to a central monitoring system, facilitating real-time updates. Upon pothole detection, the robot autonomously engages a mechanical refilling system to dispense filler material, effectively restoring the road surface. By combining IoT for data communication, sensor-based detection, and ML-based image processing, the system provides a cost-effective and scalable solution for road maintenance. This approach reduces human intervention, optimizes repair efficiency, and enhances road safety by enabling continuous, autonomous monitoring and maintenance of road infrastructure. © 2025 IEEE.

关键词： Zigbee

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A human motivation driven based user-activity recommending service using AIGC and self-supervised agent cluster with debating scheme 24

A human motivation driven based user-activity recommending s...

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2nd international conference on Educational knowledge and Informatization, EKI 2024

作者： Chen, Hongzhi Gu, Shijia Wang, Xiaoyan Pang, Feng Lin, Xiufeng AI Business Unit Kunlun Digital Technology China HRM Business Unit Kunlun Digital Technology China

ISBN: (纸本)9798400710230

The manuscript introduces a self-supervised agent framework grounded in AIGC and a multi-debate mechanism. This framework leverages the multi-debate mechanism to facilitate the reflection and continuous iterative enhancement of agents, thereby enabling precise invocation of specialized models within the AIGC large-scale model. It enhances data insights through knowledge graph-augmented retrieval, ultimately replacing traditional keyboard-mouse interactions with natural language interfaces for executing critical user data prediction and insights, multi-model/application scheduling, integration with existing systems, and user decision support. Experiments conducted on a dataset comprising five major categories and over 10,000 SKUs from a medical consumables supplier, focusing on user interaction logic recommendation, model selection and prediction, as well as material demand forecasting and interpretation, have demonstrated the efficacy of our approach. The results confirm that the proposed multi-agent framework and multi-round debate mechanism achieve levels of human-machine interaction logic determination, predictive model selection, and business interpretability that are comparable to or on par with those of domain experts. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Decision support systems

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Large Language Model Assisted multi-agent Dialogue for Ontology Alignment 23

Large Language Model Assisted Multi-Agent Dialogue for Ontol...

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23rd international conference on Autonomous agents and multiagent systems, AAMAS 2024

作者： Zhang, Shiyao Dong, Yuji Zhang, Yichuan Payne, Terry R. Zhang, Jie Xi'an Jiaotong-Liverpool University Suzhou China University of Liverpool Liverpool United Kingdom

Ontology alignment is critical in cross-domain integration;however, it typically necessitates the involvement of a human domain-expert, which can make the task costly. Although a variety of machine-learning approaches have been proposed that can simplify this task by learning the patterns from experts, such techniques are still susceptible to domain knowledge updates that could potentially change the patterns and lead to extra expert involvement. The use of Large Language Models (LLMs) has demonstrated a general cognitive ability, which has the potential to assist ontology alignment from the cognition level, thus obviating the need for costly expert involvement. However, the process by which the output of LLMs is generated can be opaque and thus the reliability and interpretability of such models is not always predictable. This paper proposes a dialogue model, in which multiple agents negotiate the correspondence between two knowledge sets with the support from an LLM. We demonstrate that this approach not only reduces the need for the involvement of a domain expert for ontology alignment, but that the results are interpretable despite the use of LLMs. © 2024 international Foundation for Autonomous agents and multiagent systems.

关键词： multi agent systems

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Employing multi-agent systems to enhance virtual reality platforms 23

Employing multi-agent systems to enhance virtual reality pla...

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6th international conference on Networking, Intelligent systems and Security, NISS 2023

作者： Mdaghri-Alaoui, Ghalia Zouhair, Abdelhamid Elghouch, Nihad DSAI2S Research Team Faculty of Sciences and Technologies Abdelmalek Essaâdi University Tetouan Morocco

ISBN: (纸本)9798400700194

The use of virtual reality technology in the classroom has the potential to transform the way students learn by fostering immersive and interesting learning experiences that let them explore, discover, and build their own knowledge by giving students realistic and interactive experiences, virtual reality can dramatically influence distance learning. This technology has a positive effect on education. In addition, several research articles show that this technology has a positive impact on the user experience of learners. This paper presents some different virtual reality platforms used in different filed, specifically education. Additionally, there are systems that enable the complete integration of two increasingly important areas: virtual reality for visualization and intelligent multi-agent systems for artificial intelligence. The main goal of this paper is to propose an architecture for our system based on a multi-agent system with Virtual Reality allowing the learner to interact with the virtual scenes. © 2023 ACM.

关键词： multi agent systems

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