In recent years,Volunteered Geographic Information(VGI)has emerged as a crucial source of mapping data,contributed by users through crowdsourcing platforms such as OpenStreetMap.This paper presents a novel approach th...In recent years,Volunteered Geographic Information(VGI)has emerged as a crucial source of mapping data,contributed by users through crowdsourcing platforms such as OpenStreetMap.This paper presents a novel approach that Integrates Large Language Models(LLMs)into a fully automated mapping workflow,utilizing VGI data.The process leverages Prompt Engineering,which involves designing and optimizing input instructions to ensure the LLM produces desired mapping outputs.By constructing precise and detailed prompts,LLM agents are able to accurately interpret mapping requirements,and autonomously extract,analyze,and process VGI geospatial data.They dynamically interact with mapping tools to automate the entire mapping process—from data acquisition to map generation.This approach significantly streamlines the creation of high-quality mapping outputs,reducing the time and resources typically required for such tasks.Moreover,the system lowers the barrier for non-expert users,enabling them to generate accurate maps without extensive technical expertise.Through various case studies,we demonstrate the LLM application across different mapping scenarios,highlighting its potential to enhance the efficiency,accuracy,and accessibility of map production.The results suggest that LLM-powered mapping systems can not only optimize VGI data processing but also expand the usability of ubiquitous mapping across diverse fields,including urban planning and infrastructure development.展开更多
Objectives:This review aimed to systematically analyze the technological frameworks,application scenarios,and outcomes of large language model-driven agents(LLMDAs)in nursing practice,and to summarize ethical,technolo...Objectives:This review aimed to systematically analyze the technological frameworks,application scenarios,and outcomes of large language model-driven agents(LLMDAs)in nursing practice,and to summarize ethical,technological,and practical challenges,guiding future research and clinical implementation.Methods:This scoping review was conducted following the JBI guidelines.Five databases(PubMed,Embase,Web of Science,APA PsycNet,Cochrane Library)were systematically searched for peer-reviewed English-language studies from inception until September 9,2025.Eligible studies were screened by title and abstract,with full-text assessments conducted independently by two reviewers.Results:Twenty-five studies published between 2023 and 2025 were included,involving nine countries,primarily China(n=9)and the United States(n=9).Technological architectures were categorized into three types:collaborative models for solving complex tasks through multi-agent division of labor;augmentative models to enhance the accuracy of information outputs;and interactive models focusing on natural interactions and robotic task execution.Application scenarios included clinical,home-based,and community care.Studies indicated that LLMDAs can enhance diagnostic accuracy,optimize resource allocation,and improve patient experience.Primary ethical challenges identified included data privacy,reliability of generated content,and ambiguous attribution of responsibility.Conclusions:LLMDAs offer a novel paradigm for intelligent transformation in nursing care through integrative technological frameworks.They demonstrate considerable potential in enhancing clinical decision-making accuracy,efficiency of care delivery,and patient satisfaction.Addressing existing ethical,technical,and practical challenges is essential for facilitating broader clinical adoption.展开更多
Large language models(LLMs)have emerged as transformative tools with significant potential across healthcare and medicine.In clinical settings,they hold promises for tasks ranging from clinical decision support to pat...Large language models(LLMs)have emerged as transformative tools with significant potential across healthcare and medicine.In clinical settings,they hold promises for tasks ranging from clinical decision support to patient education.Advances in LLM agents further broaden their utility by enabling multimodal processing and multi-task handling in complex clinical workflows.However,evaluating the performance of LLMs in medical contexts presents unique challenges due to the high-risk nature of healthcare and the complexity of medical data.This paper provides a comprehensive overview of current evaluation practices for LLMs and LLM agents in medicine.We contributed 3 main aspects:First,we summarized data sources used in evaluations,including existing medical resources and manually designed clinical questions,offering a basis for LLM evaluation in medical settings.Second,we analyzed key medical task scenarios:closed-ended tasks,open-ended tasks,image processing tasks,and real-world multitask scenarios involving LLM agents,thereby offering guidance for further research across different medical applications.Third,we compared evaluation methods and dimensions,covering both automated metrics and human expert assessments,while addressing traditional accuracy measures alongside agent-specific dimensions,such as tool usage and reasoning capabilities.Finally,we identified key challenges and opportunities in this evolving field,emphasizing the need for continued research and interdisciplinary collaboration between healthcare professionals and computer scientists to ensure safe,ethical,and effective deployment of LLMs in clinical practice.展开更多
Large Language Models(LLMs)are complex artificial intelligence systems,which can understand,generate,and translate human languages.By analyzing large amounts of textual data,these models learn language patterns to per...Large Language Models(LLMs)are complex artificial intelligence systems,which can understand,generate,and translate human languages.By analyzing large amounts of textual data,these models learn language patterns to perform tasks such as writing,conversation,and summarization.Agents built on LLMs(LLM agents)further extend these capabilities,allowing them to process user interactions and perform complex operations in diverse task environments.However,during the processing and generation of massive data,LLMs and LLM agents pose a risk of sensitive information leakage,potentially threatening data privacy.This paper aims to demonstrate data privacy issues associated with LLMs and LLM agents to facilitate a comprehensive understanding.Specifically,we conduct an in-depth survey about privacy threats,encompassing passive privacy leakage and active privacy attacks.Subsequently,we introduce the privacy protection mechanisms employed by LLMs and LLM agents and provide a detailed analysis of their effectiveness.Finally,we explore the privacy protection challenges for LLMs and LLM agents as well as outline potential directions for future developments in this domain.展开更多
Next-generation nuclear reactor technologies,such as molten salt and fast reactors present complex analytical challenges that require advanced modeling and simulation tools.Yet,traditional workflows for Monte Carlo si...Next-generation nuclear reactor technologies,such as molten salt and fast reactors present complex analytical challenges that require advanced modeling and simulation tools.Yet,traditional workflows for Monte Carlo simulations like FLUKA are labor-intensive and error-prone,relying on manual input file generation and postprocessing.This limits scalability and efficiency.In this work,we present AutoFLUKA,a novel framework that leverages domain knowledge-embedded large language models(LLMs)and AI agents to automate the entire FLUKA simulation workflow from input file creation to execution management,and data analysis.AutoFLUKA also integrates Retrieval-Augmented Generation(RAG)and a web-based user-friendly graphical interface,enabling users to interact with the system in real time.Benchmarking against manual FLUKA simulations,AutoFLUKA demonstrated substantial improvements in resolving FLUKA error-related queries,particularly those arising from input file creation and execution.Traditionally,such issues are addressed through expert support on the FLUKA user forum,often resulting in significant delays.The resolution time for these queries was also reduced from several days to under one minute.Additionally,human-induced simulation errors were mitigated,and a high accuracy in key simulation metrics,such as neutron fluence and microdosimetric quantities,was achieved,with uncertainties below 0.001%for large sample sizes.The flexibility of AutoFLUKA was demonstrated through successful application to both general and specialized nuclear scenarios,and its design allows for straightforward extension to other simulation platforms.These results highlight AutoFLUKA’s potential to transform nuclear engineering analysis by enhancing productivity,reliability,and accessibility through AI-driven automation.展开更多
Recently,tool learning with large language models(LLMs)has emerged as a promising paradigm for augmenting the capabilities of LLMs to tackle highly complex problems.Despite growing attention and rapid advancements in ...Recently,tool learning with large language models(LLMs)has emerged as a promising paradigm for augmenting the capabilities of LLMs to tackle highly complex problems.Despite growing attention and rapid advancements in this field,the existing literature remains fragmented and lacks systematic organization,posing barriers to entry for newcomers.This gap motivates us to conduct a comprehensive survey of existing works on tool learning with LLMs.In this survey,we focus on reviewing existing literature from the two primary aspects(1)why tool learning is beneficial and(2)how tool learning is implemented,enabling a comprehensive understanding of tool learning with LLMs.We first explore the“why”by reviewing both the benefits of tool integration and the inherent benefits of the tool learning paradigm from six specific aspects.In terms of“how”,we systematically review the literature according to a taxonomy of four key stages in the tool learning workflow:task planning,tool selection,tool calling,and response generation.Additionally,we provide a detailed summary of existing benchmarks and evaluation methods,categorizing them according to their relevance to different stages.Finally,we discuss current challenges and outline potential future directions,aiming to inspire both researchers and industrial developers to further explore this emerging and promising area.展开更多
With the proliferation of data and increased complexity of clinical decision-making in the medical field,powerful computational tools are needed to assist physicians in making precise and reliable decisions.While the ...With the proliferation of data and increased complexity of clinical decision-making in the medical field,powerful computational tools are needed to assist physicians in making precise and reliable decisions.While the Large Language Models(LLMs)with billions of parameters in model size have obtained a series of achievements in a broad range of biomedical and healthcare applications,the issues in terms of reliability and stability are still needed to be addressed.To this end,we propose the framework of MedRad,a system that combines LLMs,knowledge engineering,Chain of Thought(CoT)reasoning,Retrieval-Augmented Generation(RAG)techniques,and intelligent agents(Agents)to improve clinical decision-making reliability.Based on fine-tuned LLMs and existing studies in the biomedical and healthcare domain,we further concentrate on how these techniques could be utilized to achieve highly reliable clinical decision-making in scenarios with varying complexity,such as medical knowledge QA and clinical diagnosis recommendations.Experimental results demonstrate that MedRad has the ability to provide high-quality decision paths in the above scenarios,and the potential to extend to more biomedical and healthcare scenarios through its loosely coupled design.展开更多
Alpha mining,which refers to the systematic discovery of data-driven signals predictive of future crosssectional returns,is a central task in quantitative research.Recent progress in large language models(LLMs)has spa...Alpha mining,which refers to the systematic discovery of data-driven signals predictive of future crosssectional returns,is a central task in quantitative research.Recent progress in large language models(LLMs)has sparked interest in LLM-based alpha mining frameworks,which offer a promising middle ground between humanguided and fully automated alpha mining approaches and deliver both speed and semantic depth.This study presents a structured review of emerging LLM-based alpha mining systems from an agentic perspective,and analyzes the functional roles of LLMs,ranging from miners and evaluators to interactive assistants.Despite early progress,key challenges remain,including simplified performance evaluation,limited numerical understanding,lack of diversity and originality,weak exploration dynamics,temporal data leakage,and black-box risks and compliance challenges.Accordingly,we outline future directions,including improving reasoning alignment,expanding to new data modalities,rethinking evaluation protocols,and integrating LLMs into more general-purpose quantitative systems.Our analysis suggests that LLM is a scalable interface for amplifying both domain expertise and algorithmic rigor,as it amplifies domain expertise by transforming qualitative hypotheses into testable factors and enhances algorithmic rigor for rapid backtesting and semantic reasoning.The result is a complementary paradigm,where intuition,automation,and language-based reasoning converge to redefine the future of quantitative research.展开更多
Autonomous agents have long been a research focus in academic and industry communities.Previous research often focuses on training agents with limited knowledge within isolated environments,which diverges significantl...Autonomous agents have long been a research focus in academic and industry communities.Previous research often focuses on training agents with limited knowledge within isolated environments,which diverges significantly from human learning processes,and makes the agents hard to achieve human-like decisions.Recently,through the acquisition of vast amounts of Web knowledge,large language models(LLMs)have shown potential in human-level intelligence,leading to a surge in research on LLM-based autonomous agents.In this paper,we present a comprehensive survey of these studies,delivering a systematic review of LLM-based autonomous agents from a holistic perspective.We first discuss the construction of LLM-based autonomous agents,proposing a unified framework that encompasses much of previous work.Then,we present a overview of the diverse applications of LLM-based autonomous agents in social science,natural science,and engineering.Finally,we delve into the evaluation strategies commonly used for LLM-based autonomous agents.Based on the previous studies,we also present several challenges and future directions in this field.展开更多
基金National Natural Science Foundation of china(No.42371446)Natural Science Foundatiorof Hubei Province(No.2024AFD412)Fundamental Research Funds for National Universities,China University of Geosciences(Wuhan)(No.2024XLA17).
文摘In recent years,Volunteered Geographic Information(VGI)has emerged as a crucial source of mapping data,contributed by users through crowdsourcing platforms such as OpenStreetMap.This paper presents a novel approach that Integrates Large Language Models(LLMs)into a fully automated mapping workflow,utilizing VGI data.The process leverages Prompt Engineering,which involves designing and optimizing input instructions to ensure the LLM produces desired mapping outputs.By constructing precise and detailed prompts,LLM agents are able to accurately interpret mapping requirements,and autonomously extract,analyze,and process VGI geospatial data.They dynamically interact with mapping tools to automate the entire mapping process—from data acquisition to map generation.This approach significantly streamlines the creation of high-quality mapping outputs,reducing the time and resources typically required for such tasks.Moreover,the system lowers the barrier for non-expert users,enabling them to generate accurate maps without extensive technical expertise.Through various case studies,we demonstrate the LLM application across different mapping scenarios,highlighting its potential to enhance the efficiency,accuracy,and accessibility of map production.The results suggest that LLM-powered mapping systems can not only optimize VGI data processing but also expand the usability of ubiquitous mapping across diverse fields,including urban planning and infrastructure development.
文摘Objectives:This review aimed to systematically analyze the technological frameworks,application scenarios,and outcomes of large language model-driven agents(LLMDAs)in nursing practice,and to summarize ethical,technological,and practical challenges,guiding future research and clinical implementation.Methods:This scoping review was conducted following the JBI guidelines.Five databases(PubMed,Embase,Web of Science,APA PsycNet,Cochrane Library)were systematically searched for peer-reviewed English-language studies from inception until September 9,2025.Eligible studies were screened by title and abstract,with full-text assessments conducted independently by two reviewers.Results:Twenty-five studies published between 2023 and 2025 were included,involving nine countries,primarily China(n=9)and the United States(n=9).Technological architectures were categorized into three types:collaborative models for solving complex tasks through multi-agent division of labor;augmentative models to enhance the accuracy of information outputs;and interactive models focusing on natural interactions and robotic task execution.Application scenarios included clinical,home-based,and community care.Studies indicated that LLMDAs can enhance diagnostic accuracy,optimize resource allocation,and improve patient experience.Primary ethical challenges identified included data privacy,reliability of generated content,and ambiguous attribution of responsibility.Conclusions:LLMDAs offer a novel paradigm for intelligent transformation in nursing care through integrative technological frameworks.They demonstrate considerable potential in enhancing clinical decision-making accuracy,efficiency of care delivery,and patient satisfaction.Addressing existing ethical,technical,and practical challenges is essential for facilitating broader clinical adoption.
基金supported by the Start-up Fund for RAPs under the Strategic Hiring Scheme(Grant No.P0048623)from HKSARthe Global STEM Professorship Scheme(Grant No.P0046113)。
文摘Large language models(LLMs)have emerged as transformative tools with significant potential across healthcare and medicine.In clinical settings,they hold promises for tasks ranging from clinical decision support to patient education.Advances in LLM agents further broaden their utility by enabling multimodal processing and multi-task handling in complex clinical workflows.However,evaluating the performance of LLMs in medical contexts presents unique challenges due to the high-risk nature of healthcare and the complexity of medical data.This paper provides a comprehensive overview of current evaluation practices for LLMs and LLM agents in medicine.We contributed 3 main aspects:First,we summarized data sources used in evaluations,including existing medical resources and manually designed clinical questions,offering a basis for LLM evaluation in medical settings.Second,we analyzed key medical task scenarios:closed-ended tasks,open-ended tasks,image processing tasks,and real-world multitask scenarios involving LLM agents,thereby offering guidance for further research across different medical applications.Third,we compared evaluation methods and dimensions,covering both automated metrics and human expert assessments,while addressing traditional accuracy measures alongside agent-specific dimensions,such as tool usage and reasoning capabilities.Finally,we identified key challenges and opportunities in this evolving field,emphasizing the need for continued research and interdisciplinary collaboration between healthcare professionals and computer scientists to ensure safe,ethical,and effective deployment of LLMs in clinical practice.
基金supported in part by the National Natural Science Foundation of China(62402288 and 62302063)the China Postdoctoral Science Foundation,China(2024M751811).
文摘Large Language Models(LLMs)are complex artificial intelligence systems,which can understand,generate,and translate human languages.By analyzing large amounts of textual data,these models learn language patterns to perform tasks such as writing,conversation,and summarization.Agents built on LLMs(LLM agents)further extend these capabilities,allowing them to process user interactions and perform complex operations in diverse task environments.However,during the processing and generation of massive data,LLMs and LLM agents pose a risk of sensitive information leakage,potentially threatening data privacy.This paper aims to demonstrate data privacy issues associated with LLMs and LLM agents to facilitate a comprehensive understanding.Specifically,we conduct an in-depth survey about privacy threats,encompassing passive privacy leakage and active privacy attacks.Subsequently,we introduce the privacy protection mechanisms employed by LLMs and LLM agents and provide a detailed analysis of their effectiveness.Finally,we explore the privacy protection challenges for LLMs and LLM agents as well as outline potential directions for future developments in this domain.
基金supported by the US Department of Energy Office of Nuclear Energy Distinguished Early Career Program under contract number DE-NE0009468support is provided by the Texas A&M Institute of Data Science(TAMIDS)Seed Program for AI,Computing,and Data Science。
文摘Next-generation nuclear reactor technologies,such as molten salt and fast reactors present complex analytical challenges that require advanced modeling and simulation tools.Yet,traditional workflows for Monte Carlo simulations like FLUKA are labor-intensive and error-prone,relying on manual input file generation and postprocessing.This limits scalability and efficiency.In this work,we present AutoFLUKA,a novel framework that leverages domain knowledge-embedded large language models(LLMs)and AI agents to automate the entire FLUKA simulation workflow from input file creation to execution management,and data analysis.AutoFLUKA also integrates Retrieval-Augmented Generation(RAG)and a web-based user-friendly graphical interface,enabling users to interact with the system in real time.Benchmarking against manual FLUKA simulations,AutoFLUKA demonstrated substantial improvements in resolving FLUKA error-related queries,particularly those arising from input file creation and execution.Traditionally,such issues are addressed through expert support on the FLUKA user forum,often resulting in significant delays.The resolution time for these queries was also reduced from several days to under one minute.Additionally,human-induced simulation errors were mitigated,and a high accuracy in key simulation metrics,such as neutron fluence and microdosimetric quantities,was achieved,with uncertainties below 0.001%for large sample sizes.The flexibility of AutoFLUKA was demonstrated through successful application to both general and specialized nuclear scenarios,and its design allows for straightforward extension to other simulation platforms.These results highlight AutoFLUKA’s potential to transform nuclear engineering analysis by enhancing productivity,reliability,and accessibility through AI-driven automation.
基金funded by the National Key R&D Program of China(2023YFA1008704),the National Natural Science Foundation of China(Grant No.62377044)Beijing Key Laboratory of Big Data Management and Analysis Methods,Major Innovation&Planning Interdisciplinary Platform for the“Double-First Class”Initiative,funds for building world-class universities(disciplines)of Renmin University of China,and PCC@RUC.The authors would like to extend their sincere gratitude to Yankai Lin for his constructive feedback throughout the development of this work.
文摘Recently,tool learning with large language models(LLMs)has emerged as a promising paradigm for augmenting the capabilities of LLMs to tackle highly complex problems.Despite growing attention and rapid advancements in this field,the existing literature remains fragmented and lacks systematic organization,posing barriers to entry for newcomers.This gap motivates us to conduct a comprehensive survey of existing works on tool learning with LLMs.In this survey,we focus on reviewing existing literature from the two primary aspects(1)why tool learning is beneficial and(2)how tool learning is implemented,enabling a comprehensive understanding of tool learning with LLMs.We first explore the“why”by reviewing both the benefits of tool integration and the inherent benefits of the tool learning paradigm from six specific aspects.In terms of“how”,we systematically review the literature according to a taxonomy of four key stages in the tool learning workflow:task planning,tool selection,tool calling,and response generation.Additionally,we provide a detailed summary of existing benchmarks and evaluation methods,categorizing them according to their relevance to different stages.Finally,we discuss current challenges and outline potential future directions,aiming to inspire both researchers and industrial developers to further explore this emerging and promising area.
基金funded by the CAMS Fund,Grant no.2024-ZHCH630-01.
文摘With the proliferation of data and increased complexity of clinical decision-making in the medical field,powerful computational tools are needed to assist physicians in making precise and reliable decisions.While the Large Language Models(LLMs)with billions of parameters in model size have obtained a series of achievements in a broad range of biomedical and healthcare applications,the issues in terms of reliability and stability are still needed to be addressed.To this end,we propose the framework of MedRad,a system that combines LLMs,knowledge engineering,Chain of Thought(CoT)reasoning,Retrieval-Augmented Generation(RAG)techniques,and intelligent agents(Agents)to improve clinical decision-making reliability.Based on fine-tuned LLMs and existing studies in the biomedical and healthcare domain,we further concentrate on how these techniques could be utilized to achieve highly reliable clinical decision-making in scenarios with varying complexity,such as medical knowledge QA and clinical diagnosis recommendations.Experimental results demonstrate that MedRad has the ability to provide high-quality decision paths in the above scenarios,and the potential to extend to more biomedical and healthcare scenarios through its loosely coupled design.
文摘Alpha mining,which refers to the systematic discovery of data-driven signals predictive of future crosssectional returns,is a central task in quantitative research.Recent progress in large language models(LLMs)has sparked interest in LLM-based alpha mining frameworks,which offer a promising middle ground between humanguided and fully automated alpha mining approaches and deliver both speed and semantic depth.This study presents a structured review of emerging LLM-based alpha mining systems from an agentic perspective,and analyzes the functional roles of LLMs,ranging from miners and evaluators to interactive assistants.Despite early progress,key challenges remain,including simplified performance evaluation,limited numerical understanding,lack of diversity and originality,weak exploration dynamics,temporal data leakage,and black-box risks and compliance challenges.Accordingly,we outline future directions,including improving reasoning alignment,expanding to new data modalities,rethinking evaluation protocols,and integrating LLMs into more general-purpose quantitative systems.Our analysis suggests that LLM is a scalable interface for amplifying both domain expertise and algorithmic rigor,as it amplifies domain expertise by transforming qualitative hypotheses into testable factors and enhances algorithmic rigor for rapid backtesting and semantic reasoning.The result is a complementary paradigm,where intuition,automation,and language-based reasoning converge to redefine the future of quantitative research.
基金the National Natural Science Foundation of China(Grant No.62102420)the Beijing Outstanding Young Scientist Program(No.BJJWZYJH012019100020098)。
文摘Autonomous agents have long been a research focus in academic and industry communities.Previous research often focuses on training agents with limited knowledge within isolated environments,which diverges significantly from human learning processes,and makes the agents hard to achieve human-like decisions.Recently,through the acquisition of vast amounts of Web knowledge,large language models(LLMs)have shown potential in human-level intelligence,leading to a surge in research on LLM-based autonomous agents.In this paper,we present a comprehensive survey of these studies,delivering a systematic review of LLM-based autonomous agents from a holistic perspective.We first discuss the construction of LLM-based autonomous agents,proposing a unified framework that encompasses much of previous work.Then,we present a overview of the diverse applications of LLM-based autonomous agents in social science,natural science,and engineering.Finally,we delve into the evaluation strategies commonly used for LLM-based autonomous agents.Based on the previous studies,we also present several challenges and future directions in this field.
