期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到748篇文章
< 1 2 … 38 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
When Federated Learning Meets Large Language Models:Taxonomy,Challenges,and Opportunities 认领 引用
1
作者 Shan Jiang Wenxin You +2 位作者 Haoran Zhang Shichang Xuan Jiaxing Shen 《Computers, Materials & Continua》 SCIE EI 2026年第8期1-34,共34页
Large Language Models(LLMs)have been playing a transformative role in natural language understanding and generation,yet adapting LLMs to domain-specific and privacy-sensitive data remains challenging under centralized... Large Language Models(LLMs)have been playing a transformative role in natural language understanding and generation,yet adapting LLMs to domain-specific and privacy-sensitive data remains challenging under centralized training.Federated Learning(FL)provides a promising alternative by enabling training LLMs collaboratively without sharing raw data.However,integrating FL and LLMs introduces new challenges,including model size,device heterogeneity,non-IID data,and alignment requirements.This survey offers a structured overview of the federated LLM ecosystem.We present a comprehensive taxonomy encompassing system architectures,advanced data strategies for addressing heterogeneity,and retrieval-augmented generation in federated contexts.Additionally,we review efficient adaptation methods that enable LLM tuning on resource-constrained clients and analyze data security and privacy concerns.We conclude by summarizing emerging applications in healthcare,industry,software engineering,and finance,and by outlining open problems and research opportunities for scalable,secure,and responsible federated LLM deployment. 展开更多
关键词 Large language models federated learning foundation models federated large language models
暂未订购 下载PDF
Large Language Models for Cybersecurity Intelligence: A Systematic Review of Emerging Threats, Defensive Capabilities, and Security Evaluation Frameworks 认领 引用
2
作者 Hamed Alqahtani Gulshan Kumar 《Computers, Materials & Continua》 SCIE EI 2026年第6期297-367,共71页
Large Language Models(LLMs)are becoming integral components of modern cybersecurity ecosystems,simultaneously strengthening defensive capabilities while giving rise to a new class of Artificial Intelligence-Generated ... Large Language Models(LLMs)are becoming integral components of modern cybersecurity ecosystems,simultaneously strengthening defensive capabilities while giving rise to a new class of Artificial Intelligence-Generated Content(AIGC)-driven threats.This PRISMA-guided systematic review synthesises 167 peer-reviewed studies published between 2022 and 2025 and proposes a unified threat-defence-evaluation taxonomy as a central analytical framework to consolidate a previously fragmented body of research.Guided by this taxonomy,the review first examines AIGC-enabled threats,including automated and highly personalised phishing,polymorphic malware and exploit generation,jailbreak and adversarial prompting,prompt-injection attack vectors,multimodal deception,persona-steering attacks,and large-scale disinformation campaigns.The surveyed evidence indicates a qualitative escalation in adversarial capabilities,with LLMs significantly enhancing scalability,adaptability,and realism while markedly reducing the technical barriers to conducting sophisticated attacks.Second,the review analyses LLM-enabled defensive applications spanning intrusion and anomaly detection,malware analysis and log-semantic modelling,multilingual threat intelligence extraction,vulnerability discovery and code repair,and Security Operations Center(SOC)automation through Retrieval-Augmented Generation(RAG)and multi-agent systems.Although these approaches demonstrate strong potential as semantic reasoning and decision-support components within hybrid security architectures,their real-world effectiveness remains constrained by hallucination risks,adversarial susceptibility,distributional shifts,and operational overhead.Third,the review synthesises current security evaluation and red-teaming practices,revealing a fragmented assessment landscape characterised by narrow benchmarks,inconsistent evaluation metrics,and limited longitudinal robustness analysis.Overall,the taxonomy-driven synthesis highlights a structurally imbalanced ecosystem in which offensive innovation outpaces defensive maturity and governance,and it informs a structured,research-question-aligned roadmap for developing trustworthy,resilient,and policy-aligned LLM-powered cybersecurity systems. 展开更多
关键词 Artificial intelligence-generated content threats cybersecurity intelligence large language model-based defensive systems large language models red-teaming and evaluation frameworks
暂未订购 下载PDF
ZhongdaChat-ED:a medical large language model for personalized erectile dysfunction health consultation and professional clinical decision-making using retrieval-augmented generation 认领 引用
3
作者 Yi Xia Yong-Kun Zhu +6 位作者 Chun-Hui Liu Xiang Hong Rui-Xin Zhang Nie-Ke Zhang Ming Chen Guang-Yuan Zhang Chao Sun 《Asian Journal of Andrology》 SCIE CAS CSCD 2026年第1期71-79,共9页
Artificial intelligence(AI)-driven large language models(LLMs)hold potential for medical applications but face challenges,such as inaccurate or outdated training data.In this study,ZhongdaChat-ED,a personalized medica... Artificial intelligence(AI)-driven large language models(LLMs)hold potential for medical applications but face challenges,such as inaccurate or outdated training data.In this study,ZhongdaChat-ED,a personalized medical LLM integrating retrieval-augmented generation(RAG)technology,was developed to enhance erectile dysfunction(ED)counseling and clinical decision-making.The model was built using the open-source Deepseek-r1:32b framework,augmented with two specialized databases:a patient health consultation database and a clinical decision support database updated with real-time medical advancements.Two versions of ZhongdaChat-ED were developed:a Consumer Version for patient-facing health consultations and a Professional Version for clinician support.Performance was evaluated against four commonly used LLMs(ChatGPT4,Copilot,Claude,and Gemini)through simulated clinical consultations and case analyses.Three urologists and three patients assessed responses across various dimensions,including accuracy,human caring,ease of understanding,clinical significance,and informational frontier.The Consumer Version outperformed commonly used LLMs in accuracy(4.77/5),human caring(4.86/5),and ease of understanding(4.88/5)with all P85.2%case score rate)and informational frontier scores(4.52/5)than those of other models(P<0.001).ZhongdaChat-ED effectively addresses limitations of conventional LLMs by leveraging RAG to integrate real-time,domain-specific data.ZhongdaChat-ED shows promise in enhancing patient health consultation and clinician decision-making for ED,underscoring the value of tailored AI systems in bridging gaps between generalized AI and specialized medical needs.Future work should expand multimodal capabilities and cross-disciplinary integration to broaden clinical utility. 展开更多
关键词 artificial intelligence clinical decision-making erectile dysfunction large language models medical consultation ZhongdaChat-ED
暂未订购 下载PDF
Domain-Specific Large Language Model for Maintenance Decision-Making on Wind Farms by Labeled-Data-Supervised Fine-Tuning 认领 引用
4
作者 Dongming Fan Meng Liu +5 位作者 Yi Shao Linchao Yang Yiliu Liu Yue Zhang Yi Ren Zili Wang 《Engineering》 SCIE EI CSCD 2026年第5期343-361,共19页
Wind farm operators always need a better maintenance strategy to increase resource utilization efficiency while controlling operation and maintenance costs.However,conventional maintenance decision-making approaches a... Wind farm operators always need a better maintenance strategy to increase resource utilization efficiency while controlling operation and maintenance costs.However,conventional maintenance decision-making approaches are time-consuming and have poor flexibility and adaptability to various scenarios.This study addressed these challenges by using a large language model(LLM)to understand,generate,and plan maintenance strategies for wind farms characterized by various failure modes and maintenance costs.A labelled-data-supervised fine-tuning LLM for maintenance,named LLM4M,is proposed.The proposed LLM4M model is trained on an extensive dataset of mathematical programs for maintenance to generate optimal strategies for wind farms.Compared with other large parameter LLMs,the fine-tuned LLM4M model demonstrates remarkable accuracy,with an error of approximately 2%from the optimal strategy.In addition,the generalization of the proposed LLM4M model has achieved remarkable results.If the LLM4M model correctly generates the maintenance strategy,the maintenance cost deviates from the optimal solution by only approximately 5%.Furthermore,phase transition behavior is observed,which provides considerable guidance for the development of domain-specific LLMs for the maintenance domain. 展开更多
关键词 Large language model Maintenance decision-making Wind farms Fine-tuning
暂未订购 下载PDF
CellReasoner:a reasoning-enhanced large language model for cell type annotation 认领 引用
5
作者 Guangshuo Cao Yi Shen +4 位作者 Lingyu Zhao Jianghong Wu Haoyu Chao Ming Chen Dijun Chen 《Journal of Genetics and Genomics》 SCIE CAS CSCD 2026年第4期762-765,共4页
Cell type annotation is a fundamental step in single-cell data analysis,and it also represents a reasoning process that integrates diverse sources of evidence,including gene expression profiles,canonical marker genes,... Cell type annotation is a fundamental step in single-cell data analysis,and it also represents a reasoning process that integrates diverse sources of evidence,including gene expression profiles,canonical marker genes,and reference datasets,to accurately infer cellular identities.Similar to stepwise inference in artificial intelligence,this process relies on combining prior knowledge with context-specific features to achieve confident classification.Recent advances in large language models have shown that sufficiently scaled models can perform sophisticated reasoning across mathematical,logical,and programming tasks(Azerbayev et al.,2023;Jaech et al.,2024;Guo et al.,2025;Ye et al.,2025).This progress highlights the potential for leveraging LLM-based reasoning paradigms to enhance complex biological inference tasks such as automated cell type annotation. 展开更多
关键词 combining prior knowledge gene expression profilescanonical cell type annotation artificial intelligencethis reasoning large language models stepwise inference reasoning process
暂未订购 下载PDF
Multitype Game Optimisation:A Two-Stage Fine-Tuning Framework for Multi-Game Optimisation With Large Language Models 认领 引用
6
作者 Xiali Li Jingshi Gu +3 位作者 Feifan He Yang Xiao Yuanli Jia Ping Lan 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期739-753,共15页
Large language models(LLMs)have made remarkable advances in natural language processing,demonstrating great potential in modelling structured sequences.However,adapting these capabilities to machine gaming tasks such ... Large language models(LLMs)have made remarkable advances in natural language processing,demonstrating great potential in modelling structured sequences.However,adapting these capabilities to machine gaming tasks such as Go remains challenging due to limitations in strategy generalisation and optimisation efficiency.This paper presents multitype game optimisation(MyGO),a two-stage fine-tuning framework tailored for two-player perfect information board games,exploring the applicability of LLMs to nonlinguistic decision-making domains.In the supervised fine-tuning stage,we propose a unified structural encoding method,action semantic unit(ASU),which efficiently converts heterogeneous game records into discrete token sequences compatible with LLMs.In the reinforcement learning stage,we design TA-PPO(token-level adaptive proximal policy optimisation),an enhanced PPO-based algorithm to address the issue of sparse feedback commonly encountered in game reinforcement learning.Experimental results demonstrate that the fine-tuned models achieve superior or comparable performance to traditional game-playing algorithms in terms of strategy quality,rule generalisation and inference efficiency.This work provides a scalable paradigm for fine-tuning LLMs in complex decision-making tasks and lays a foundation for future research in game AI and generalisable strategy optimisation. 展开更多
关键词 computer game fine‐tuning large language models reinforcement learning reward function
暂未订购 下载PDF
Large language models for battery prognostics 认领 引用
7
作者 Jingyuan Zhao Yan Wang +2 位作者 Misheng Cai Lili Xie Hewu Wang 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第4期159-177,I0006,共19页
Predicting battery health with accuracy and interpretability has become a grand challenge at the intersection of electrochemistry,artificial intelligence,and sustainable energy.Conventional data-driven and physics-bas... Predicting battery health with accuracy and interpretability has become a grand challenge at the intersection of electrochemistry,artificial intelligence,and sustainable energy.Conventional data-driven and physics-based methods remain constrained by nonlinear,coupled,and heterogeneous battery dynamics that limit generalization across chemistries,duty cycles,and environments.Recent breakthroughs in large language models(LLMs)and foundation-model artificial intelligence introduce a paradigm shift—enabling machines to learn from multimodal signals,encode physical laws,and reason adaptively across scales.This review unifies these advances into ten foundational methodologies that delineate the emerging landscape of intelligent battery prognostics:transfer learning,knowledge augmentation,physics-informed and explainable intelligence,ensemble fusion,causal reasoning,continual adaptation,multi-agent coordination,digital-twin coupling,and the pursuit of artificial general intelligence.Together,these dimensions redefine batteries from passive electrochemical devices into cognitive energy systems—self-optimizing,trustworthy,and responsive to uncertainty.Framed within the broader evolution toward Industry 5.0,we chart a roadmap for autonomous battery management that fuses physics,data,and reasoning,establishing artificial intelligence as a scientific and technological cornerstone for the next generation of resilient,adaptive,and sustainable electrification. 展开更多
关键词 Battery management system Artificial intelligence Deep learning Large language models Multi-agent Artificial general intelligence Industry 5.0
暂未订购 下载PDF
Towards Generalisable and Explainable Traffic Signal Control via Deep Reinforcement Learning and Large Language Models 认领 引用
8
作者 Hao Huang Wenjie He +6 位作者 Qilie Liu Qian Liu Chao Huang Anwar PPAbdul Majeed Xiangguang Dai Gang Fang Xiaohua Xu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第2期483-497,共15页
As a government-regulated public service,traffic signal control(TSC)requires reliable and transparent decision-making.However,existing deep reinforcement learning(DRL)methods,despite improvements in control accuracy,s... As a government-regulated public service,traffic signal control(TSC)requires reliable and transparent decision-making.However,existing deep reinforcement learning(DRL)methods,despite improvements in control accuracy,still lack explainability and generalisation,severely limiting their applicability in real-world environments.To address the challenges above,this paper proposes GenEx-TSC,a generalisable and explainable TSC method that integrates deep reinforcement learning with large language models(LLMs).First,starting from vehicle-level states,we train a DRL agent incorporating intersection physical heterogeneity and neighbourhood information,which lays the evaluation foundation for constructing a high-quality LLM dataset.Subsequently,the LLM agent is optimised through a two-stage training mechanism.In the distillation stage,a lightweight LLM agent is trained using the reasoning trajectories of a larger-scale LLM agent,inheriting its semantic understanding and decision-generation capabilities and in the alignment stage,the DRL evaluation network is employed to calibrate the outputs of the distilled LLM agent,ensuring that the generated cycle-level signal timing strategies are both efficient and interpretable.We synthesise 10 intersection networks with different physical attributes in SUMO and set traffic flows of varying scales.Experimental results across diverse traffic environments demonstrate that the proposed GenEx-TSC exhibits clear advantages over traditional methods,mainstream DRL methods and LLM baselines in terms of control accuracy,generalisation and explainability. 展开更多
关键词 deep reinforcement learning explainable intelligence large language model traffic signal control
暂未订购 下载PDF
A Metaheuristic Football Optimization Algorithm Integrated with Large Language Models for Automated Seismic Time-Series Modeling 认领 引用
9
作者 Amal H.Alharbi Marwa M.Eid +2 位作者 Nima Khodadadi Ebrahim A.Mattar Sayed Elkenawy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期947-987,共41页
Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Alt... Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Although recent studies have improved neural architectures and optimization techniques,preprocessing is often treated as a fixed or manually designed stage,with limited integration into model optimization.To address this,this paper proposes an integrated,data-driven modelling framework that combines guided preprocessing with systematic hyperparameter optimization for seismic prediction,specifically forecasting earthquake magnitude from seismic catalog time-series data,with experiments conducted on Canadian seismic records.The method uses a Large Language Model to guide data preparation and feature engineering,rather than fully automate them,and applies deep learning-based forecasting with the N-HITS architecture,optimized via metaheuristic-assisted feature selection and hyperparameter tuning.The Football Optimization Algorithm(FbOA),employed as a metaheuristic optimization strategy in this study,is evaluated and compared with several well-known optimizers under identical conditions.The results show significant performance gains,with FbOA achieving superior accuracy,robustness,and convergence compared to baseline and competing methods.Notably,error metrics are reduced(MSE 3.10×10-7,RMSE 5.57×103),with high performance indicators(r=0.982,R2=0.979,NSE=0.981,WI=0.985).These results highlight the value of integrating guided preprocessing with optimization and demonstrate a scalable framework for high-precision time-series prediction in geophysical and related domains. 展开更多
关键词 Seismic time-series forecasting large language models metaheuristic algorithms football optimization algorithm earthquake modeling
暂未订购 下载PDF
From Lexicons to Large Language Models:A Comprehensive Survey of Sentiment Analysis Methods,Benchmarks,and Emerging Frontiers 认领 引用
10
作者 Shuvodeep De Agnivo Gosai +4 位作者 Karun Thankachan Ramadan A.Zein Eldin Abdulaziz T.Almaktoom Mustafa Bayram Ali Wagdy Mohamed 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期119-167,共49页
Sentiment analysis(SA)has evolved from a niche text-classification task into a central problem in natural language processing,spanning multiple domains,modalities,and languages.This survey provides a comprehensive rev... Sentiment analysis(SA)has evolved from a niche text-classification task into a central problem in natural language processing,spanning multiple domains,modalities,and languages.This survey provides a comprehensive review of sentiment analysis methods from their origins in lexicon-based approaches through classical machine learning,deep learning architectures,pre-trained transformers,and the current era of large language models(LLMs).We formalize the SA problem across multiple granularity levels(document,sentence,and aspect)and present a taxonomy that encompasses classification,regression,aspect-based sentiment analysis(ABSA),emotion detection,and stance detection tasks across diverse domains including movie reviews,product reviews,healthcare,finance,and social media.We review benchmark datasets spanning text-only corpora(IMDb,SST,SemEval series),multimodal benchmarks(CMU-MOSI,CMU-MOSEI,MELD),and domain-specific evaluation suites such as SentiEval.The methodological evolution is traced from VADER and SentiWordNet,through SVM and Naïve Bayes classifiers,CNN and LSTM architectures,BERT and its variants,to modern LLMs including GPT-4,Llama 3,and ModernBERT,with technical details of key architectures and their mathematical formulations.We provide dedicated analyses of chain-ofthought reasoning for implicit sentiment,multimodal fusion strategies,cross-lingual transfer methods,sarcasm and irony detection,explainability through SHAP and LIME,and the emerging challenge of AI-generated fake reviews.A comparative analysis across paradigms reveals that while LLMs achieve strong zero-shot performance,fine-tuned smaller models remain competitive on standard benchmarks,a finding with significant implications for deployment efficiency.We identify persistent open challenges including domain drift,cultural bias,and the model variability problem,and outline future research directions encompassing reasoning-augmented SA,agentic workflows,federated learning,and real-time edge deployment.With coverage of over 130 references spanning two decades of research and 29 new references from 2024 and 2025,this survey provides a unified roadmap for both newcomers and researchers at the frontier of sentiment analysis. 展开更多
关键词 Sentiment analysis opinion mining large language models transformers BERT aspect-based sentiment analysis multimodal sentiment analysis cross-lingual NLP explainable AI chain-of-thought reasoning sarcasm detection benchmark datasets
暂未订购 下载PDF
Development of a large language model-driven intelligent agent for predicting relative permeability in oil and gas reservoirs 认领 引用
11
作者 Jiulong Wang Yuanchun Zhou +2 位作者 Xuhui Zhang Junming Lao Hongqing Song 《Theoretical & Applied Mechanics Letters》 EI CAS CSCD 2026年第3期91-98,共8页
To address the challenges and difficulties in predicting the relative permeability of reservoirs using traditional physics-driven and data-driven approaches,this paper proposes a collaborative analysis intelligent age... To address the challenges and difficulties in predicting the relative permeability of reservoirs using traditional physics-driven and data-driven approaches,this paper proposes a collaborative analysis intelligent agent for the relative permeability of oil and gas reservoirs based on a large model.By constructing a multiagent collaborative workflow,integrated collaboration of data,models,and analysis results is achieved.The intelligent agent automatically completes data preprocessing,feature extraction,parameter calibration,small model calling,and output and evaluation of prediction results based on preset task dependencies.At the same time,by introducing deep learning-based embedding of physical information,the analysis efficiency and accuracy are significantly improved.The results show that compared with traditional physical analysis methods,this method improves the accuracy of reservoir relative permeability prediction by 10%,has a computational efficiency 10 times higher than traditional deep learning algorithms,and a computational speed 100–1,000 times higher than conventional physical models.This study further enhances the efficiency and intelligence of physical property analysis of oil and gas reservoirs,providing a new research direction for the intelligent development of oil and gas digitization. 展开更多
关键词 Relative permeability Large language model Intelligent agent
暂未订购 下载PDF
CMM-EmbedCluster:a clustering framework for Chinese materia medica based on large language model and Chinese materia medica property theory 认领 引用
12
作者 Jiayi He Jiadong Xie +1 位作者 Kongfa Hu Haiyan Li 《Digital Chinese Medicine》 CAS CSCD 2026年第2期278-289,共12页
Objective This study proposes a clustering framework for Chinese materia medica(CMM)based on a large language model(LLM),aiming to explore potential compatibility patterns among CMMs from the semantic perspective of C... Objective This study proposes a clustering framework for Chinese materia medica(CMM)based on a large language model(LLM),aiming to explore potential compatibility patterns among CMMs from the semantic perspective of CMM property theory.Methods First,a CMM property knowledge base was constructed based on Chinese Materia Medica,including 567 commonly used CMMs characterized by four properties,five flavors,and meridian tropism.Then,49 CMMs derived from 10 prescriptions for Zangdu(脏毒,pathogenic toxins)recorded in Waike Zhengzong(《外科正宗》,Orthodox Manual of External Medicine)and Yangke Xinde Ji(《疡科心得集》,Collected Insights on Ulcer Medicine)were selected as the experimental dataset.Five semantic representation methods—One-Hot,Word2Vec,Bidirectional Encoder Representations from Transformers(BERT),Beijing Academy of Artificial Intelligence General Embedding(BGE),and Qwen—were applied to encode CMM property information into vector representations.Subsequently,t-distributed Stochastic Neighbor Embedding(t-SNE)was used for nonlinear dimensionality reduction on high-dimensional semantic vectors,followed by k-means clustering(k=7).Clustering performance was evaluated using the Silhouette Score(SS),Davies-Bouldin Index(DBI),and Calinski-Harabasz Index(CHI).Results The Qwen-based clustering method,CMM-EmbedCluster,achieved the highest SS(0.6074)and CHI(158.0572),as well as the lowest DBI(0.4995),indicating improved cluster separation and compactness compared with other methods.Visualization of CMM clustering results showed that the clusters were well separated in the low-dimensional space,with strong inter-cluster discrimination and high intra-cluster functional consistency.Further interpretability analysis of CMM clustering results revealed stable structural differences among clusters in terms of four properties,five flavors,and meridian tropism,forming functional partitions consistent with CMM property theory.Conclusion CMM-EmbedCluster utilizes an LLM to achieve semantic-level representation and clustering of CMMs within the framework of CMM property theory,providing support for exploring potential compatibility patterns among CMMs from the perspective of CMM property semantics. 展开更多
关键词 Large language model Chinese materia medica property theory Chinese materia medica clustering Semantic representation k-Means clustering CMM-EmbedCluster
暂未订购 下载PDF
Large Language Model-Enabled Constitutive Modeling for Rate-Dependent Plasticity and Automatic UMAT Subroutine Generation 认领 引用
13
作者 Yuchuan Gu Lusheng Wang +3 位作者 Jun Ding Yanhong Peng Changgeng Li Shaojie Gu 《Computers, Materials & Continua》 SCIE EI 2026年第5期315-329,共15页
In materials science and engineering design,high-fidelity and high-efficiency numerical simulation has become a driving force for innovation and practical implementation.To address longstanding bottlenecks in the deve... In materials science and engineering design,high-fidelity and high-efficiency numerical simulation has become a driving force for innovation and practical implementation.To address longstanding bottlenecks in the development of conventional material constitutive models—such as lengthy modeling cycles and difficulties in numerical implementation—this study proposes an intelligent modeling and code generation approach powered by large languagemodels.A structured knowledge base integrating constitutive theory,numerical algorithms,and UMAT(User Material)interface specifications is constructed,and a retrieval-augmented generation strategy is employed to establish an end-to-end workflow spanning experimental data parsing,constitutive model formulation,and automatic UMAT subroutine generation.Experimental results show that the method achieves high accuracy for both a classical Johnson–Cookmodel and a physics-informed neural network(PINN)model,with key parameter identification errors below 5%.Moreover,the automatically generated UMAT subroutines yield finite element simulation results in Abaqus that are highly consistent with theoretical predictions(coefficient of determination R2>0.98)while maintaining good numerical stability.This framework is currently focused on the automatic construction of rate-dependent elastoplastic material models,and its core method also provides a clear path for extending to other constitutive categories such as hyperelasticity and viscoelasticity.This work provides an effective technical route for the rapid development and reliable numerical implementation of material constitutive models,significantly advancing the intelligence level of computational mechanics research and improving engineering application efficiency. 展开更多
关键词 Large language model constitutive model UMAT subroutine
暂未订购 下载PDF
Bilingual evaluation of large language models for patient education in refractive surgery 认领 引用
14
作者 Tsung-Hsien Tsai Chin-Ling Tsai +2 位作者 Jui-Hung Hsu Ching-Hsi Hsiao Hung-Chi Chen 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2026年第6期1019-1027,共9页
AIM:To evaluate the ability of six advanced large language models(LLMs)—in providing accurate,comprehensive,and readable patient education on corneal refractive surgeries[laser in-situ keratomileusis(LASIK),keratoref... AIM:To evaluate the ability of six advanced large language models(LLMs)—in providing accurate,comprehensive,and readable patient education on corneal refractive surgeries[laser in-situ keratomileusis(LASIK),keratorefractive lenticule extraction(KLEx),and photorefractive keratectomy(PRK)]in both English and Chinese.METHODS:This is a cross-sectional,comparative study.Twenty-six questions,compiled from authoritative ophthalmologic sources and covering four domains(procedure basics and eligibility;safety,risks and longterm stability;recovery and postoperative experience;and practical concerns),were administered in both English and Chinese via fresh chat sessions with each LLM,respectively.Five performance metrics were evaluated:accuracy,comprehensiveness,word count,readability,and reproducibility,using appropriate statistical tests.RESULTS:OpenAI o1 and DeepSeek-R1 consistently achieved the highest accuracy and most comprehensive responses,significantly outperforming ChatGPT-4o,Gemini Advanced,Claude Sonnet,and Tongyi Qwen(Friedman P<0.001).Although overall accuracy and comprehensiveness were similar across languages,Chinese responses were significantly longer.Readability varied among the models,with Claude Sonnet generally producing the most readable English texts.Reproducibility analysis revealed moderate consistency,reflecting inherent variability in outputs to identical prompts.CONCLUSION:Reasoning-augmented LLMs,particularly OpenAI o1 and DeepSeek-R1,demonstrate superior performance in delivering bilingual patient education for corneal refractive surgery,with high accuracy and comprehensiveness.However,variations in response length,readability,and reproducibility indicate that further refinement is necessary before these tools can be reliably integrated into clinical practice. 展开更多
关键词 large language models refractive surgery patient education
暂未订购 下载PDF
A Grounded Multi-Agent Multimodal Large Language Model Framework for Interpretable Risk Assessment in Driving Scenes 认领 引用
15
作者 Chien-Hao Tseng Min-Yu Chen +2 位作者 Meng-Wei Lin Jyh-Horng Wu Chung-I Huang 《Computers, Materials & Continua》 SCIE EI 2026年第9期1137-1163,共27页
Context-aware driving assistance must do more than detect objects:it has to identify the cues that materially affect risk,separate observable evidence from inference,and produce recommendations that humans can audit.T... Context-aware driving assistance must do more than detect objects:it has to identify the cues that materially affect risk,separate observable evidence from inference,and produce recommendations that humans can audit.This paper presents a grounded multi-agent multimodal large language model(MLLM)framework for interpretable risk assessment in driving scenes.The framework decomposes reasoning into four stages—context relevance evaluation,visual interpretation,factual verification with anomaly extraction,and risk assessment with action recommendation—so that the final advisory is generated only from a verified intermediate representation rather than directly from a free-form scene description.We evaluate the framework on a manually labeled benchmark derived from BDD100K covering traffic-sign interpretation,traffic-density assessment,and pedestrian–vehicle interaction risk.The benchmark contains 600 frames with three-rater annotation and majority-vote labels(Fleiss’κ=0.79 on risk levels);we explicitly discuss the implications of this scale for generalization and complement it with a multi-backbone stress test.Across five independent runs,the proposed framework improves risk accuracy from 74.3±0.9%to 84.8±0.6%and macro-F1 from 72.8±1.1%to 83.1±0.7%over a single-agent MLLM baseline.The hallucination rate—defined as the fraction of outputs containing at least one entity,attribute,or relation that has no visual support in the source frame—drops from 18.7%to 8.9%,and the actionability score—a five-point human rating averaged over usefulness,specificity,and visual consistency—rises from 3.62 to 4.28.McNemar tests confirm that the gain in risk accuracy is statistically significant(p<0.001).The framework is intended as a semantic decision-support layer for explainable advanced driver-assistance systems and human-centered autonomous-driving interfaces. 展开更多
关键词 Autonomous driving context-aware risk assessment multimodal large language model multi-agent system interpretable reasoning driving-scene understanding
暂未订购 下载PDF
Decision-making performance of large language models vs.human physicians in challenging lung cancer cases:A real-world case-based study 认领 引用
16
作者 Ning Yang Kailai Li +19 位作者 Baiyang Liu Xiting Chen Aimin Jiang Chang Qi Wenyi Gan Lingxuan Zhu Weiming Mou Dongqiang Zeng Mingjia Xiao Guangdi Chu Shengkun Peng Hank ZHWong Lin Zhang Hengguo Zhang Xinpei Deng Quan Cheng Bufu Tang Anqi Lin Juan Zhou Peng Luo 《Intelligent Oncology》 CAS 2026年第1期15-24,共10页
Background:Despite the promise shown by large language models(LLMs)for standardized tasks,their multidimensional performance in real-world oncology decision-making remains unevaluated.This study aims to introduce a fr... Background:Despite the promise shown by large language models(LLMs)for standardized tasks,their multidimensional performance in real-world oncology decision-making remains unevaluated.This study aims to introduce a framework for evaluating LLMs and physician decisions in challenging lung cancer cases.Methods:We curated 50 challenging lung cancer cases(25 local and 25 published)classified as complex,rare,or refractory.Blinded three-dimensional,five-point Likert evaluations(1–5 for comprehensiveness,specificity,and readability)compared standalone LLMs(DeepSeek R1,Claude 3.5,Gemini 1.5,and GPT-4o),physicians by experience level(junior,intermediate,and senior),and AI-assisted juniors;intergroup differences and augmentation effects were analyzed statistically.Results:Of 50 challenging cases(18 complex,17 rare,and 15 refractory)rated by three experts,DeepSeek R1 achieved scores of 3.95±0.33,3.71±0.53,and 4.26±0.18 for comprehensiveness,specificity,and readability,respectively,positioning it between intermediate(3.68,3.68,3.75)and senior(4.50,4.64,4.53)physicians.GPT-4o and Claude 3.5 reached intermediate physician–level comprehensiveness(3.76±0.39,3.60±0.39)but junior-to-intermediate physician–level specificity(3.39±0.39,3.39±0.49).All LLMs scored higher on rare cases than intermediate physicians but fell below junior physicians in refractory-case specificity.AIassisted junior physicians showed marked gains in rare cases,with comprehensiveness rising from 2.32 to 4.29(84.8%),specificity from 2.24 to 4.26(90.8%),and readability from 2.76 to 4.59(66.0%),while specificity declined by 3.2%(3.17 to 3.07)in refractory cases.Error analysis showed complementary strengths,with physicians demonstrating reasoning stability and LLMs excelling in knowledge updating and risk management.Conclusions:LLMs performed variably in clinical decision-making tasks depending on case type,performing better in rare cases and worse in refractory cases requiring longitudinal reasoning.Complementary strengths between LLMs and physicians support case-and task-tailored human–AI collaboration. 展开更多
关键词 Large language models Clinical evaluation Decision-making Lung cancer
暂未订购 下载PDF
Knowledge-Enhanced Industrial Question-Answering Using Large Language Models 认领 引用 被引量：2
17
作者 Ronghui Liu Hao Ren +5 位作者 Haojie Ren Wu Rui Wei Cui Xiaojun Liang Chunhua Yang Weihua Gui 《Engineering》 SCIE EI CSCD 2026年第5期142-153,共12页
Modern industrial systems have grown increasingly extensive,complex,and hierarchical,with operations relying on numerous knowledge-based queries.These queries necessitate considerable human resources while also requir... Modern industrial systems have grown increasingly extensive,complex,and hierarchical,with operations relying on numerous knowledge-based queries.These queries necessitate considerable human resources while also requiring high levels of accuracy,subjectivity,and consistency,all of which critically influence operational efficiency.To overcome these challenges,this study proposes an industrial retrieval-augmented generation(RAG)method designed to enhance large language models(LLMs)using domain-specific knowledge,thereby improving the precision of question answering.A comprehensive industrial knowledge base was constructed from diverse sources,including journal articles,theses,books,and patents.A Text classification model based on bidirectional encoder representations from transformers(BERTs)was trained to accurately classify incoming queries.Furthermore,the general text embedding-dense passage retrieval(GTE-DPR)model was employed to perform word embedding and vector similarity retrieval,facilitating the alignment of query vectors with relevant entries in the knowledge base to obtain initial responses.LLMs subsequently refined these initial results to produce accurate final answers.Experimental evaluations confirm the effectiveness of the proposed approach.In particular,when applied to ChatGLM2-6B,the RAG method increased the ROUGE-L score from 32.52%to 55.04%and improved accuracy from 50.52%to 73.92%.Comparable improvements were also observed with LLaMA2-7B,underscoring the RAG framework's capability to significantly enhance the accuracy and relevance of industrial question-answering(QA)systems. 展开更多
关键词 Retrieval augmented generation Knowledge enhancement Question answering Large language models Industrial knowledge automation
暂未订购 下载PDF
Command-agent:Reconstructing warfare simulation and command decision-making using large language models 认领 引用 被引量：1
18
作者 Mengwei Zhang Minchi Kuang +3 位作者 Heng Shi Jihong Zhu Jingyu Zhu Xiao Jiang 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第2期294-313,共20页
War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient an... War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient and inflexible,with particularly pronounced limitations in command and decision-making.The overwhelming volume of information and high decision complexity hinder the realization of autonomous and agile command and control.To address this challenge,an intelligent warfare simulation framework named Command-Agent is proposed,which deeply integrates large language models(LLMs)with digital twin battlefields.By constructing a highly realistic battlefield environment through real-time simulation and multi-source data fusion,the natural language interaction capabilities of LLMs are leveraged to lower the command threshold and to enable autonomous command through the Observe-Orient-Decide-Act(OODA)feedback loop.Within the Command-Agent framework,a multimodel collaborative architecture is further adopted to decouple the decision-generation and command-execution functions of LLMs.By combining specialized models such as Deep Seek-R1 and MCTool,the limitations of single-model capabilities are overcome.MCTool is a lightweight execution model fine-tuned for military Function Calling tasks.The framework also introduces a Vector Knowledge Base to mitigate hallucinations commonly exhibited by LLMs.Experimental results demonstrate that Command-Agent not only enables natural language-driven simulation and control but also deeply understands commander intent.Leveraging the multi-model collaborative architecture,during red-blue UAV confrontations involving 2 to 8 UAVs,the integrated score is improved by an average of 41.8%compared to the single-agent system(MCTool),accompanied by a 161.8%optimization in the battle loss ratio.Furthermore,when compared with multi-agent systems lacking the knowledge base,the inclusion of the Vector Knowledge Base further improves overall performance by 16.8%.In comparison with the general model(Qwen2.5-7B),the fine-tuned MCTool leads by 5%in execution efficiency.Therefore,the proposed Command-Agent introduces a novel perspective to the military command system and offers a feasible solution for intelligent battlefield decision-making. 展开更多
关键词 Digital twin battlefield Large language models Multi-agent system Military command
暂未订购 下载PDF
Agri-Eval:Multi-level Large Language Model Valuation Benchmark for Agriculture 认领 引用 被引量：1
19
作者 WANG Yaojun GE Mingliang +2 位作者 XU Guowei ZHANG Qiyu BIE Yuhui 《农业机械学报》 EI CAS CSCD 北大核心 2026年第1期290-299,共10页
Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLM... Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture. 展开更多
关键词 large language models assessment systems agricultural knowledge agricultural datasets
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 38 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈