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• Designing and evaluating large language model-enabled clinical decision support for heart failure: a modular and risk-tiered framework.
  Heart failure (HF) care requires repeated decisions across suspected disease, diagnostic confirmation, phenotyping, guideline-directed medical therapy, device consideration, worsening HF, transition care, and advanced HF planning. Large language models (LLMs) …
  europepmc 2026 置信度 0.80
• LLM-Conductor: A Closed-Loop Resource-Adaptive Architecture for Secure LLM Deployment in Industrial Sensor Networks and IIoT Systems.
  To address the bottlenecks of missing decision-making closed loop, insufficient experience reuse, and decoupled resource scheduling in industrial LLM deployment, this paper proposes LLM-Conductor, a three-layer collaborative architecture that enables monitorin…
  europepmc 2026 置信度 0.80
• OEMA: ontology-enhanced multi-agent collaboration framework for zero-shot clinical named entity recognition.
  With the rapid growth of unstructured clinical narratives in electronic health records (EHRs), clinical named entity recognition (NER) has become a crucial technique for extracting structured medical information. However, traditional supervised models such as …
  pubmed Tao X, Dong X, Zhu Q, Zhou X 2026 置信度 0.82
• Agentic and LLM-Based Multimodal Anomaly Detection: Architectures, Challenges, and Prospects.
  Anomaly detection is crucial in maintaining the safety, reliability, and optimal performance of complex systems across diverse domains, such as industrial manufacturing, cybersecurity, and autonomous systems. While conventional methods typically handle single …
  europepmc 2026 置信度 0.80
• Toward Verifiable Misinformation Detection: A Multi-Tool LLM Agent Framework
  With the proliferation of Large Language Models (LLMs), the detection of misinformation has become increasingly important and complex. This research proposes an innovative verifiable misinformation detection LLM agent that goes beyond traditional true/false bi…
  openalex Zikun Cui, Tianyi Huang, Chia-En Chiang, C. Du 2025-08-04 置信度 0.72 MisinformationVerifiable secret sharingComputer scienceCredibilityTrustworthiness
• Optimization modeling and verification from problem specifications using a multi-agent multi-stage LLM framework
  This paper explores the use of Large Language Models (LLMs) in modeling real-world optimization problems. We concretely define the task of translating natural language descriptions into optimization models (NL2OPT) and provide criteria for classifying optimiza…
  openalex Mahdi Mostajabdaveh, Timothy T. Yu, Rindranirina Ramamonjison, Giuseppe Carenini 等 2024-08-07 置信度 0.72 Computer scienceTask (project management)Modeling languageSolverRepresentation (politics)
• EduMAS: A Novel LLM-Powered Multi-Agent Framework for Educational Support
  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-…
  openalex Qiaomu Li, Ying Xie, Sumit Chakravarty, Dabae Lee 2024-12-15 置信度 0.72 Computer scienceHuman–computer interaction
• LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision, and the Road Ahead
  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 article explores the transformative potential of in…
  openalex Junda He, Christoph Treude, David Lo 2025-01-13 置信度 0.72 Computer scienceScalabilitySoftware engineeringSystems development life cycleTransformative learning
• Designing Heterogeneous LLM Agents for Financial Sentiment Analysis
  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 strategic elicitation of the full potential of existing pre-tr…
  openalex Frank Xing 2024-08-13 置信度 0.72 Leverage (statistics)Summative assessmentContext (archaeology)Computer scienceGenerative grammar
• Reflexion: Language Agents with Verbal Reinforcement Learning
  Large language models (LLMs) have been increasingly used to interact with external environments (e.g., games, compilers, APIs) as goal-driven agents. However, it remains challenging for these language agents to quickly and efficiently learn from trial-and-erro…
  openalex Noah Shinn, Cassano, Federico, Berman, Edward, Gopinath, Ashwin 等 2023-03-20 置信度 0.72 Computer scienceReinforcement learningCoding (social sciences)Benchmark (surveying)Compiler
• Application of LLM Agents in Recruitment: A Novel Framework for Resume Screening
  The automation of resume screening is a crucial aspect of the recruitment process in organizations. Automated resume screening systems often encompass a range of natural language processing (NLP) tasks. This paper introduces a novel Large Language Models (LLMs…
  openalex Chengguang Gan, Qinghao Zhang, Tatsunori Mori 2024-01-16 置信度 0.72 Automatic summarizationComputer scienceArtificial intelligenceGrading (engineering)Engineering
• AIKernel Semantic DSL Compiler and Deterministic Agent Execution Architecture
  AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks. AutoGen agents are customizable, conversable, and can operate in various modes that employ combinati…
  openalex Wu, Qingyun, Gagan Bansal, Jieyu Zhang, Yiran Wu 等 2023-08-16 置信度 0.72 ConverseConversationComputer scienceCoding (social sciences)Entertainment
• Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering
  Recent progress with LLM-based agents has shown promising results across various tasks.However, their use in answering questions from knowledge bases remains largely unexplored.Implementing a KBQA system using traditional methods is challenging due to the shor…
  openalex Chang Zong, Yuchen Yan, Weiming Lü, Jian Shao 等 2024-01-01 置信度 0.72 Triad (sociology)Question answeringComputer scienceKnowledge baseOpen Knowledge Base Connectivity
• The TRIPOD-LLM reporting guideline for studies using large language models
  openalex Jack Gallifant, Majid Afshar, Saleem Ameen, Yindalon Aphinyanaphongs 等 2025-01-01 置信度 0.72 Tripod (photography)ChecklistGuidelineStandardizationComputer science
• PENTEST-AI, an LLM-Powered Multi-Agents Framework for Penetration Testing Automation Leveraging Mitre Attack
  In the digital transformation era, the surge of better development technologies and citizen developers disrupted the space of innovation by increasing the number and complexity of applications used in production. This context prompts advanced cybersecurity mea…
  openalex Stanislas G. Bianou, Rodrigue G. Batogna 2024-09-02 置信度 0.72 Computer scienceAutomationPenetration (warfare)Software engineeringEngineering
• Mitigating LLM Hallucinations Using a Multi-Agent Framework
  The rapid advancement of Large Language Models (LLMs) has led to substantial investment in enhancing their capabilities and expanding their feature sets. Despite these developments, a critical gap remains between model sophistication and their dependable deplo…
  openalex Ahmed M. Darwish, Essam A. Rashed, Ghada Khoriba 2025-06-21 置信度 0.72 PsychologyPsychotherapistComputer science
• LLM Agents for Education: Advances and Applications
  Large Language Model (LLM) agents are transforming education by automating complex pedagogical tasks and enhancing both teaching and learning processes. In this survey, we present a systematic review of recent advances in applying LLM agents to address key cha…
  openalex Zhendong Chu, Shen Wang, Jianhe Xie, Tinghui Zhu 等 2025-01-01 置信度 0.72 Computer scienceEngineeringMedicineTroubleshootingKey (lock)
• Mental-LLM
  Advances in large language models (LLMs) have empowered a variety of applications. However, there is still a significant gap in research when it comes to understanding and enhancing the capabilities of LLMs in the field of mental health. In this work, we prese…
  openalex Xuhai Xu, Bingsheng Yao, Yuanzhe Dong, Saadia Gabriel 等 2024-03-06 置信度 0.72 Mental healthSet (abstract data type)Computer scienceTask (project management)Variety (cybernetics)
• MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution
  In software development, resolving the emergent issues within GitHub repositories is a complex challenge that involves not only the incorporation of new code but also the maintenance of existing code. Large Language Models (LLMs) have shown promise in code gen…
  openalex Wei Tao, Yucheng Zhou, Wang, Yanlin, Wenqiang Zhang 等 2024-03-26 置信度 0.72 Computer scienceResolution (logic)Artificial intelligence
• Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents
  In this paper, we present a novel framework for enhancing the capabilities of large language models (LLMs) by leveraging the power of multi-agent systems. Our framework introduces a collaborative environment where multiple intelligent agent components, each wi…
  openalex Yashar Talebirad, Amirhossein Nadiri 2023-06-05 置信度 0.72 Computer scienceScalabilityIntelligent agentMulti-agent systemWork (physics)
• From LLM to Agent: A large-language-model-driven machine learning framework for catalyst design of MgH2 dehydrogenation
  • AI framework automates MgH 2 catalyst data extraction from literature. • LLM to Agent approach accelerates MgH 2 catalyst discovery and design. • Machine learning predicts MgH 2 dehydrogenation with high accuracy. • Cat-Advisor provides actionable catalyst d…
  openalex Tongao Yao, Yang Yang, Jianghao Cai, Rui Liu 等 2025-10-22 置信度 0.72 DehydrogenationComputer scienceMachine learningArtificial intelligenceHydrogen storage
• KG4Diagnosis: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Medical Diagnosis
  Integrating Large Language Models (LLMs) in healthcare diagnosis demands systematic frameworks that can handle complex medical scenarios while maintaining specialized expertise. We present KG4Diagnosis, a novel hierarchical multi-agent framework that combines …
  openalex Kaiwen Zuo, Yirui Jiang, Fan Mo, Píetro Lió 2024-12-22 置信度 0.72 GraphKnowledge graphComputer scienceArtificial intelligenceData science
• ExpeL: LLM Agents Are Experiential Learners
  The recent surge in research interest in applying large language models (LLMs) to decision-making tasks has flourished by leveraging the extensive world knowledge embedded in LLMs. While there is a growing demand to tailor LLMs for custom decision-making tasks…
  openalex Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Gaetan Lin 等 2024-03-24 置信度 0.72 Experiential learningPsychologyExperiential educationMathematics educationMedical education
• Character-LLM: A Trainable Agent for Role-Playing
  Large language models (LLMs) can be used to serve as agents to simulate human behaviors, given the powerful ability to understand human instructions and provide high-quality generated texts. Such ability stimulates us to wonder whether LLMs can simulate a pers…
  openalex Yunfan Shao, Linyang Li, Junqi Dai, Xipeng Qiu 2023-01-01 置信度 0.72 Character (mathematics)MemorizationWonderCleopatraComputer science
• SurrealDriver: Designing LLM-powered Generative Driver Agent Framework based on Human Drivers' Driving-thinking Data
  Leveraging advanced reasoning capabilities and extensive world knowledge of large language models (LLMs) to construct generative agents for solving complex real-world problems is a major trend. However, LLMs inherently lack embodiment as humans, resulting in s…
  openalex Ye Jin, Yang, Ruoxuan, Yi, Zhijie, Xiaoxi Shen 等 2023-09-22 置信度 0.72 Computer scienceGenerative grammarContext (archaeology)Human–computer interactionDriving simulation
• Engineering LLM Powered Multi-Agent Framework for Autonomous CloudOps
  Cloud Operations (CloudOps) is a rapidly growing field focused on the automated management and optimization of cloud infrastructure which is essential for organizations nav-igating increasingly complex cloud environments. MontyCloud Inc. is one of the major co…
  openalex Kannan Parthasarathy, Karthik Vaidhyanathan, Rudra Dhar, Venkat Krishnamachari 等 2025-04-27 置信度 0.72 Computer scienceSystems engineeringEngineering
• Evaluating large language models as agents in the clinic
  Recent developments in large language models (LLMs) have unlocked opportunities for healthcare, from information synthesis to clinical decision support. These LLMs are not just capable of modeling language, but can also act as intelligent “agents” that interac…
  openalex Nikita Mehandru, Brenda Y. Miao, Eduardo Rodriguez Almaraz, Madhumita Sushil 等 2024-04-03 置信度 0.72 WorkflowProcess (computing)Computer scienceFidelityCorporate governance
• Penhealnet: An Agent-Based Llm Framework for Automated Pentesting and Optimal Remediation
  openalex Junjie Huang, Quanyan Zhu 2024-01-01 置信度 0.72 Environmental remediationComputer scienceEnvironmental scienceBiologyEcology
• Framework for LLM applications in manufacturing
  In the era of Industry 4.0, the proliferation of data within manufacturing environments has presented both unprecedented opportunities and challenges. This paper introduces a framework that capitalizes on the capabilities of Large Language Models (LLMs) to rev…
  openalex Cristian I. Garcia, Marcus A. DiBattista, Tomás A. Letelier, Hunter D. Halloran 等 2024-10-01 置信度 0.72 Computer scienceManufacturing engineeringEngineeringEngineering drawing
• The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges
  Agentic AI systems are a recently emerged and important approach that goes beyond traditional AI, generative AI, and autonomous systems by focusing on autonomy, adaptability, and goal-driven reasoning. This study provides a clear review of agentic AI systems b…
  openalex Ajay Bandi, Bhavani Kongari, Roshini Naguru, Sahitya Pasnoor 等 2025-09-04 置信度 0.72 Computer scienceArtificial intelligenceData scienceMachine learningSoftware engineering
• SurrealDriver: Designing LLM-powered Generative Driver Agent Framework based on Human Drivers’ Driving-thinking Data
  Leveraging advanced reasoning capabilities and extensive world knowledge of large language models (LLMs) to construct generative agents for solving complex real-world problems is a major trend. However, LLMs inherently lack embodiment as humans, resulting in s…
  openalex Jin Ye, Ruoxuan Yang, Zhijie Yi, Xiaoxi Shen 等 2024-10-14 置信度 0.72 Computer scienceGenerative grammarHuman–computer interactionSystems engineeringArtificial intelligence
• Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection
  Large Language Models (LLMs) are increasingly being integrated into applications, with versatile functionalities that can be easily modulated via natural language prompts. So far, it was assumed that the user is directly prompting the LLM. But, what if it is n…
  openalex Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres 等 2023-11-21 置信度 0.72 Computer scienceExploitComputer securitySoftware deploymentInterface (matter)
• AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML
  Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline, such as optimal model search and hyperparameter tuning. Existing AutoML systems often require technical expertise to set up complex tools, which is …
  openalex Patara Trirat, Wonyong Jeong, Sung Ju Hwang 2024-10-03 置信度 0.72 Pipeline (software)Computer scienceProgramming language
• Creating Large Language Model Applications Utilizing LangChain: A Primer on Developing LLM Apps Fast
  This study focuses on the utilization of Large Language Models (LLMs) for the rapid development of applications, with a spotlight on LangChain, an open-source software library. LLMs have been rapidly adopted due to their capabilities in a range of tasks, inclu…
  openalex Oğuzhan Topsakal, Tahir Çetin Akıncı 2023-07-22 置信度 0.72 BespokeComputer scienceModular designDebuggingSoftware engineering
• An evaluation framework for clinical use of large language models in patient interaction tasks
  openalex Shreya Johri, Jae‐Hwan Jeong, Benjamin A. Tran, Daniel I. Schlessinger 等 2025-01-01 置信度 0.72 CraftSet (abstract data type)Medical historyComputer sciencePsychology
• Stance Detection with Collaborative Role-Infused LLM-Based Agents
  Stance detection automatically detects the stance in a text towards a target, vital for content analysis in web and social media research. Despite their promising capabilities, LLMs encounter challenges when directly applied to stance detection. First, stance …
  openalex Xiaochong Lan, Chen Gao, Depeng Jin, Yong Li 2024-05-28 置信度 0.72 Computer sciencePsychology
• Evaluation and Benchmarking of LLM Agents: A Survey
  The rise of LLM-based agents has opened new frontiers in AI applications, yet evaluating these agents remains a complex and underdeveloped area.This survey provides an in-depth overview of the emerging field of LLM agent evaluation, introducing a twodimensiona…
  openalex Mahmoud Mohammadi, Yipeng Li, Jane C. Lo, Wendy Yip 2025-08-03 置信度 0.72 BenchmarkingComputer scienceBusinessMarketing
• HEAL-KGGen: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Genetic Biomarker-Based Medical Diagnosis
  ABSTRACT The discovery and validation of genetic biomarkers across diverse diseases demand intelligent systems capable of integrating complex multi-omics data with clinical relevance. We introduce HEAL-KGGen, an end-to-end framework that enhances Large Languag…
  preprints Kaiwen Zuo, Zixuan Zhong, Peizhou Huang, Shiyan Tang 等 2025 置信度 0.74 BiomarkerComputer scienceGraphComputational biologyArtificial intelligence
• AGILE: A Novel Reinforcement Learning Framework of LLM Agents
  We introduce a novel reinforcement learning framework of LLM agents named AGILE (AGent that Interacts and Learns from Environments) designed to perform complex conversational tasks with users, leveraging LLMs, memory, tools, and interactions with experts. The …
  openalex Peiyuan Feng, Yichen He, Guanhua Huang, Yuan Lin 等 2024-01-01 置信度 0.72 Computer scienceReinforcement learningArtificial intelligenceAction (physics)Key (lock)
• Multi-Model Dialectical Evaluation of LLM Reasoning Chains: A Structured Framework with Dual Scoring Agents
  (1) Background and objectives: Large language models (LLMs) such as GPT, Mistral, and LLaMA exhibit strong capabilities in text generation, yet assessing the quality of their reasoning—particularly in open-ended and argumentative contexts—remains a persistent …
  openalex Cătălin Anghel, Andreea Alexandra Anghel, Emilia Pecheanu, Ioan Șușnea 等 2025-08-01 置信度 0.72 Dual (grammatical number)DialecticComputer scienceArtificial intelligenceEpistemology
• Balancing performance and cost of LLMs in a multi-agent framework for BIM data retrieval
  This study explores strategies for optimizing the use of large language models (LLMs) in Building Information Modeling (BIM) data retrieval. BIM data retrieval plays a crucial role in enhancing the efficiency and effectiveness of building management and constr…
  openalex Deli Liu, Xiaoping Zhou, Yu Li 2025-01-27 置信度 0.72 Building information modelingBusinessComputer scienceRisk analysis (engineering)Engineering
• FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design
  Recent advancements in Large Language Models (LLMs) have exhibited notable efficacy in question-answering (QA) tasks across diverse domains. Their prowess in integrating extensive web knowledge has fueled interest in developing LLM-based autonomous agents. Whi…
  openalex Yangyang Yu, Haohang Li, Cheng Zhi, Yuechen Jiang 等 2024-05-20 置信度 0.72 Computer scienceInterpretabilityTrading strategyFinancial marketArtificial intelligence
• Enhancing Anomaly Detection in Financial Markets with an LLM-based Multi-Agent Framework
  This paper introduces a Large Language Model (LLM)-based multi-agent framework designed to enhance anomaly detection within financial market data, tackling the longstanding challenge of manually verifying system-generated anomaly alerts. The framework harnesse…
  openalex Taejin Park 2024-03-28 置信度 0.72 Anomaly detectionAnomaly (physics)BusinessFinancial marketFinance
• ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models
  ChatMOF is an artificial intelligence (AI) system that is built to predict and generate metal-organic frameworks (MOFs). By leveraging a large-scale language model (GPT-4, GPT-3.5-turbo, and GPT-3.5-turbo-16k), ChatMOF extracts key details from textual inputs …
  openalex Yeonghun Kang, Jihan Kim 2024-06-03 置信度 0.72 Computer scienceVariety (cybernetics)Pipeline (software)Key (lock)Artificial intelligence
• A Multi-Agent LLM Environment for Software Design and Refactoring: A Conceptual Framework
  Modern software systems demand continuous evolution to maintain performance, scalability, and security. Traditional single-agent AI-driven code refactoring approaches are often limited in addressing the multi-faceted constraints (e.g., performance, security, m…
  openalex Vasanth Rajendran, Dinesh Besiahgari, Sachin C. Patil, Manjunath Chandrashekaraiah 等 2025-03-22 置信度 0.72 Code refactoringComputer scienceSoftware engineeringConceptual designConceptual model
• LLM-Based Framework for Administrative Task Automation in Healthcare
  Artificial Intelligence (AI) has been transformative in the healthcare sector, leading to enhanced precision in medical diagnosis, more effective treatment options, and a significant improvement in patient safety. However, computer-based administrative tasks, …
  openalex Senay A. Gebreab, Khaled Salah, Raja Jayaraman, Muhammad Habib ur Rehman 等 2024-04-29 置信度 0.72 AutomationTask (project management)Health careComputer scienceTask analysis
• GPT-4 Technical Report
  Abstract—Large Language Models (LLMs) suffer from inherent stochasticity, limiting their utility in high-stakes enterprise environments where determinism and auditability are required. This paper introduces the MFOUR Vibe Framework (MVF), a platform-agnostic a…
  openalex OpenAI, Achiam, Josh, Adler, Steven, Agarwal, Sandhini 等 2023-03-15 置信度 0.72 TransformerComputer scienceSecurity tokenProcess (computing)Scale (ratio)
• MMUEChange: A generalized LLM agent framework for intelligent multi-modal urban environment change analysis
  openalex Zixuan Xiao, Jun Ma, Siwei Zhang 2026-01-06 置信度 0.72 Computer scienceModular designUrban planningTask (project management)Quality (philosophy)
• HackSynth: LLM Agent and Evaluation Framework for Autonomous Penetration Testing
  We introduce HackSynth, a novel Large Language Model (LLM)-based agent capable of autonomous penetration testing. HackSynth's dual-module architecture includes a Planner and a Summarizer, which enable it to generate commands and process feedback iteratively. T…
  openalex Lajos Muzsai, David Imolai, András Lukács 2024-12-02 置信度 0.72 Penetration (warfare)Computer scienceBusinessEngineeringOperations research
• Evaluating Faithfulness in Agentic RAG Systems for e-Governance Applications Using LLM-Based Judging Frameworks
  As Large Language Models (LLMs) are core components in Retrieval-Augmented Generation (RAG) systems for knowledge-intensive tasks, concerns regarding hallucinations, redundancy, and unverifiable outputs have intensified, particularly in high-stakes domains, su…
  openalex George Papageorgiou, Vangelis Sarlis, Manolis Μaragoudakis, Ioannis Magnisalis 等 2025-12-03 置信度 0.72 InterpretabilityComputer scienceRedundancy (engineering)Artificial intelligenceVocabulary
• LLM-augmented multi-agent cooperative framework for medical case retrieval in cardiology
  Abstract Retrieving relevant medical cases or documents is a critical information retrieval (IR) task in clinical decision support, particularly in cardiology, yet traditional search methods struggle with complex semantic queries in healthcare. Recent advances…
  openalex Lang Deng, Huanhuan Hu, Kongjie Lu, Ping He 2025-10-20 置信度 0.72 Computer scienceInformation retrievalRelevance (law)Task (project management)Question answering
• AI-powered Automatic Item Generation for Psychological Tests: A Conceptual Framework for an LLM-based Multi-Agent AIG System
  Abstract Large Language Models (LLMs) are transforming industrial-organizational psychology and human resource management, with one of their most promising applications being automatic item generation (AIG) for psychological test development. Although recent a…
  openalex Philseok Lee, Mina Son, Zihao Jia 2025-08-26 置信度 0.72 Industrial and organizational psychologyPsychologyApplied psychologyConceptual modelSocial psychology
• Self-Collaboration Code Generation via ChatGPT
  Although large language models (LLMs) have demonstrated remarkable code-generation ability, they still struggle with complex tasks. In real-world software development, humans usually tackle complex tasks through collaborative teamwork, a strategy that signific…
  openalex Yihong Dong, Xue Jiang, Zhi Jin, Ge Li 2024-06-12 置信度 0.72 Computer scienceCode generationCode (set theory)Software engineeringProgramming language
• TradingAgents: Multi-Agents LLM Financial Trading Framework
  Significant progress has been made in automated problem-solving using societies of agents powered by large language models (LLMs). In finance, efforts have largely focused on single-agent systems handling specific tasks or multi-agent frameworks independently …
  openalex Xiao, Yijia, Edward W. Sun, Di Luo, Wei Wang 2024-12-28 置信度 0.72 BusinessFinanceFinancial system
• ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
  Text evaluation has historically posed significant challenges, often demanding substantial labor and time cost. With the emergence of large language models (LLMs), researchers have explored LLMs' potential as alternatives for human evaluation. While these sing…
  openalex Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu 等 2023-08-14 置信度 0.72 Computer scienceConstruct (python library)Quality (philosophy)Process (computing)Bridge (graph theory)
• LLM-enabled multi-agent framework for natural language interaction with graph-based digital twins
  Digital twins are increasingly used in the Architecture, Engineering, and Construction (AEC) industry, but their adoption is often hindered by the need for specialised knowledge, such as database querying. This paper presents Graph-DT-GPT, a multi-agent framew…
  openalex Yuandong Pan, Mudan Wang, Linjun Lu, Rabindra Lamsal 等 2026-01-27 置信度 0.72 Computer scienceCorrectnessScalabilityModular designGraph
• MedAgentBench: A Virtual EHR Environment to Benchmark Medical LLM Agents
  BACKGROUND Recent large language models (LLMs) have demonstrated significant advancements, particularly in their ability to serve as agents, thereby surpassing their traditional role as chatbots.These agents can leverage their planning and tool utilization cap…
  openalex Yixing Jiang, Kameron Collin Black, Gloria Geng, Dae-Gyun Park 等 2025-08-14 置信度 0.72 Computer scienceHealth careLeverage (statistics)Software deploymentBenchmark (surveying)
• SecureGov-Agent: A Governance-Centric Multi-Agent Framework for Privacy-Preserving and Attack-Resilient LLM Agents
  Large Language Model (LLM)-based multi-agent systems have demonstrated remarkable capabilities across di- verse applications, yet they face critical security challenges in- cluding backdoor attacks, prompt injection, and privacy leakage. Existing defense mecha…
  openalex Jinyu Chen, Jixiao Yang, Ziyang Zeng, Z. Jennifer Huang 等 2025-12-26 置信度 0.72 Computer scienceAction (physics)Context (archaeology)Key (lock)Perspective (graphical)
• AgentsBench: A Multi-Agent LLM Simulation Framework for Legal Judgment Prediction
  The justice system has increasingly applied AI techniques for legal judgment to enhance efficiency. However, most AI techniques focus on decision-making outcomes, failing to capture the deliberative nature of the real-world judicial process. To address these c…
  openalex Cong Jiang, Xiaolei Yang 2025-08-01 置信度 0.72 Computer scienceArtificial intelligence
• MM-LLMs: Recent Advances in MultiModal Large Language Models
  openalex Duzhen Zhang, Yahan Yu, Jiahua Dong, Chenxing Li 等 2024-01-01 置信度 0.72 Computer science
• A Dual-Agent Collaboration Framework Based on LLMs for Nursing Robots to Perform Bimanual Coordination Tasks
  Dual-arm coordination is a fundamental problem in humanoid nursing robot. Large language model (LLM)-driven dual-arm collaboration is gradually becoming a research hotspot in this field. However, the single-thread LLM task planner lacks the ability of co-sched…
  openalex Zhendong Zhao, X. Yue, Jiexin Xie, Chuanhong Fang 等 2025-01-24 置信度 0.72 Dual (grammatical number)RobotHuman–computer interactionComputer scienceKnowledge management
• AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework
  Rare, yet critical, scenarios pose a significant challenge in testing and evaluating autonomous driving planners. Relying solely on real-world driving scenes requires collecting massive datasets to capture these scenarios. While automatic generation of traffic…
  openalex Yu Yao, Salil Bhatnagar, Markus Mazzola, Vasileios Belagiannis 等 2025-10-19 置信度 0.72 Computer scienceKey (lock)Control (management)Domain (mathematical analysis)Quality (philosophy)
• CellAgent: An LLM-driven Multi-Agent Framework for Automated Single-cell Data Analysis
  Single-cell RNA sequencing (scRNA-seq) data analysis is crucial for biological research, as it enables the precise characterization of cellular heterogeneity. However, manual manipulation of various tools to achieve desired outcomes can be labor-intensive for …
  openalex Yihang Xiao, Jinyi Liu, Yan Zheng, Xiaohan Xie 等 2024-07-13 置信度 0.72 Computer scienceData scienceData mining
• Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agents
  Scene simulation in autonomous driving has gained significant attention because of its huge potential for generating customized data. However, existing editable scene simulation approaches face limitations in terms of user interaction efficiency, multi-camera …
  openalex Yuxi Wei, Zi Wang, Yifan Lu, Chenxin Xu 等 2024-06-16 置信度 0.72 Computer scienceHuman–computer interaction
• A Plan Reuse Mechanism for LLM-Driven Agent
  Integrating large language models (LLMs) into personal assistants, like Xiao Ai and Blue Heart V, effectively enhances their ability to interact with humans, solve complex tasks, and manage IoT devices. Such assistants are also termed LLM-driven agents. Upon r…
  arxiv Guopeng Li, Ruiqi Wu, Haisheng Tan 2025-12-24T18:08:03Z 置信度 0.78 cs.MA
• Small LLMs Are Weak Tool Learners: A Multi-LLM Agent
  Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs, empowering them to interact with external tools (e.g., APIs, functions) and complete various tasks in a self-directed fashion. The challenge of tool use demands that LL…
  arxiv Weizhou Shen, Chenliang Li, Hongzhan Chen, Ming Yan 等 2024-01-14T16:17:07Z 置信度 0.78 cs.AIcs.CL
• YouZhi: Towards High-Concurrency Financial LLMs via Adaptive GQA-to-MLA Transition
  Large language models (LLMs) drive significant financial innovations, yet their high-concurrency deployment is severely bottlenecked by KV cache memory overhead, which inflates infrastructure costs and throttles scalability. To address this, we propose YouZhi-…
  arxiv PSBC LLM Team, Huawei LLM Team, Ruihan Long, Junjie Wu 等 2026-06-04T08:44:37Z 置信度 0.78 cs.CL
• PhishDebate: An LLM-Based Multi-Agent Framework for Phishing Website Detection
  Phishing websites remain a major cybersecurity threat, exploiting deceptive structures, brand impersonation, and social engineering to evade detection. Recent advances in large language models (LLMs) have improved phishing detection through contextual understa…
  arxiv Wenhao Li, Selvakumar Manickam, Yung-wey Chong, Shankar Karuppayah 2025-06-18T17:33:18Z 置信度 0.78 cs.CR
• Agents at Risk: How Users Unwittingly Undermine LLM Safety
  Large language model (LLM)-based agents are increasingly deployed in applications, such as trip-planning agents and web-use agents, to perform complex planning and execution tasks. Prior work has shown that LLM-based agents are vulnerable to context confusion,…
  arxiv Fengchao Chen, Tingmin Wu, Van Nguyen, Surya. Nepal 等 2026-01-14T03:29:13Z 置信度 0.78 cs.CR
• What Twelve LLM Agent Benchmark Papers Disclose About Themselves: A Pilot Audit and an Open Scoring Schema
  We read twelve well-known LLM agent benchmark papers and recorded, dimension by dimension, what each paper actually says about how its evaluation was run. The motivation came from a familiar frustration: two papers will report results on the same benchmark wit…
  arxiv Mahdi Naser Moghadasi, Faezeh Ghaderi 2026-05-20T17:02:36Z 置信度 0.78 cs.LG
• The Social Laboratory: A Psychometric Framework for Multi-Agent LLM Evaluation
  As Large Language Models (LLMs) transition from static tools to autonomous agents, traditional evaluation benchmarks that measure performance on downstream tasks are becoming insufficient. These methods fail to capture the emergent social and cognitive dynamic…
  arxiv Zarreen Reza 2025-10-01T07:10:28Z 置信度 0.78 cs.AIcs.MA
• SkCC: Portable and Secure Skill Compilation for Cross-Framework LLM Agents
  LLM agents increasingly rely on reusable skills (e.g., SKILL markdown files) to execute complex tasks, yet these artifacts lack portability: agent frameworks are highly sensitive to prompt formatting, leading to a large performance variation for the same skill…
  arxiv Yipeng Ouyang, Yi Xiao, Yuhao Gu, Xianwei Zhang 2026-05-05T04:15:48Z 置信度 0.78 cs.CRcs.AI
• ODA: Observation-Driven Agent for integrating LLMs and Knowledge Graphs
  The integration of Large Language Models (LLMs) and knowledge graphs (KGs) has achieved remarkable success in various natural language processing tasks. However, existing methodologies that integrate LLMs and KGs often navigate the task-solving process solely …
  arxiv Lei Sun, Zhengwei Tao, Youdi Li, Hiroshi Arakawa 2024-04-11T12:16:16Z 置信度 0.78 cs.CLcs.AI
• Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents
  Large language models (LLMs) have achieved success in acting as agents, which interact with environments through tools such as search engines. However, LLMs are optimized for language generation instead of tool use during training or alignment, limiting their …
  arxiv Renxi Wang, Haonan Li, Xudong Han, Yixuan Zhang 等 2024-02-18T17:10:07Z 置信度 0.78 cs.CL
• Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents
  Driven by the rapid development of Large Language Models (LLMs), LLM-based agents have been developed to handle various real-world applications, including finance, healthcare, and shopping, etc. It is crucial to ensure the reliability and security of LLM-based…
  arxiv Wenkai Yang, Xiaohan Bi, Yankai Lin, Sishuo Chen 等 2024-02-17T06:48:45Z 置信度 0.78 cs.CRcs.AIcs.CL
• Uncertainty Decomposition for Clarification Seeking in LLM Agents
  Recent position papers argue that the classical aleatoric/epistemic uncertainty framework is insufficient for interactive large language model (LLM) agents and call for underspecification-aware, decomposed, and communicable uncertainty representations that can…
  arxiv Gregory Matsnev 2026-06-17T19:59:32Z 置信度 0.78 cs.AIcs.CL
• Edu-Theater: A Data-Efficient Agent Framework for Scalable Learner Behavior Simulation through Staging Roll-Call
  Large-scale learner-task interaction data are crucial for intelligent educational systems but are costly to collect and constrained by privacy and learner engagement. Learner simulators play a critical role in simulating scalable learner behavior without the n…
  arxiv Weibo Gao, Qi Liu, Linan Yue, Zheng Zhang 等 2026-06-13T09:48:25Z 置信度 0.78 cs.LGcs.AIcs.IR
• FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making
  Large language models (LLMs) have demonstrated notable potential in conducting complex tasks and are increasingly utilized in various financial applications. However, high-quality sequential financial investment decision-making remains challenging. These tasks…
  arxiv Yangyang Yu, Zhiyuan Yao, Haohang Li, Zhiyang Deng 等 2024-07-09T05:52:26Z 置信度 0.78 cs.CL
• Agent Capsules: Quality-Gated Granularity Control for Multi-Agent LLM Pipelines
  A multi-agent pipeline with N agents typically issues N LLM calls per run. Merging agents into fewer calls (compound execution) promises token savings, but naively merged calls silently degrade quality through tool loss and prompt compression. We present Agent…
  arxiv Aninda Ray 2026-05-01T05:08:14Z 置信度 0.78 cs.CLcs.AIcs.SE
• Motion-Agent: A Conversational Framework for Human Motion Generation with LLMs
  While previous approaches to 3D human motion generation have achieved notable success, they often rely on extensive training and are limited to specific tasks. To address these challenges, we introduce Motion-Agent, an efficient conversational framework design…
  arxiv Qi Wu, Yubo Zhao, Yifan Wang, Xinhang Liu 等 2024-05-27T09:57:51Z 置信度 0.78 cs.CV
• LLM-X: A Scalable Negotiation-Oriented Exchange for Communication Among Personal LLM Agents
  We propose a personal-LLM exchange (LLM-X), a scalable negotiation-oriented environment that enables direct, structured communication across populations of personal agents (LLMs), each representing an individual user. Unlike existing tool-centric protocols tha…
  arxiv Giuliano Lorenzoni, Paulo Alencar, Donald Cowan 2026-05-12T01:04:37Z 置信度 0.78 cs.AI
• Communication and Verification in LLM Agents towards Collaboration under Information Asymmetry
  While Large Language Model (LLM) agents are often approached from the angle of action planning/generation to accomplish a goal (e.g., given by language descriptions), their abilities to collaborate with each other to achieve a joint goal are not well explored.…
  arxiv Run Peng, Ziqiao Ma, Amy Pang, Sikai Li 等 2025-10-29T15:03:53Z 置信度 0.78 cs.CLcs.AI
• SSFF: Investigating LLM Predictive Capabilities for Startup Success through a Multi-Agent Framework with Enhanced Explainability and Performance
  LLM based agents have recently demonstrated strong potential in automating complex tasks, yet accurately predicting startup success remains an open challenge with few benchmarks and tailored frameworks. To address these limitations, we propose the Startup Succ…
  arxiv Xisen Wang, Yigit Ihlamur, Fuat Alican 2024-05-29T19:07:42Z 置信度 0.78 cs.AI
• Social Agent: Mastering Dyadic Nonverbal Behavior Generation via Conversational LLM Agents
  We present Social Agent, a novel framework for synthesizing realistic and contextually appropriate co-speech nonverbal behaviors in dyadic conversations. In this framework, we develop an agentic system driven by a Large Language Model (LLM) to direct the conve…
  arxiv Zeyi Zhang, Yanju Zhou, Heyuan Yao, Tenglong Ao 等 2025-10-06T09:41:37Z 置信度 0.78 cs.GRcs.CV
• Agent-R1: A Unified and Modular Framework for Agentic Reinforcement Learning
  Large language models (LLMs) have rapidly evolved from single-turn text generators into the foundation of increasingly capable agents. As these agents take on more complex reasoning, decision making, tool use, and long-horizon tasks, reinforcement learning (RL…
  arxiv Mingyue Cheng, Shuo Yu, Daoyu Wang, Qingchuan Li 等 2025-11-18T13:03:15Z 置信度 0.78 cs.CL
• Doing What They Say, Not What They Reason: Locating the Faithfulness Gap in LLM Agents
  Do LLM agents act on the reasoning they state? This question of process fidelity is central to LLM-based social simulation, yet hard to measure where no reference for correct behavior exists. We study it in a controlled setting: a Texas Poker simulator with a …
  arxiv Yufeng Wang 2026-05-30T02:02:21Z 置信度 0.78 cs.AI
• Nuclear Deployed: Analyzing Catastrophic Risks in Decision-making of Autonomous LLM Agents
  Large language models (LLMs) are evolving into autonomous decision-makers, raising concerns about catastrophic risks in high-stakes scenarios, particularly in Chemical, Biological, Radiological and Nuclear (CBRN) domains. Based on the insight that such risks c…
  arxiv Rongwu Xu, Xiaojian Li, Shuo Chen, Wei Xu 2025-02-17T02:11:17Z 置信度 0.78 cs.CLcs.AIcs.CRcs.CY
• Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering
  Large language model (LLM) agents are increasingly built less by changing model weights than by reorganizing the runtime around them. Capabilities that earlier systems expected the model to recover internally are now externalized into memory stores, reusable s…
  arxiv Chenyu Zhou, Huacan Chai, Wenteng Chen, Zihan Guo 等 2026-04-09T13:19:41Z 置信度 0.78 cs.SEcs.MA
• LLM-as-a-Verifier: A General-Purpose Verification Framework
  Scaling pre-training, post-training, and test-time compute have become the central paradigms for improving the capabilities of LLMs. In this work, we identify verification, the ability to determine the correctness of a solution, as a new scaling axis. To unloc…
  arxiv Jacky Kwok, Shulu Li, Pranav Atreya, Yuejiang Liu 等 2026-07-06T17:59:35Z 置信度 0.78 cs.AIcs.CLcs.LGcs.MAcs.RO
• When Lower Privileges Suffice: Investigating Over-Privileged Tool Selection in LLM Agents
  As LLM agents increasingly select tools autonomously, their choices among tools with different privileges become safety-relevant. However, prior tool-selection studies focus on safety-agnostic metadata preferences, leaving privilege-sensitive choices underexpl…
  arxiv Kaiyue Yang, Yuyan Bu, Jingwei Yi, Yuchi Wang 等 2026-06-18T09:54:48Z 置信度 0.78 cs.SEcs.AIcs.CL
• EvalSafetyGap: A Hybrid Survey and Conceptual Framework for LLM Evaluation-Safety Failures
  This paper presents a systematic survey and conceptual synthesis of the shared measurement problem underlying large language model (LLM) evaluation and AI safety: benchmark scores, reward signals, and safety metrics can improve while the capabilities and align…
  arxiv Buğra Alperen Uluırmak, Rifat Kurban 2026-06-29T12:33:06Z 置信度 0.78 cs.AIcs.CLcs.LGcs.SE
• Think-on-Graph 3.0: Efficient and Adaptive LLM Reasoning on Heterogeneous Graphs via Multi-Agent Dual-Evolving Context Retrieval
  Graph-based Retrieval-Augmented Generation (GraphRAG) has become the important paradigm for enhancing Large Language Models (LLMs) with external knowledge. However, existing approaches are constrained by their reliance on high-quality knowledge graphs: manuall…
  arxiv Xiaojun Wu, Cehao Yang, Xueyuan Lin, Chengjin Xu 等 2025-09-26T00:13:10Z 置信度 0.78 cs.CL
• Learning Hierarchical Procedural Memory for LLM Agents through Bayesian Selection and Contrastive Refinement
  We present MACLA, a framework that decouples reasoning from learning by maintaining a frozen large language model while performing all adaptation in an external hierarchical procedural memory. MACLA extracts reusable procedures from trajectories, tracks reliab…
  arxiv Saman Forouzandeh, Wei Peng, Parham Moradi, Xinghuo Yu 等 2025-12-22T01:56:28Z 置信度 0.78 cs.LGcs.AI
• RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents
  LLM recommendation agents increasingly produce structured recommendation reports: sets of items accompanied by natural-language justifications. Yet existing evaluations often reduce this setting to reranking small shortlisted candidate sets or judge reports ma…
  arxiv Imad Aouali, Flavian Vasile, Otmane Sakhi, Alexandre Gilotte 等 2026-05-11T18:55:32Z 置信度 0.78 cs.IRcs.AIcs.LG
• Agent Memory Below the Prompt: Persistent Q4 KV Cache for Multi-Agent LLM Inference on Edge Devices
  Multi-agent LLM systems on edge devices face a memory management problem: device RAM is too small to hold every agent's KV cache simultaneously. On Apple M4 Pro with 10.2 GB of cache budget, only 3 agents fit at 8K context in FP16. A 10-agent workflow must con…
  arxiv Yakov Pyotr Shkolnikov 2026-02-17T05:46:20Z 置信度 0.78 cs.LGcs.AI
• A Methodology for Selecting and Composing Runtime Architecture Patterns for Production LLM Agents
  Production LLM agents combine stochastic model outputs with deterministic software systems, yet the boundary between the two is rarely treated as a first-class architectural object. This paper names that boundary the stochastic-deterministic boundary (SDB): a …
  arxiv Vasundra Srinivasan 2026-05-19T17:54:21Z 置信度 0.78 cs.AIcs.SE
• DIAMOND: An LLM-Driven Agent for Context-Aware Baseball Highlight Summarization
  Traditional approaches -- such as Win Probability Added (WPA)-based ranking or computer vision-driven event detection -- can identify scoring plays but often miss strategic depth, momentum shifts, and storyline progression. Manual curation remains the gold sta…
  arxiv Jeonghun Kang, Soonmok Kwon, Joonseok Lee, Byung-Hak Kim 2025-06-03T01:10:20Z 置信度 0.78 cs.CLcs.AIcs.CV
• Task-Aware Delegation Cues for LLM Agents
  LLM agents increasingly present as conversational collaborators, yet human--agent teamwork remains brittle due to information asymmetry: users lack task-specific reliability cues, and agents rarely surface calibrated uncertainty or rationale. We propose a task…
  arxiv Xingrui Gu 2026-03-11T17:35:44Z 置信度 0.78 cs.HC
• TREK: A Travel Reasoning and Evaluation Kit for LLM Agents in Complex Trip Planning
  Travel planning is a demanding stress test for tool-using LLM agents: a usable itinerary is a single artifact that must be right along many axes at once - every flight, hotel, and attraction must exist and be bookable, the days must be physically traversable, …
  arxiv Jinhu Qi, Wentao Zhang, Siu Man Ng, Feiyang Xu 等 2026-07-29T14:35:29Z 置信度 0.78 cs.CL
• Computer Environments Elicit General Agentic Intelligence in LLMs
  Agentic intelligence in large language models (LLMs) requires not only model intrinsic capabilities but also interactions with external environments. Equipping LLMs with computers now represents a prevailing trend. However, the computer environment's intrinsic…
  arxiv Daixuan Cheng, Shaohan Huang, Yuxian Gu, Huatong Song 等 2026-01-22T18:57:09Z 置信度 0.78 cs.CLcs.AI