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Empowering intelligent quality control with large models:A comprehensive survey of methods,challenges,and perspectives 认领 引用 被引量：1
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作者 Xinyu PAN Di WANG Fugee TSUNG 《ENGINEERING Management》 CSCD 2026年第1期42-64,共23页
Quality control(QC)serves as a cornerstone of modern manufacturing,exerting a decisive influence on production efficiency,product reliability and customer satisfaction.However,traditional QC systems,which largely rely... Quality control(QC)serves as a cornerstone of modern manufacturing,exerting a decisive influence on production efficiency,product reliability and customer satisfaction.However,traditional QC systems,which largely rely on rule‑based frameworks and narrowly defined statistical methods,face increasing limitations in handling the scale,diversity and complexity of contemporary industrial data.This limitation provides a strong motivation to explore the potential of large models(LMs)for advancing QC.Distinguished by their powerful capabilities in knowledge integration,contextual understanding and adaptive reasoning,LMs offer transformative opportunities to modernize QC.This review begins by analyzing why LMs are particularly well positioned to enhance QC,focusing on three crucial dimensions:input alignment,which enables seamless integration of heterogeneous data sources;task adaptability,which supports associative learning across multiple QC tasks and allows knowledge transfer;and augmented intelligence,which supports human experts in complex decision‑making.Recent advances in industrial applications are summarized,with particular attention to methodological innovations,deployment practices and integration pathways into manufacturing workflows.To systematically structure the current landscape,the key challenges are categorized into three inter‑related dimensions,i.e.,data,model and evaluation,which correspond to the core requirements for model training,practical implementation and sustainable adaptability in real‑world scenarios.Building on this foundation,the review further outlines future research directions,highlighting secure data collaboration,system‑level integration and continual learning under dynamic environments as critical priorities for the next stage of development.Collectively,these insights underscore the promise of LMs in reshaping QC into an intelligent,resilient and future‑ready paradigm. 展开更多
关键词 quality control large models foundation models industrial artificial intelligence survey manufacturing intelligence
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Artificial intelligence for energy materials research:From classical machine learning to large models 认领 引用
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作者 Mingxi Jiang Jie Zhou +2 位作者 Yanggang An Zhengran Lin Menghao Yang 《ENGINEERING Energy》 SCIE EI CAS CSCD 2026年第1期25-43,共19页
With the global energy system transitioning to renewable energy,high-efficiency energy storage and conversion technologies have become crucial.However,traditional research paradigms for the research and development(R&... With the global energy system transitioning to renewable energy,high-efficiency energy storage and conversion technologies have become crucial.However,traditional research paradigms for the research and development(R&D)of energy materials such as batteries and electrocatalysts present the limitations in efficiency.This review systematically summarizes the progress of artificial intelligent(AI)in this field,ranging from classical machine learning(ML)to advanced representation methods such as graph neural networks(GNNs)and transformers that enable precise property prediction and structure generation.It also covers generative models for inverse design and large language models(LLMs)for knowledge extraction,along with key domain databases.Current challenges include limited interpretability and the underutilization of emerging AI technologies.Finally,this review discusses future directions such as the applications of multimodal language models,aiming to provide insights for accelerating high-performance energy materials innovation and advancing the global renewable energy transition. 展开更多
关键词 Energy materials Artificial intelligence(AI) Machine learning(ML) Generative models Large language models(LLMs)
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Global open source and international standards promote the inclusive development of large models 认领 引用
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作者 Lin Yonghua 《China Standardization》 2025年第5期25-25,共1页
In the era of AI,especially large models,the importance of open source has become increasingly prominent.First,open source allows innovation to avoid starting from scratch.Through iterative innovation,it promotes tech... In the era of AI,especially large models,the importance of open source has become increasingly prominent.First,open source allows innovation to avoid starting from scratch.Through iterative innovation,it promotes technical exchanges and learning globally.Second,resources required for large model R&D are difficult for a single institution to obtain.The evaluation of general large models also requires the participation of experts from various industries.Third,without open source collaboration,it is difficult to form a unified upper-layer software ecosystem.Therefore,open source has become an important cooperation mechanism to promote the development of AI and large models.There are two cases to illustrate how open source and international standards interact with each other. 展开更多
关键词 open source large model international standards inclusive development iterative innovationit large modelsthe evaluation general large models large models
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Harnessing computational power for intelligent oncology in the age of large models: Status, challenges, and prospects 认领 引用
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作者 Kexin Xu Yueran Xu Qing Shi 《Intelligent Oncology》 CAS 2026年第1期51-63,共13页
The integration of large-scale foundation models(e.g.,GPT series and AlphaFold)into oncology is fundamentally transforming both research methodologies and clinical practices,driven by unprecedented advancements in com... The integration of large-scale foundation models(e.g.,GPT series and AlphaFold)into oncology is fundamentally transforming both research methodologies and clinical practices,driven by unprecedented advancements in computational power.This review synthesizes recent progress in the application of large language models to core oncological tasks,including medical imaging analysis,genomic interpretation,and personalized treatment planning.Underpinned by advanced computational infrastructures,such as graphics processing unitensor processing unit clusters,heterogeneous computing,and cloud platforms,these models enable superior representation learning and generalization across multimodal data sources.This review examines how these infrastructures overcome key bottlenecks in intelligent oncology through scalable optimization strategies,including mixed-precision training,memory optimization,and heterogeneous computing.Alongside these technical advancements,the review explores pressing challenges,such as data heterogeneity,limited model interpretability,regulatory uncertainties,and the environmental impact of artificial intelligence(AI)systems.Special emphasis is placed on emerging solutions,encompassing green AI and edge computing,which offer promising approaches for low-resource deployment scenarios.Additionally,the review highlights the critical role of interdisciplinary collaboration among oncology,computer science,ethics,and policy to ensure that AI systems are not only powerful but also transparent,safe,and clinically relevant.Finally,the review outlines potential avenues for future research aimed at developing robust,scalable,and human-centered frameworks for intelligent oncology. 展开更多
关键词 Large language models Intelligent oncology Medical AI Computational infrastructure High-performance computing
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Next Phase of Large Models 认领 引用
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作者 WANG ZHE 《China Today》 2026年第8期56-59,共4页
ON June 22 of this year,the artificial intelligence developer Zhipu AI crossed the HK$1 trillion market valuation mark,becoming China’s first large language model company to reach this threshold.Its share price has s... ON June 22 of this year,the artificial intelligence developer Zhipu AI crossed the HK$1 trillion market valuation mark,becoming China’s first large language model company to reach this threshold.Its share price has surged over 2,000 percent since its listing in January this year. 展开更多
关键词 large language model company share price market valuation large language models listing artificial intelligence
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Research on the Reconstruction of Data Structure Experimental Teaching Evaluation System and AI Standard Usage Mechanism under the Background of Large Models 认领 引用
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作者 Yueyue Liu Xueqing Lu +1 位作者 Qingqing Liu Kai Xu 《Journal of Contemporary Educational Research》 2026年第6期88-93,共6页
Aiming at the problems in the experimental teaching of data structure such as the replacement of students’learning process,the weakening of algorithm thinking,and the distortion of teaching evaluation results caused ... Aiming at the problems in the experimental teaching of data structure such as the replacement of students’learning process,the weakening of algorithm thinking,and the distortion of teaching evaluation results caused by the overuse of large models,this paper analyzes the influence mechanism of large models on students’ability after their intervention in experimental teaching.On the basis of defining the reasonable usage boundary of large models,a new procedural teaching evaluation system is constructed,and an implementation plan for data structure experimental teaching under the constraint of standardized AI usage is proposed.Teaching practice results show that this evaluation model can effectively standardize students’use of AI and has a significant promoting effect on improving students’algorithm understanding ability,program debugging ability,and engineering practical ability. 展开更多
关键词 Large model Data structure Experimental teaching Procedural teaching evaluation
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Envisioning the blueprint:Aeronautics in large models era 认领 引用 被引量：1
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作者 Weiwei ZHANG Shule ZHAO 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第8期139-141,共3页
Following the groundbreaking introduction of the Transformer architecture in 2017,the development of Large Language Models(LLMs)formally commenced.In May 2020,Chat GPT-3,with over one hundred billion parameters,entere... Following the groundbreaking introduction of the Transformer architecture in 2017,the development of Large Language Models(LLMs)formally commenced.In May 2020,Chat GPT-3,with over one hundred billion parameters,entered the public eye,marking a significant milestone in LLM advancement. 展开更多
关键词 aeronautics large languagemodels transformer architecture transformerarchitecture llms chatgpt large language models llms formally
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A new human-computer interaction paradigm: Agent interaction model based on large models and its prospects 认领 引用
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作者 Yang LIU 《虚拟现实与智能硬件（中英文）》 EI CSCD 2025年第3期237-266,共30页
This study examines the advent of agent interaction(AIx)as a transformative paradigm in humancomputer interaction(HCI),signifying a notable evolution beyond traditional graphical interfaces and touchscreen interaction... This study examines the advent of agent interaction(AIx)as a transformative paradigm in humancomputer interaction(HCI),signifying a notable evolution beyond traditional graphical interfaces and touchscreen interactions.Within the context of large models,AIx is characterized by its innovative interaction patterns and a plethora of application scenarios that hold great potential.The paper highlights the pivotal role of AIx in shaping the future landscape of the large model industry,emphasizing its adoption and necessity from a user's perspective.This study underscores the pivotal role of AIx in dictating the future trajectory of a large model industry by emphasizing the importance of its adoption and necessity from a user-centric perspective.The fundamental drivers of AIx include the introduction of novel capabilities,replication of capabilities(both anthropomorphic and superhuman),migration of capabilities,aggregation of intelligence,and multiplication of capabilities.These elements are essential for propelling innovation,expanding the frontiers of capability,and realizing the exponential superposition of capabilities,thereby mitigating labor redundancy and addressing a spectrum of human needs.Furthermore,this study provides an in-depth analysis of the structural components and operational mechanisms of agents supported by large models.Such advancements significantly enhance the capacity of agents to tackle complex problems and provide intelligent services,thereby facilitating a more intuitive,adaptive,and personalized engagement between humans and machines.The study further delineates four principal categories of interaction patterns that encompass eight distinct modalities of interaction,corresponding to twenty-one specific scenarios,including applications in smart home systems,health assistance,and elderly care.This emphasizes the significance of this new paradigm in advancing HCI,fostering technological advancements,and redefining user experiences.However,it also acknowledges the challenges and ethical considerations that accompany this paradigm shift,recognizing the need for a balanced approach to harness the full potential of AIx in modern society. 展开更多
关键词 Interaction paradigm Agent interaction Large models
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Dataset Copyright Auditing for Large Models:Fundamentals,Open Problems,and Future Directions 认领 引用
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作者 DU Linkang SU Zhou YU Xinyi 《ZTE Communications》 2025年第3期38-47,共10页
The unprecedented scale of large models,such as large language models(LLMs)and text-to-image diffusion models,has raised critical concerns about the unauthorized use of copyrighted data during model training.These con... The unprecedented scale of large models,such as large language models(LLMs)and text-to-image diffusion models,has raised critical concerns about the unauthorized use of copyrighted data during model training.These concerns have spurred a growing demand for dataset copyright auditing techniques,which aim to detect and verify potential infringements in the training data of commercial AI systems.This paper presents a survey of existing auditing solutions,categorizing them across key dimensions:data modality,model training stage,data overlap scenarios,and model access levels.We highlight major trends,including the prevalence of black-box auditing methods and the emphasis on fine-tuning rather than pre-training.Through an in-depth analysis of 12 representative works,we extract four key observations that reveal the limitations of current methods.Furthermore,we identify three open challenges and propose future directions for robust,multimodal,and scalable auditing solutions.Our findings underscore the urgent need to establish standardized benchmarks and develop auditing frameworks that are resilient to low watermark densities and applicable in diverse deployment settings. 展开更多
关键词 dataset copyright auditing large language models diffusion models multimodal auditing membership inference
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Special Topic on Security of Large Models 认领 引用
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作者 SU Zhou DU Linkang 《ZTE Communications》 2025年第3期1-2,共2页
Large models,such as large language models(LLMs),vision-language models(VLMs),and multimodal agents,have become key elements in artificial intelli⁃gence(AI)systems.Their rapid development has greatly improved percepti... Large models,such as large language models(LLMs),vision-language models(VLMs),and multimodal agents,have become key elements in artificial intelli⁃gence(AI)systems.Their rapid development has greatly improved perception,generation,and decision-making in various fields.However,their vast scale and complexity bring about new security challenges.Issues such as backdoor vulnerabilities during training,jailbreaking in multimodal rea⁃soning,and data provenance and copyright auditing have made security a critical focus for both academia and industry. 展开更多
关键词 large modelssuch security multimodal agentshave multimodal rea soningand large language models llms vision language data provenance copyright auditing backdoor vulnerabilities vision language models
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The Synergy of Seeing and Saying: Revolutionary Advances in Multi-modality Medical Vision-Language Large Models 认领 引用 被引量：1
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作者 Xiang LI Yu SUN +3 位作者 Jia LIN Like LI Ting FENG Shen YIN 《Artificial Intelligence Science and Engineering》 2025年第2期79-97,共19页
The application of visual-language large models in the field of medical health has gradually become a research focus.The models combine the capability for image understanding and natural language processing,and can si... The application of visual-language large models in the field of medical health has gradually become a research focus.The models combine the capability for image understanding and natural language processing,and can simultaneously process multi-modality data such as medical images and medical reports.These models can not only recognize images,but also understand the semantic relationship between images and texts,effectively realize the integration of medical information,and provide strong support for clinical decision-making and disease diagnosis.The visual-language large model has good performance for specific medical tasks,and also shows strong potential and high intelligence in the general task models.This paper provides a comprehensive review of the visual-language large model in the field of medical health.Specifically,this paper first introduces the basic theoretical basis and technical principles.Then,this paper introduces the specific application scenarios in the field of medical health,including modality fusion,semi-supervised learning,weakly supervised learning,unsupervised learning,cross-domain model and general models.Finally,the challenges including insufficient data,interpretability,and practical deployment are discussed.According to the existing challenges,four potential future development directions are given. 展开更多
关键词 large language models vision-language models medical health multimodality models
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Neuromorphic Computing in the Era of Large Models 认领 引用 被引量：1
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作者 Haoxuan SHAN Chiyue WEI +4 位作者 Nicolas RAMOS Xiaoxuan YANG Cong GUO HaiLI Yiran CHEN 《Artificial Intelligence Science and Engineering》 2025年第1期17-30,共14页
The rapid advancement of deep learning and the emergence of largescale neural models,such as bidirectional encoder representations from transformers(BERT),generative pre-trained transformer(GPT),and large language mod... The rapid advancement of deep learning and the emergence of largescale neural models,such as bidirectional encoder representations from transformers(BERT),generative pre-trained transformer(GPT),and large language model Meta AI(LLaMa),have brought significant computational and energy challenges.Neuromorphic computing presents a biologically inspired approach to addressing these issues,leveraging event-driven processing and in-memory computation for enhanced energy efficiency.This survey explores the intersection of neuromorphic computing and large-scale deep learning models,focusing on neuromorphic models,learning methods,and hardware.We highlight transferable techniques from deep learning to neuromorphic computing and examine the memoryrelated scalability limitations of current neuromorphic systems.Furthermore,we identify potential directions to enable neuromorphic systems to meet the growing demands of modern AI workloads. 展开更多
关键词 neuromorphic computing spiking neural networks large deep learning models
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Research status and application of artificial intelligence large models in the oil and gas industry 认领 引用 被引量：8
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作者 LIU He REN Yili +6 位作者 LI Xin DENG Yue WANG Yongtao CAO Qianwen DU Jinyang LIN Zhiwei WANG Wenjie 《Petroleum Exploration and Development》 SCIE 2024年第4期1049-1065,共17页
This article elucidates the concept of large model technology,summarizes the research status of large model technology both domestically and internationally,provides an overview of the application status of large mode... This article elucidates the concept of large model technology,summarizes the research status of large model technology both domestically and internationally,provides an overview of the application status of large models in vertical industries,outlines the challenges and issues confronted in applying large models in the oil and gas sector,and offers prospects for the application of large models in the oil and gas industry.The existing large models can be briefly divided into three categories:large language models,visual large models,and multimodal large models.The application of large models in the oil and gas industry is still in its infancy.Based on open-source large language models,some oil and gas enterprises have released large language model products using methods like fine-tuning and retrieval augmented generation.Scholars have attempted to develop scenario-specific models for oil and gas operations by using visual/multimodal foundation models.A few researchers have constructed pre-trained foundation models for seismic data processing and interpretation,as well as core analysis.The application of large models in the oil and gas industry faces challenges such as current data quantity and quality being difficult to support the training of large models,high research and development costs,and poor algorithm autonomy and control.The application of large models should be guided by the needs of oil and gas business,taking the application of large models as an opportunity to improve data lifecycle management,enhance data governance capabilities,promote the construction of computing power,strengthen the construction of“artificial intelligence+energy”composite teams,and boost the autonomy and control of large model technology. 展开更多
关键词 foundation model large language mode visual large model multimodal large model large model of oil and gas industry pre-training fine-tuning
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Implementation of Digital Classroom State System for Teachers and Students Based on Large Models 认领 引用
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作者 Wenbo Lyu Guangmin Zhu +2 位作者 Ziyi Qin Mengting Yan Jie Zhang 《Journal of Contemporary Educational Research》 2024年第11期101-106,共6页
Deep learning has become a hot field of artificial intelligence,and the deep learning large model framework has become a bridgehead for the active layout of Chinese and foreign technology companies.Large models play a... Deep learning has become a hot field of artificial intelligence,and the deep learning large model framework has become a bridgehead for the active layout of Chinese and foreign technology companies.Large models play a significant role in the application field,greatly improving the efficiency of training and optimization,and contributing to the landing of many innovative artificial intelligence tools.Based on the Chinese PaddlePaddle large model framework,an application system is designed in combination with the intelligent classroom teaching scenario,which uses machine vision algorithms to distinguish and present teachers’and students’behaviors,that is,the digitization and multi-classification scheme of class character states.After having digital data,data analysis can be carried out to evaluate the class status of teachers and students,and the traditional subjective judgment such as peacetime grades and teaching ability can be upgraded to the objective judgment of artificial intelligence. 展开更多
关键词 Large model Machine vision Digitalization Status Technical realization
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New Opportunities For the Integration of Artificial Intelligence With Materials Science:From Large Language Models to Embodied Large Models 认领 引用
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作者 Zhen Song Yishuai Cai +4 位作者 Yadong Yu Pan Xiang Minglong Li Zhe Liu Mengyan Dai 《Materials Genome Engineering Advances》 CAS CSCD 2026年第1期1-33,共33页
The rapid advancement of large model technology in recent years has ushered in new opportunities for materials science.Leveraging their powerful feature learning capabilities,emergent properties,and flexible fine-tuni... The rapid advancement of large model technology in recent years has ushered in new opportunities for materials science.Leveraging their powerful feature learning capabilities,emergent properties,and flexible fine-tuning mechanisms,large models are gradually being applied to all aspects of materials science research.However,we contend that the deep integration of artificial intelligence and materials science urgently requires a transition from“virtual intelligence assistance”to“embodied intelligence dominance”.Future materials discovery will be autonomously executed in closed-loop operations within physical environments by Embodied AI Chemists.This review first introduces the blossom of large model technology and its multifaceted applications in various materials science-related topics.Subsequently,this review specifically maps the developmental pathways of autonomous experimental platforms,underscoring the comparative advantages of large-model-based systems over traditional machine learning approaches.Furthermore,it discusses the potential of embodied large models in materials science and proposes potential applications by constructing diverse training datasets,enhancing embodied reasoning capabilities,establishing intelligent collaborative environments,and developing multi-agent collaborative frameworks.Through this comprehensive analysis,we aim to pioneer new intelligent pathways for materials science research,motivating an end-to-end intelligent transformation from theoretical exploration to experimental realization. 展开更多
关键词 autonomous experimentation platforms embodied large models large language models materials science
From Lexicons to Large Language Models:A Comprehensive Survey of Sentiment Analysis Methods,Benchmarks,and Emerging Frontiers 认领 引用 被引量：1
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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
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Multitype Game Optimisation:A Two-Stage Fine-Tuning Framework for Multi-Game Optimisation With Large Language Models 认领 引用
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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
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Towards Generalisable and Explainable Traffic Signal Control via Deep Reinforcement Learning and Large Language Models 认领 引用
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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
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Large language models for battery prognostics 认领 引用
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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
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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
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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
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