The Milestone Year of New AI: Review of Hotspots in Artificial Intelligence and Intelligent Technology
SHEN Tianyu1, ZHANG Hui2, YE Peijun3, WANG Fei-Yue3
1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029; 2. School of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044; 3. The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
Abstract:In 2024, a milestone year for new artificial intelligence(AI), significant breakthroughs were achieved in the field of artificial intelligence. This paper reviews the hotspots and trends in artificial intelligence from various aspects, including large models and embodied intelligence, artificial intelligence generated content(AIGC), AI agents, AI for Science(AI4S) and Science for AI(S4AI), as well as AI-related policies and platforms. With the rapid advancement of large models and AI agent technologies, the application of AI continues to expand, leading to new impacts across various industries. The construction of policies and platforms is continuously improving, and new quality productive forces are showing greater development potential. For autonomous intelligence, the "new AI", the emergence of more milestone works are being expected.
沈甜雨, 张慧, 叶佩军, 王飞跃. 新AI的里程碑之年:人工智能与智能科技热点回眸[J]. 模式识别与人工智能, 2025, 38(6): 485-504.
SHEN Tianyu, ZHANG Hui, YE Peijun, WANG Fei-Yue. The Milestone Year of New AI: Review of Hotspots in Artificial Intelligence and Intelligent Technology. Pattern Recognition and Artificial Intelligence, 2025, 38(6): 485-504.
[1] DAO T, GU A.Transformers Are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality. Proceedings of the Machine Learning Research, 2024, 235: 10041-10071. [2] 黄峻,林飞,杨静,等.生成式AI的大模型提示工程:方法、现状与展望.智能科学与技术学报, 2024, 6(2): 115-133. (HUANG J, LIN F, YANG J, et al. From Prompt Engineering to Generative Artificial Intelligence for Large Models: The State of the Art and Perspective. Chinese Journal of Intelligent Science and Technology, 2024, 6(2): 115-133.) [3] HU C, GE Y, MA X N, et al. RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners // Proc of the Joint International Conference on Computational Linguistics, Language Resources and Evaluation. Stroudsburg, USA: ACL, 2024: 13524-13536. [4] CHENG X X, LI J Y, ZHAO W X, et al. ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting // Proc of the Joint International Conference on Computational Linguistics, Language Resources and Evaluation. Stroudsburg, USA: ACL, 2024: 2969-2983. [5] MU L, ZHANG W H, ZHANG Y W, et al. DDPrompt: Differential Diversity Prompting in Large Language Models // Proc of the 62nd Annual Meeting of the Association for Computational Linguistics. Stroudsburg, USA: ACL, 2024: 168-174. [6] YANG J, WANG X, ZHAO Y, et al. RAG-Based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts. IEEE Transactions on Computational Social Systems, 2024. DOI: 10.1109/TCSS.2024.3501316. [7] EDGE D, TRINH H, CHENG N, et al. From Local to Global: A Graph RAG Approach to Query-Focused Summarization[C/OL].[2025-04-15]. https://arxiv.org/abs/2404.16130. [8] SHAHRIAR S, LUND B D, MANNURU N R, et al. Putting GPT-4o to the Sword: A Comprehensive Evaluation of Language, Vision, Speech, and Multimodal Proficiency. Applied Sciences, 2024, 14(17). DOI: 10.3390/app14177782. [9] ABANE A, BEKRI A, BATTOU A. FastRAG: Retrieval Augmented Generation for Semi-Structured Data[C/OL]. [2025-04-15]. https://arxiv.org/abs/2411.13773. [10] LIU J, WANG Y, LIN Z Q, et al. Natural Language Fine-Tuning[C/OL]. [2025-04-15]. https://arxiv.org/abs/2412.20382. [11] NIKDAN M, TABESH S, CRNČEVIĆ E, et al. RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation. Procee-dings of the Machine Learning Research, 2024, 235: 38187-38206. [12] ZHANG Y H, LI P Z, HONG J Y, et al. Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark. Proceedings of the Machine Learning Research, 2024, 235: 59173-59190. [13] YU Y, PING W, LIU Z H, et al. RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs // Proc of the 38th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2024: 121156-121184. [14] CHIEN C W, TAI Y M.Performances of Large Language Models in Detecting Psychiatric Diagnoses from Chinese Electronic Medical Records: Comparisons between GPT-3.5, GPT-4, and GPT-4o. Taiwanese Journal of Psychiatry, 2024, 38(3): 134-141. [15] LIU Y X, ZHANG K, LI Y, et al. Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models[C/OL]. [2025-04-15]. https://doi.org/10.48550/arXiv.2402.17177. [16] SONODA Y, KUROKAWA R, NAKAMURA Y, et al. Diagnostic Performances of GPT-4o, Claude 3 Opus, and Gemini 1.5 pro in “Diagnosis Please” Cases. Japanese Journal of Radiology, 2024, 42(11): 1231-1235. [17] DEROY A, MAITY S.Code Generation and Algorithmic Problem Solving Using Llama 3.1 405B[C/OL].[2025-04-15]. https://arxiv.org/abs/2409.19027. [18] YING Z H, LIU A S, LIANG S Y, et al. SafeBench: A Safety Eva-luation Framework for Multimodal Large Language Models[C/OL].[2025-04-15]. https://arxiv.org/abs/2410.18927. [19] HU H C, SHANG Y, XU G L, et al. Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugs// Proc of the IEEE/ACM International Workshop on Automated Program Repair. Washington, USA: IEEE, 2025: 11-18. [20] QUAN S H R, YANG J X, YU B W, et al. CODEELO: Benchmarking Competition-Level Code Generation of LLMs with Human-Comparable Elo Ratings[C/OL].[2025-04-15]. https://arxiv.org/abs/2501.01257. [21] DeepSeek-AI. DeepSeek-V3 Technical Report[C/OL].[2025-04-15]. https://arxiv.org/abs/2412.19437. [22] 刘冀辰,王磊,黄一鸣,等.一种业务办理状态提示方法、装置、存储介质及电子设备:中国, 202410179428.0[2024-02-18]. (LIU Y C, WANG L, HUANG Y M, et al. A Business Processing Status Prompt Method, Device, Storage Medium, and Electronic Device: China, 202410179428.0[2024-02-18].) [23] CUI J X, NING M N, LI Z J, et al. Chatlaw: A Multi-agent Co-llaborative Legal Assistant with Knowledge Graph Enhanced Mixture-of-Experts Large Language Model[C/OL].[2025-04-15]. https://arxiv.org/abs/2306.16092. [24] LIANG Y W, HE J F, LI G, et al. Rich Human Feedback for Text-to-Image Generation // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2024: 19401-19411. [25] 田波,张越,蒙飞,等.电网故障处置信息自适应理解框架及关键技术.中国电力, 2024, 57(7): 188-195. (TIAN B, ZHANG Y, MENG F, et al. Adaptive Understanding Framework and Key Technology of Power Grid Fault Disposal Information. Electric Power, 2024, 57(7): 188-195.) [26] ZHU B, LIN B, NING M N, et al. LanguageBind: Extending Vi-deo-Language Pretraining to N-Modality by Language-Based Semantic Alignment[C/OL].[2025-04-15]. https://arxiv.org/pdf/2310.01852. [27] KONG L D, XU X, REN J W, et al. Multi-modal Data-Efficient 3D Scene Understanding for Autonomous Driving. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025, 47(5): 3748-3765. [28] YANG J K, CEN J, PENG W X, et al. 4D Panoptic Scene Graph Generation // Proc of the 37th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2023: 69692-69705. [29] JIANG B, CHEN X, LIU W, et al. MotionGPT: Human Motion as a Foreign Language // Proc of the 37th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2023: 20067-20079. [30] CHEN L H, LU S L, ZENG A L, et al. MotionLLM: Understan-ding Human Behaviors from Human Motions and Videos[C/OL].[2025-04-15]. https://arxiv.org/abs/2405.20340. [31] VAN DER HEIJDEN B, LUIJKX J, FERRANTI L, et al. Engine Agnostic Graph Environments for Robotics(EAGERx): A Graph-Based Framework for Sim2real Robot Learning. IEEE Robotics & Automation Magazine, 2024, 32(2): 99-112. [32] PATEL B, DORBALA V S, MANOCHA D, et al. Embodied Ques-tion Answering Via Multi-LLM Systems[C/OL].[2025-04-15]. https://arxiv.org/pdf/2406.10918v2. [33] ZHOU G Z, HONG Y C, WU Q.NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models. Proceedings of the AAAI Conference on Artificial Intelligence, 2024, 38(7): 7641-7649. [34] POKHARIYA C, SHAH I N, XING A, et al. MANUS: Markerless Grasp Capture Using Articulated 3D Gaussians // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2024: 2197-2208. [35] WANG T, MAO X H, ZHU C M, et al. EmbodiedScan: A Holistic Multi-modal 3D Perception Suite Towards Embodied AI// Proc of the IEEE/CVF Conference on Computer Vision and Pattern Re-cognition. Washington, USA: IEEE, 2024: 19757-19767. [36] WU Y C, ZHANG P C, GU M Y, et al. Embodied Navigation with Multi-modal Information: A Survey from Tasks to Methodology. Information Fusion, 2024, 112. DOI: 10.1016/j.inffus.2024.102532. [37] HUANG J Y, YONG S L, MA X J, et al. An Embodied Generalist Agent in 3D World[C/OL].[2025-04-15]. https://arxiv.org/pdf/2311.12871. [38] 阙天舒,郑圣贤.空间智能安全的场态转换与治理反思——基于“感知-计算-行动”的国家安全风险探赜.国家安全论坛, 2024, 3(6): 37-53, 95-96. (QUE T S, ZHENG S X. Reflection on the Field State Transformation and Governance of Spacial Intelligence and Security-Exploration of National Security Risks Based on "Perception-Computation-Action". National Security Forum, 2024, 3(6): 37-53, 95-96.) [39] 张旭,王波.空间计算技术:基于三维视觉的感知交互计算新范式.中国工业和信息化, 2024,(12): 6-12. (ZHANG X, WANG B.Spatial Computing Technology: A New Paradigm of Perceptual Interactive Computing Based on 3D Vision. China Industry and Information Technology, 2024(12): 6-12.) [40] MASALKHI M, WAISBERG E, ONG J, et al. Apple Vision Pro for Ophthalmology and Medicine. Annals of Biomedical Enginee-ring, 2023, 51(12): 2643-2646. [41] GODDEN E, STEEDMAN W, PAN M K X J. Robotic Characteriza-tion of Markerless Hand-Tracking on Meta Quest Pro and Quest 3 Virtual Reality Headsets. IEEE Transactions on Visualization and Computer Graphics, 2025, 31(5): 3025-3034. [42] 杨雨彤. AI大模型与具身智能终将相遇.机器人产业, 2024,(2): 71-74. (YANG Y T.AI Big Models and Embodied Intelligence will Eventually Meet. Robot Industry, 2024(2): 71-74.) [43] 王文晟,谭宁,黄凯,等.基于大模型的具身智能系统综述.自动化学报, 2025, 51(1): 1-19. (WANG W S, TAN N, HUANG K, et al. Embodied Intelligence Systems Based on Large Models: A Survey. Acta Automatica Sinica, 2025, 51(1): 1-19.) [44] NAGY M, LǍZǍROIU G, VALASKOVA K. Machine Intelligence and Autonomous Robotic Technologies in the Corporate Context of SMEs: Deep Learning and Virtual Simulation Algorithms, Cyber-Physical Production Networks, and Industry 4.0-Based Manufacturing Systems. Applied Sciences. 2023, 13(3). DOI: 10.3390/app13031681. [45] ZHAO G S, WANG X F, ZHU Z, et al. DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 2025, 39(10): 10412-10420. [46] OpenAI. Sora: Creating Video from Text[EB/OL].[2025-04-15]. https://openai.com/sora. [47] DING J T, ZHANG Y K, SHANG Y, et al. Understanding World or Predicting Future? A Comprehensive Survey of World Models. ACM Computing Surveys, 2025. DOI: 10.1145/3746449. [48] CHEN H X, XIA M H, HE Y Q, et al. VideoCrafter1: Open Di-ffusion Models for High-Quality Video Generation[C/OL].[2025-04-15]. https://arxiv.org/pdf/2310.19512. [49] CHEN H X, ZHANG Y, CUN X D, et al. VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2024: 7310-7320. [50] ESLAMI S M A, JIMENEZ REZENDE D, BESSE F, et al. Neural Scene Representation and Rendering. Science, 2018, 360(6394): 1204-1210. [51] CLARK A, DONAHUE J, SIMONYAN K.Adversarial Video Gene-ration on Complex Datasets[C/OL]. [2025-04-15].https://arxiv.org/pdf/1907.06571. [52] HO J, CHAN W, SAHARIA C, et al. Imagen Video: High Definition Video Generation with Diffusion Models[C/OL].[2025-04-15]. https://arxiv.org/pdf/2210.02303. [53] VILLEGAS R, BABAEIZADEH M, KINDERMANS P J, et al. Phenaki: Variable Length Video Generation from Open Domain Textual Descriptions[C/OL].[2025-04-15]. https://arxiv.org/pdf/2210.02399. [54] KONDRATYUK D, YU L J, GU X Y, et al. VideoPoet: A Large Language Model for Zero-Shot Video Generation[C/OL].[2025-04-15]. https://arxiv.org/pdf/2312.14125. [55] GAO S Y, YANG J, CHEN L, et al. Vista: A Generalizable Dri-ving World Model with High Fidelity and Versatile Controllability // Proc of the 38th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2024: 91560-91596. [56] SHANG Y, LIN Y M, ZHENG Y, et al. UrbanWorld: An Urban World Model for 3D City Generation[C/OL].[2025-04-15]. https://arxiv.org/pdf/2407.11965. [57] WANG X, ZHANG X, LUO Z, et al. Emu3: Next-Token Prediction Is All You Need[C/OL].[2025-04-15]. https://arxiv.org/pdf/2409.18869. [58] PODELL D, ENGLISH Z, LACEY K, et al. SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis[C/OL].[2025-04-15]. https://arxiv.org/pdf/2307.01952. [59] LIU H, LI C, LI Y, et al. LLaVA-NeXT: Improved Reasoning, OCR, and World Knowledge[EB/OL].[2025-04-15]. https://github.com/haotian-liu/LLaVA-NeXT. [60] ZHENG Z W, PENG X Y, YANG T J, et al. Open-Sora: Democratizing Efficient Video Production for All[C/OL].[2025-04-15]. https://arxiv.org/pdf/2412.20404. [61] GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Gene-rative Adversarial Networks. Communications of the ACM, 2020, 63(11). DOI: 10.1145/3422622. [62] LIANG S S, PAN Z, LIU W, et al. A Survey on Variational Autoencoders in Recommender Systems. ACM Computing Surveys, 2024, 56(10). DOI: 10.1145/3663364. [63] YU H X, DUAN H Y, HERRMANN C, et al. WonderWorld: Interactive 3D Scene Generation from a Single Image // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2025: 5916-5926. [64] CHEN Z X, TANG J X, DONG Y H, et al. 3DTopia-XL: Scaling High-Quality 3D Asset Generation via Primitive Diffusion // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Re-cognition. Washington, USA: IEEE, 2025: 26576-26586. [65] CHEN Y D, ZHENG C X, XU H F, et al. MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views // Proc of the 38th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2024: 107064-107086. [66] GAO Y F, OU J, WANG L, et al. Bootstrap 3D Reconstructed Scenes from 3D Gaussian Splatting[C/OL].[2025-04-15]. https://arxiv.org/pdf/2404.18669v1. [67] SZYMANKOWICZ S, INSAFUTDINOV E, ZHENG C X, et al. Flash3D: Feed-Forward Generalisable 3D Scene Reconstruction from a Single Image[C/OL].[2025-04-15]. https://arxiv.org/pdf/2406.04343. [68] LI X Y, ZHANG Q, KANG D, et al. Advances in 3D Generation: A Survey[C/OL].[2025-04-15]. https://arxiv.org/pdf/2401.17807. [69] ABRAMSON J, ADLER J, DUNGER J, et al. Accurate Structure Prediction of Biomolecular Interactions with AlphaFold 3. Nature, 2024, 630: 493-500. [70] CUI H T, WANG C, MAAN H, et al. scGPT: Toward Building a Foundation Model for Single-Cell Multi-omics Using Generative AI. Nature Methods, 2024, 21: 1470-1480. [71] WIESNER D, SUK J, DUMMER S, et al. Generative Modeling of Living Cells with SO(3)-Equivariant Implicit Neural Representations. Medical Image Analysis, 2024, 91. DOI: 10.1016/j.media.2024.102991. [72] HUANG Z Y, WENG X S, IGL M, et al. Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-Tuning[C/OL].[2025-04-15]. https://arxiv.org/pdf/2410.05582. [73] WANG L N, REN Y L, JIANG H, et al. AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model[C/OL].[2025-04-15]. https://arxiv.org/pdf/2312.13156. [74] ZHANG J F, JIANG Z H, YANG D D, et al. AvatarGen: A 3D Generative Model for Animatable Human Avatars // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2022: 668-685. [75] XIA H C, SUN S, WANG X R, et al. Market-GAN: Adding Control to Financial Market Data Generation with Semantic Context. Proceedings of the AAAI Conference on Artificial Intelligence, 2024, 38(14): 15996-16004. [76] ZHENG B Y, GOU B Y, KIL J, et al. GPT-4V(ision) Is a Ge-neralist Web Agent, If Grounded[C/OL].[2025-04-15]. https://arxiv.org/pdf/2401.01614. [77] ZHOU R L, DU S, LI B H.Reflect-RL: Two-Player Online RL Fine-Tuning for LMs // Proc of the 62nd Annual Meeting of the Association for Computational Linguistics(Long Papers). Stroudsburg, USA: ACL, 2024: 995-1015. [78] SHEN Y L, SONG K T, TAN X, et al. HuggingGPT: Solving AI Tasks with ChatGPT and Its Friends in Hugging Face // Proc of the 38th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2024: 38154-38180. [79] GRUDIN J.ChatGPT and Chat History: Challenges for the New Wave. Computer, 2023, 56(5): 94-100. [80] ZUNIN V V.Intel OpenVINO Toolkit for Computer Vision: Object Detection and Semantic Segmentation // Proc of the International Russian Automation Conference. Washington, USA: IEEE, 2021: 847-851. [81] ZHOU Y Y, SONG L, WANG B N, et al. MetaGPT: Merging Large Language Models Using Model Exclusive Task Arithmetic // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2024: 1711-1724. [82] TAO W, ZHOU Y C, WANG Y L, et al. MAGIS: LLM-Based Multi-agent Framework for GitHub Issue Resolution // Proc of the 38th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2024: 51963-51993. [83] QIAO B, LI L Q, ZHANG X, et al. TaskWeaver: A Code-First Agent Framework[C/OL].[2025-04-15]. https://arxiv.org/pdf/2311.17541. [84] LI J K, LAI Y, LI W T, et al. Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents[C/OL].[2025-04-15]. https://arxiv.org/pdf/2405.02957. [85] 詹伟华,张昕妍.引领人工智能在教育领域规范、有序、高效应用——《北京市教育领域人工智能应用指南(2024年)》解读.中小学信息技术教育, 2024(12): 19-21. (ZHAN W H, ZHANG X Y. Leading the Standardized, Orderly, and Efficient Application of Artificial Intelligence in Education: Interpretation of the "Beijing Education Sector Artificial Intelligence Application Guidelines(2024)." Primary and Secondary School Information Technology Education, 2024(12): 19-21.) [86] WANG J C, TANG Y, HARE R, et al. Parallel Intelligent Education with ChatGPT. Frontiers of Information Technology & Electronic Engineering, 2024, 25(1): 12-18. [87] 蔡丹丹,王诗卉.智能体技术进展及其在图书馆领域的应用.信息与管理研究, 2024, 9(6): 24-33. (CAI D D, WANG S H. The Progress of AI Agent Technology and Its Application in the Libraries. Journal of Information and Ma-nagement, 2024, 9(6): 24-33.) [88] CHRISTIANOS F, PAPOUDAKIS G, COSTE T, et al. Lightweight Neural App Control[C/OL].[2025-04-15]. https://arxiv.org/pdf/2410.17883. [89] MIAO Q H, WANG F Y.Artificial Intelligence for Science(AI4S): Frontiers and Perspectives Based on Parallel Intelligence. Berlin, Germany: Springer, 2024. [90] ABRIATA L A.The Nobel Prize in Chemistry: Past, Present, and Future of AI in Biology. Communications Biology, 2024, 7(1). DOI:10.1038/s42003-024-07113-5. [91] 王飞跃,王雨桐.数字科学家与平行科学:AI4S和S4AI的本源与目标.中国科学院院刊, 2024, 39(1): 27-33. (WANG F Y, WANG Y T.Digital Scientists and Parallel Sciences: The Origin and Goal of AI for Science and Science for AI. Bulletin of the Chinese Academy of Sciences, 2024, 39(1): 27-33.) [92] DURANT G, BOYLES F, BIRCHALL K, et al. The Future of Machine Learning for Small-Molecule Drug Discovery Will Be Driven by Data. Nature Computational Science, 2024, 4: 735-743. [93] FENG B, LIU Z Q, HUANG N L, et al. A Bioactivity Foundation Model Using Pairwise Meta-Learning. Nature Machine Intelligence, 2024, 6: 962-974. [94] WU Z X, ZHANG O D, WANG X K, et al. Leveraging Language Model for Advanced Multiproperty Molecular Optimization via Prompt Engineering. Nature Machine Intelligence, 2024, 6: 1359-1369. [95] WANG J Q, LIU J P, WANG H S, et al. A Comprehensive Transformer-Based Approach for High-Accuracy Gas Adsorption Predictions in Metal-Organic Frameworks. Nature Communications, 2024, 15(1). DOI: 10.1038/s41467-024-46276-x. [96] PARK H, YAN X L, ZHU R J, et al. A Generative Artificial Intelligence Framework Based on a Molecular Diffusion Model for the Design of Metal-Organic Frameworks for Carbon Capture. Communications Chemistry, 2024, 7(1). DOI: 10.1038/s42004-023-01090-2. [97] WANG Y X, LI Y, TANG Z C, et al. Universal Materials Model of Deep-Learning Density Functional Theory Hamiltonian. Science Bulletin, 2024, 69(16): 2514-2521. [98] HUNG N T, OKABE R, CHOTRATTANAPITUK A, et al. Universal Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures. Advanced Materials, 2024, 36(46). DOI: 10.1002/adma.202409175. [99] LI Z, ZHAO C Q, WANG H K, et al. Interpreting Chemisorption Strength with AutoML-Based Feature Deletion Experiments. PNAS, 2024, 121(12). DOI: 10.1073/pnas.2320232121. [100] SRIRAM A, MILLER B K, CHEN R T Q, et al. FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions // Proc of the 38th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2024: 46025-46046. [101] MARTÍNEZ-PEÑAS U, PUCHINGER S. Maximum Sum-Rank Dis-tance Codes over Finite Chain Rings. IEEE Transactions on Information Theory, 2024, 70(6): 3878-3890. [102] XU Z H, ZHOU T K, MA M Z, et al. Large-Scale Photonic Chiplet Taichi Empowers 160-TOPS/W Artificial General Intelligence. Science, 2024, 384(6692): 202-209. [103] YANG Z Y, WANG T Y, LIN Y H, et al. A Vision Chip with Complementary Pathways for Open-World Sensing. Nature, 2024, 629(8014): 1027-1033. [104] 张伯伟,马凡慧.智能制造如何提升产业链供应链韧性:理论机制与实证检验.经济学动态, 2024,(11): 20-37. (ZHANG B W, MA F H.How Intelligent Manufacturing Enhances Industrial Chain and Supply Chain Resilience: Theoretical Mechanisms and Empirical Tests. Economic Perspectives, 2024(11): 20-37.) [105] 李锋,叶宜涛,程亮.生成式人工智能在数字教材建设中的现实问题、改进方法与实践策略.中国电化教育, 2024,(12): 23-30. (LI F, YE Y T, CHENG L.Leveraging Artificial Intelligence Generated Content for Digital Textbooks: Challenges, Methods, and Implementation Strategies. China Educational Technology, 2024(12): 23-30.) [106] 倪宝琛儿,徐健涵,陈锦雯,等.智能网联汽车车载服务生态的架构规划与发展建议.汽车实用技术, 2024, 49(19): 40-45. (NI B C E, XU J H, CHEN J W, et al. Intelligent Connected Vehicle In-vehicle Service Ecology Framework Planning and Development Proposals. Automobile Applied Technology, 2024, 49(19): 40-45.) [107] 李红梅.谱写气象强国建设新篇章.人民日报, 2024-12-26(001). (LI H M. Writing a New Chapter in the Construction of a Meteo-rological Power. People's Daily Online, 2024-12-26(001).) [108] 高巍,高静,杨哲.面向人工智能的数据通信网络发展.中兴通讯技术, 2024, 30(6): 3-9. (GAO W, GAO J, YANG Z.Data Communication Network Development for Artificial Intelligence. ZTE Technology Journal, 2024, 30(6): 3-9.)